Office Action 2: Final Rejection (2023-12-04)
Rejection types: 102 - Lack of Novelty

CLAIMS:
AMENDMENTS TO THE CLAIMS

This listing of claims will replace all prior versions of claims in the application:

Listing of Claims:

1\. (Currently Amended) A method, comprising:training by a system comprising a processor, a first deep neural network (DNN) model for image classification of a group ofcategories ofimages, wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion;
extracting, by the system, the feature extraction layer portion from the first DNN model:
incorporating, by the system, the feature extraction layerportion as an executable module in a storage device ofthe system:
extracting, by the system, using the feature extraction layer portion in the storage device, respective features of images stored in the storage device
storing, by the system, in the storage device, the respective features ofthe images:
retrieving, by the system, from the storage device, the respective features oftraining images in the images, wherein the training images are associated with a sub-group ofthe group ofcategories ofimages:
training, by the system,\[\[an\]\] a second output layer portion for image classification ofthe sub-group ofthe group ofcategories ofimages, based on the respective features of the training images and according to classification labels of the training images; and
generating, by the system, a second DNN model for image classification ofthe sub-group ofthe group of categories ofimages by combining the feature extraction layer portion and the second output layer portion.
2\. (Currently Amended) The method according to claim 1, wherein the first DNN model is trained using a subset ofthe images

3\. (Currently Amended) The method according to claim 1, further comprising:
receiving, by the system, a new image; and
classifying, by the system, using the second DNN model, the new image .
4\. (Currently Amended) The method according to claim \[\[3\]\] SVG {EMBED HERE} 0.140.117 further comprising:
 receiving, by the system, additional training images; and
extracting, by the system, using the feature extraction layer portion, respective features of the additional training images,
wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images by .

5\. (Currently Amended) The method according to claim 1, further comprising: adjusting, by the system, the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images.

6\. (Currently Amended) The method according to claim 5, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises:training the second DNN model by using original data of the training images and the classification labels.

7\. (Currently Amended) A system, comprising:at least one storage device;
at least one processing unit; and
at least one memory that is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, the at least one processing unit comprising:
an image classification model generation unit, configured to train a first deep neural network (DNN) model for image classification of a group of categories of images,wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion; anda feature extraction unit configured SVG {EMBED HERE} 0.1230.173 extract the feature extraction layer portion from the first DNN model;
incorporate the feature extraction layer portion as an executable module in the at least one storage device of the system; and
extract, using the feature extraction layer portion in the at least one storage device, respective features of images stored in the at least one storage device;
storing, in the at least one storage device, the respective features of the images;and
\[\[an\]\] wherein image classification model generation unit\[\[,\]\] is further configured to:retrieve, from the storage device, the respective features of training images in the images, wherein the training images are associated with a sub-group of the group of categories of images;
train a\[\[n\]\] second output layer portion for image classification of the sub- group of the group of categories of images, based on the respective features of the training images and according to classification labels of the training images; and
generate a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion.

8\. (Currently Amended) The system according to claim 7, wherein the first DNN model is trained using a subset of the images .

9\. (Currently Amended) The system according to claim 7, wherein the image classification model generation unit is further configured to:receive a new image; and
classify, using the second DNN model, the new image .
10\. (Currently Amended) The system according to claim \[\[9\]\] 7, wherein the image classification model generation unit is further configured SVG {EMBED HERE} 0.1230.173 receive additional training images;
 extract, using the feature extraction layer portion, \[\[the\]\] respective features of the additional training images; and
train the second output layer portion based further on the respective features of the additional training images .

11\. (Currently Amended) The system according to claim 7, wherein the image classification model generation unit is further configured to further adjust the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images.

12\. (Currently Amended) The system according to claim 11, wherein the image classification model generation unit is further configured to train the second DNN model by using original data of the training images and the classification labels.

13\. (Currently Amended) The system according to claim 11, wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the second DNN model by using at least one graphics processing unit.

14\. (Currently Amended) A non-transitory computer-readable medium, comprising machine- executable instructions that, in response to execution by a system comprisinga processor, cause the device to perform operations, comprising:training a first deep neural network (DNN) model for image classification of a group of categories of images, wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion;
extracting the feature extraction layer portion from the first DNN model;
incorporating the feature extraction layer portion as an executable module in a storage device of the system;
extracting, using the feature extraction layer portion in the storage device, a group of features of respective features of images stored in the storage device by ;
storing, in the storage device, the respective features of the images;
retrieving, from the storage device, the respective features of training images in the images, wherein the training images are associated with a sub-group of the group of categories of images;
training \[\[an\]\] a second output layer portion for image classification of the sub-group of the group of categories of images, based on the respective features of the training images and according to classification labels of the training images; and
generating a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion.

15\. (Currently Amended) The non-transitory computer-readable medium according to claim 14, wherein the firstDNN model is training using a subset of the images .

16\. (Currently Amended) The non-transitory computer-readable medium according to claim 14, wherein the operations further comprise:receiving a new image; and
classifying, using the second DNN model, the new image .

17\. (Currently Amended) The non-transitory computer-readable medium according to claim 14\[\[6\]\], wherein the operations further comprise: receiving additional training images;
extracting, using the feature extraction layer portion, respective features of the additional training images; and
wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images by .

18\. (Currently Amended) The non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: adjusting the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images.

19\. (Currently Amended) The non-transitory computer-readable medium according to claim 18, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises:training the second DNN model by using original data of the training images and the classification labels.

20\. (Currently Amended) The non-transitory computer-readable medium according to claim 19, wherein the adjusting the feature extraction layer portion and the output layer portion of the second DNN model \[\[by\]\] comprises using at least one graphics processing unit.

OFFICE 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 .
Response to Amendments
The amendments were filed on 11/01/2023.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see REMARKS, filed 11/01/2023, with respect to the rejections of claims 1-20 under 35 USC 102 and 35 USC 103 have been fully considered and are persuasive.  Therefore, the rejections have been withdrawn.  However, upon further consideration, new grounds of rejection are made in view of US 10,360,499 KUMAR et al. See the rejection below.
Claim Rejections – 35 USC § 102
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 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.
(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 1-20 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 10,360,499  to  KUMAR et al. (hereinafter “KUMAR”).
As per claim 1, KUMAR discloses a method, comprising: training, by a system comprising a processor (a method for training an image classifier system comprising a computing processor; column 6, lines 9-60), a first deep neural network (DNN) model for image classification of a group of categories of images (the system comprises a first deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37); extracting, by the system, the feature extraction layer portion from the first DNN model: incorporating, by the system (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42), the feature extraction layer portion as an executable module in a storage device of the system: extracting, by the system, using the feature extraction layer portion in the storage device, respective features of images stored in the storage device (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); storing, by the system, in the storage device (storing feature via an on board storage memory device; column 60, lines 13-34), the respective features of the images: retrieving, by the system, from the storage device, the respective features of training images in the images (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of  the group of categories of images: training, by the system, a second output layer portion for image classification of the sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network; column 45, lines 12-67); and generating, by the system, a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 2, KUMAR discloses the method according to claim 1, wherein the first DNN model is trained using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 3, KUMAR discloses the method according to claim 1, further comprising: receiving, by the system, a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classifying, by the system, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 4, KUMAR discloses the method according to claim 3 further comprising: receiving, by the system, additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); and extracting, by the system, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24), wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 5, KUMAR discloses the method according to claim 1, further comprising: adjusting, by the system, the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 6, KUMAR discloses the method according to claim 5, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises: training the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 7, KUMAR discloses a system, comprising: at least one storage device (a system for classifying images comprising a memory storage device within the computing system; column 6, lines 9-60); at least one processing unit (the computing system further includes a computing processor and accompanying hardware; column 6, lines 9-60); and at least one memory that is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit (wherein said storage memory is in connection with said processor within the computing system and the memory unit stores a plurality of computer implemented systems to be executed by the processing unit; column 6, lines 9-60), the at least one processing unit comprising: an image classification model generation unit (wherein the processing unit is adapted to perform a computer program contains instructions when executed by the processor create (generate) an artificial neural network(s) which may be trained for tasks; column 6, lines 9-60), configured to train a first deep neural network (DNN) model for image classification of a group of categories of images (once the processor executes the instructions to create the network the system comprises a first trainable deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37); and a feature extraction unit, configured extract the feature extraction layer portion from the first DNN model (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42); incorporate the feature extraction layer portion as an executable module in the at least one storage device of the system (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); and extract, using the feature extraction layer portion in the at least one storage device (storing feature via an on board storage memory device; column 6, lines 9-60; column 60, lines 13-34), respective features of images stored in the at least one storage device (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11); storing, in the at least one storage device, the respective features of the images (storing feature via an on board storage memory device; column 6, lines 9-60; column 60, lines 13-34); and  wherein image classification model generation unit is further configured to: retrieve, from the storage device, the respective features of training images in the images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of the group of categories of images (input training data samples are associated with various yield results, sub-samples (subgroups), and key events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11); train a second output layer portion for image classification of the sub- group of the group of categories of images, based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect and classify cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network, where the classification labels would be cancerous or ; column 45, lines 12-67); and generate a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 8, KUMAR discloses the system according to claim 7, wherein the first DNN model is trained using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 9, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured to: receive a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classify, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 10, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured receive additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); extract, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24); and train the second output layer portion based further on the respective features of the additional training images (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 11, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured to further adjust the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 12, KUMAR discloses the system according to claim 11, wherein the image classification model generation unit is further configured to train the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 13, KUMAR discloses the system according to claim 11, wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the second DNN model by using at least one graphics processing unit (the image classification system utilizing two neural network models to determine a result after training wherein the output layer may have its ratios adjusted and the system comprises a GPU to change those image settings; column 45, line 46 – column 46, line 12; column 52, lines 15-41).
As per claim 14, KUMAR discloses a non-transitory computer-readable medium, comprising machine- executable instructions that, in response to execution by a system comprising a processor, cause the device to perform operations (non-transitory computer-readable memory storage, comprising processor executable instructions that a method for training an image classifier system comprising a computing processor; column 6, lines 9-60), comprising: training a first deep neural network (DNN) model for image classification of a group of categories of images (the system comprises a first deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37); extracting the feature extraction layer portion from the first DNN model (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42); incorporating the feature extraction layer portion as an executable module in a storage device of the system (storing feature via an on board storage memory device; column 60, lines 13-34); extracting, using the feature extraction layer portion in the storage device, a group of features of respective features of images stored in the storage device by (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); storing, in the storage device, the respective features of the images (storing feature via an on board storage memory device; column 60, lines 13-34); retrieving, from the storage device, the respective features of training images in the images (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11); training a second output layer portion for image classification of the sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network; column 45, lines 12-67); and generating a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 15, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the first DNN model is training using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 16, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: receiving a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classifying, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 17, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: receiving additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); extracting, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24); and wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images by (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 18, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: adjusting the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 19, KUMAR discloses the non-transitory computer-readable medium according to claim 18, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises: training the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 20, KUMAR discloses the non-transitory computer-readable medium according to claim 19, wherein the adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises using at least one graphics processing unit (the image classification system utilizing two neural network models to determine a result after training wherein the output layer may have its ratios adjusted and the system comprises a GPU to change those image settings; column 45, line 46 – column 46, line 12; column 52, lines 15-41).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

APPLICANT REBUTTAL: REMARKS
Claims 1-20 are currently pending in the subject application and are presently under
consideration. Claims 1-20 have been amended as indicated at pages 2-7 of the Reply. No new matter has been introduced by the herein amendments.
Assignee's representative thanks Examiner Dhooge for courtesies extended during the telephonic interview conducted on October 25, 2023. Examiner was contacted to discuss the claim rejections under 35 U.S.C. § 102 and 35 U.S.C. § 103. During the interview, assignee's representative provided clarification regarding distinctions of the claims over the cited references. Examiner indicated that further search and consideration was required to determine if the claims would be allowed.
Favorable reconsideration of the subject patent application is respectfully requested in view of the comments and amendments herein.
I.
Rejection of Claims 1-4
,
,
and 14-18 under 35 U.S.C.
§
102(a)(1)
Claims 1-4, 7-11, and 14-18 stand rejected under 35 U.S.C. § 102(a)(1) as allegedly
being anticipated by Guo et al. (US Patent No. 10,262,240 B2). It is respectfully submitted that this rejection should be withdrawn for at least the following reasons. Guo et al. fails to disclose each and every element of the subject claims.
For a prior art reference to anticipate, 35 U.S.C. § 102 requires that "each and every element as set forth in the claim is found, either expressly or inherently described, in a single prior art reference."
In re Robertson, 169 F.3d 743, 745, 49 USPQ2d 1949, 1950 (Fed.
Cir. 1999) (quoting Verdegaal Bros., Inc. v. Union Oil Co., 814 F.2d 628, 631, 2USPQ2d 1051, 1053 (Fed. Cir. 1987)). "The identical invention must be shown in as complete detail as is contained in the...claim." Richardson v. Suzuki Motor Co., 868
F.2d 1226, 9 USPQ2d 1913, 1920 (Fed. Cir. 1989).
Independent claim 1 (and similarly independent claims 7 and 14) recites, in part: training, by a system comprising a processor, a first deep neural network (DNN) model for image classification of a group of categories of images, wherein the first DNN model comprises a
feature extraction layer portion and a first output layer portion;
extracting, by the system, the feature extraction layer portion from the first DNN model;
incorporating, by the system, the feature extraction layer portion as an executable module in a storage device of the
system;
extracting, by the system, using the feature extraction
layer portion in the storage device, respective features of
images stored in the storage device;
storing, by the system, in the storage device, the respective features of the images;
retrieving, by the system, from the storage device, the respective features of training images in the images, wherein the training images are associated with a sub-group of the
group of categories of images;
training, by the system, a second output layer portion for image classification of the sub-group of the group of categories of images, based on the respective features of the training images and according to classification labels of the
training images; and
generating, by the system, a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second
output layer portion
(emphasis added)
Guo et al. does not disclose all elements as recited in the subject claim. Column 2, lines 16-60, column 6, lines 38-37, column 8, lines 38-63, and column 9, line 36 to column 10, line 11 of Guo et al. state:
(column 2, lines 16-60)
A method used for initial condition construction of a DNN includes leveraging  an  existing, already-trained  neural network with similar structure, which has been trained for general purpose tasks and
then fine tune the new neural network to the particular desired task.
In some previous implementations, at the initial stage, the lower-level layers of the model to be trained are typically set to have the same parameters as the pre-trained model, while the parameters of the last layer are initialized to be random numbers sampled from certain distributions (usually Gaussian). This is due mainly to the lower-level layers being more general, while the last (e.g., top) layer is focused on a specific task.
For example, for tasks including Flickr-style estimation (e.g., utilizes the Flicker data set), flower recognition, and places recognition, data scientists have trained the network by fine tuning
an existing neural network trained for general image recognition.
Embodiments presented herein show how to train classifiers quickly while maintaining or improving the accuracy of the classifiers, such as by training the neural network in a supervised learning scenario. As used herein, the initial network to be trained is called the predecessor network, and the second neural network, trained based on the predecessor network, is called the successor network. The predecessor network is modified by eliminating the use of bias in the classifier, and the
classifier of the successor network is initialized during the training phase based on the parameters learned from the predecessor network. The results are neural networks that train
much faster while maintaining accuracy.
In one example, a method includes training a predecessor network defined for image recognition of items, the training of the predecessor network further comprising initializing parameters of a predecessor classifier with random numbers sampled from a predetermined distribution, and utilizing, by the predecessor classifier, an image-classification probability function without bias.
The method further includes an operation for training a successor network defined for image recognition of items in a plurality of classes, the training of the successor network    further comprising initializing parameters of a successor classifier with parameters learned from the predecessor network, and utilizing, by the successor classifier, the image-classification probability function without bias. The method further includes operations for receiving an image for recognition, and for
recognizing the image utilizing the successor classifier
(column 6, lines 38-37)
At operation 402, a copy of the parameters of the feature extractor layers from a DNN configured for a general task is made, as discussed above. From operation 402, the method flows to operation 404 to set an initial condition for the classifier, where the initial condition includes random samples from the
predetermined distribution (e.g., Gaussian distribution).
From operation 404, the method flows to operation 406 to learn the classifier, e.g., train the classifier with the training data
based on the initial condition set at operation 404
(column 8, lines 38-63)
The method 600 includes operations 602-605 to train the first classifier (the predecessor network) and operations 612-615 to
train the second classifier (the successor network). At operation 602, the parameters of the feature extractor la
y
ers are copied
from a DNN
configured for performing general tests.
From operation 602, the method flows to operation 603 for selecting a cost function with zero bias, such as equation (3). From operation 603, the method flows to operation 604 where the initial condition for the classifier is set. As discussed earlier, the initial
condition is based on a random distribution.
From operation 604, the method flows to operation 605 were the first classifier is learned by performing the training operation
with the training data. From operation 605, the method flows to operation 612 when
training for the second classifier is started.
At operation 612
,
the parameters of the feature extractor
lay
ers are copied from the DNN
for the general task.
From operation 612, the method flows to operation 613 where the cost function with zero bias is selected, such as equation (3). From operation 613, the method flows to operation 614, where the initial condition is set based on the parameters of the first classifier. See for example equation (4). From operation 614, the method flows to operation 615 to learn the second classifier
(column 9, line 45 to column 10, line 11)
From operation 802, the method flows to operation 808 for training, by the one or more processors, a successor network defined for image recognition of items in a plurality of classes. The training of the successor network further comprises operations 810 and 812. Operation 810 is for initializing parameters of a successor classifier with  parameters learned   from   the predecessor network, and operation 812 is for utilizing, by the successor classifier, the image-classification probability function
without bias.
From operation 808, the method flows to operation 814 where an image is received for recognition. At operation 816, the one or more processors recognize the image utilizing the successor
classifier.
In one example, initializing parameters of the successor classifier further comprises initializing a weight vector wk for a class k based on a set of indexes for training samples which belong to the k
class.
In one example, initializing parameters of the successor classifier further comprises initializing the weight vector wk for the class k further based on an average direction of vectors for
the class k in the predecessor network.
In one example, vectors for a same class are located substantially
on a same hyper-sphere.
In one example, the predecessor network includes a plurality of feature-extraction layers and the predecessor classifier, where
the successor network includes the plurality of feature- extraction la
y
ers and the successor classifier.
In one example, the image-classification probability function for a sample image x in class k is based on a weight vector w and features extracted from the sample image x by the feature-
extraction layers.
In one example, the
feature-extraction la
y
ers of the successor network share same parameters as the feature-extraction
lay
ers of the predecessor network.
(emphasis added)
The above cited paragraphs disclose a process whereby a predecessor network is trained, and the feature-extraction layers parameters of the predecessor network are copied to the feature- extraction layers parameters of a successor network, which successor network is then trained. This allows the successor network to start training a point where the feature-extraction layers of the successor network have already been trained. However, the entire successor network is being trained in the process of Guo et al. In stark contrast, the subject claim trains only the second output layer portion of the second DNN model (i.e. referred to as image classification model in the instant specification) using stored features extracted from training images by a previously trained feature extraction layer portion from a first DNN model. Therefore, Guo et al. fails to disclose "training, by a system comprising a processor, a first deep neural network (DNN) model for image classification of a group of categories of images, wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion; extracting, by the system, the feature extraction layer portion from the first DNN model; incorporating, by the system, the feature extraction layer portion as an executable module in a storage device of the system; extracting, by the system, using the feature extraction layer portion in the storage device, respective features of images stored in the storage device; storing, by the system, in the storage device, the respective features of the images; retrieving, by the system, from the storage device, the respective features of training images in the images, wherein the training images are associated with a sub-group of the group of categories of images; training, by the system, a second output layer portion for image classification of the sub-group of the group of categories of images, based on the respective features of the training images and according to classification labels of the training images; and generating, by the system, a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion" as recited in claim 1, and similarly claims 7 and 14.
In view of the foregoing, Assignee's representative respectfully submits that Guo et al. fails to disclose or suggest all elements of independent claims 1, 7, and 14 (and claims 1-4, 8-11,
and 15-18 that depend therefrom), and thus fails to anticipate the subject claims. Accordingly, withdrawal of this rejection is respectfully requested.
II.
Rejection of Claims 5-6
,
,
and 19-20 under 35 U.S.C.
@
Claims 5-6, 12-13, and 19-20 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Guo et al. in view of Chen et al. (US Patent No. 10,742,399 B2). It is
respectfully submitted that this rejection should be withdrawn for at least the following reasons. Guo et al. and Chen et al., alone or in combination, do not disclose, teach or suggest each andevery element of the subject claims.
A factfinder should be aware, of course, of the distortion caused by hindsight bias and must be cautious of arguments reliant upon ex post reasoning. See KSR v. Teleflex, 550 U.S. 398, 127 S. Ct. 1727 (2007) citing Graham v. John Deere Co. of Kansas City, 383 U. S.
1, 36 (warning against a "temptation to read into the prior art the teachings of the invention in issue" and instructing courts to "'guard against slipping into the use of hindsight"' (quoting Monroe Auto Equipment Co. v. Heckethorn Mfg. & Supply Co.,
332 F. 2d 406, 412 (CA6 1964))).
Claims 5-6, 12-13, and 19-20 depend from independent claims 1, 7, or 14 respectively.
As noted supra, Guo et al. does not disclose or suggest each and every element as recited in these independent claims, and Chen et al. fails to make up for the aforementioned deficiencies of Guo et al. Chen et al., as summarized in the Abstract, is directed to:
In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the   uncompressed  image  is  associated  with  a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage
device.
Chen et al. fails to disclose, teach or suggest the elements of independent claims 1, 7, and 14 (alone or in combination with the other cited references). In view of at least the foregoing discussion, assignee's representative respectfully submits that Guo et al. and Chen et al., alone or in combination, do not disclose or suggest all elements as recited in independent claims 1, 7, and 14 (and claims 5-6, 12-13, and 19-20 that depend therefrom), and thus the rejection fails to establish a prima facie case of obviousness with respect to the subject claims. Accordingly, withdrawal of this rejection is respectfully requested.
CONCLUSION

NEXT OFFICE 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection.  Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on 3/4/2024/2024 has been entered.
Response to Amendment
Response to amendments filed on 3/4/2024.
Claims 1-20 are pending. Claims 1, 7, and 14 are amended.
Response to Arguments
Applicant’s arguments, see REMARKS on pages 8-12 filed on 02/02/2024, with respect to 35 USC 102 have been fully considered and are not persuasive.
On page 9 of REMARKS, regarding rejections made to claims 1-20 under 35 USC § 102(a)(1) the applicant states that:
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The Examiner respectfully disagrees. The applicant’s amendments include “and wherein the feature extraction layer portion only comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model; … images, wherein the training of the second output layer portion does not comprise training the feature extraction layer portion”.
The cited art of record US 10,360,499 B2 (KUMAR) discloses wherein the feature extraction layer portion only comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model. Kumar discloses that the features of the image are extracted via input layers 1, x1, x2, and xm which are adapted to extract features according to predetermined weights of desired values represented as w0, w1, w2, wm and fed into a net input function of the first artificial neural network; figure 5a; column 24, line 44 – column 25, line 55).
Wherein, it is clearly seen that output layer in figure 5a is separated from the hidden input layers and corresponding weights relating to the features to be extracted via the input layers by a net input function layer and an activation function layer as suggested in figure 5a. This separation of layers can additionally be seen in figures 5B, 6A, and 6B wherein the final output layers are separated from hidden input layers by at least one additional layer in all examples.
Further, Kumar discloses wherein the training of the second output layer portion does not comprise training the feature extraction layer portion. See, col. 24 line 44 to col. 25 line 55 where the output layer as seen in figure 5a is unrelated to the training of input layers 1, x1, x2, and xm to extract the features with weight relating to w0, w1, w2, wm. In view of this, it is clear that Kumar discloses the limitations recited in claim 1, for example. Please see the rejection below.
Claim Rejections - 35 USC § 102
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 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.
(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 1-20 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 10,360,499 B2 to A, KUMAR et al. (hereinafter “KUMAR”).
As per claim 1, KUMAR discloses a method, comprising: training, by a system comprising a processor (a method for training an image classifier system comprising a computing processor; column 6, lines 9-60), a first deep neural network (DNN) model for image classification of a group of categories of images (the system comprises a first deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37), and wherein the feature extraction layer portion only comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model (the features of the image are extracted via input layers 1, x1, x2, and xm which are adapted to extract features according to predetermined weights of desired values represented as w0, w1, w2, wm and fed into a net input function of the first artificial neural network; figure 5a; column 24, line 44 – column 25, line 55); extracting, by the system, the feature extraction layer portion from the first DNN model (and extracting via the net input function the extracted features of input layer relating to 1, x1, x2, and xm; figure 5a; column 24, line 44 – column 25, line 55); incorporating, by the system, the feature extraction layer portion as an executable module in a storage device of the system (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42); extracting, by the system, using the feature extraction layer portion in the storage device, respective features of images stored in the storage device (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); storing, by the system, in the storage device (storing feature via an on board storage memory device; column 60, lines 13-34), the respective features of the images: retrieving, by the system, from the storage device, the respective features of training images in the images (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of  the group of categories of images: training, by the system, a second output layer portion for image classification of the sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network; column 45, lines 12-67), and wherein the feature extraction layer portion only comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model (wherein the output layer as seen in figure 5a is unrelated to the training of input layers 1, x1, x2, and xm to extract features with weight relating to w0, w1, w2, wm; figure 5a; column 24, line 44 – column 25, line 55); and generating, by the system, a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 2, KUMAR discloses the method according to claim 1, wherein the first DNN model is trained using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 3, KUMAR discloses the method according to claim 1, further comprising: receiving, by the system, a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classifying, by the system, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 4, KUMAR discloses the method according to claim 3 further comprising: receiving, by the system, additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); and extracting, by the system, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24), wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 5, KUMAR discloses the method according to claim 1, further comprising: adjusting, by the system, the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 6, KUMAR discloses the method according to claim 5, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises: training the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 7, KUMAR discloses a system, comprising: at least one storage device (a system for classifying images comprising a memory storage device within the computing system; column 6, lines 9-60); at least one processing unit (the computing system further includes a computing processor and accompanying hardware; column 6, lines 9-60); and at least one memory that is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit (wherein said storage memory is in connection with said processor within the computing system and the memory unit stores a plurality of computer implemented systems to be executed by the processing unit; column 6, lines 9-60), the at least one processing unit comprising: an image classification model generation unit (wherein the processing unit is adapted to perform a computer program contains instructions when executed by the processor create (generate) an artificial neural network(s) which may be trained for tasks; column 6, lines 9-60), configured to train a first deep neural network (DNN) model for image classification of a group of categories of images (once the processor executes the instructions to create the network the system comprises a first trainable deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37), and wherein the feature extraction layer portion comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model (the features of the image are extracted via input layers 1, x1, x2, and xm which are adapted to extract features according to predetermined weights of desired values represented as w0, w1, w2, wm and fed into a net input function of the first artificial neural network; figure 5a; column 24, line 44 – column 25, line 55); and a feature extraction unit, configured extract the feature extraction layer portion from the first DNN model (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42); incorporate the feature extraction layer portion as an executable module in the at least one storage device of the system (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); and extract, using the feature extraction layer portion in the at least one storage device (storing feature via an on board storage memory device; column 6, lines 9-60; column 60, lines 13-34), respective features of images stored in the at least one storage device (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11); storing, in the at least one storage device, the respective features of the images (storing feature via an on board storage memory device; column 6, lines 9-60; column 60, lines 13-34); and  wherein image classification model generation unit is further configured to: retrieve, from the storage device, the respective features of training images in the images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of the group of categories of images (input training data samples are associated with various yield results, sub-samples (subgroups), and key events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11); train a second output layer portion for image classification of the sub- group of the group of categories of images, based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect and classify cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network, where the classification labels would be cancerous or ; column 45, lines 12-67), wherein the training of the second output layer portion does not comprise training the feature extraction layer portion (wherein the output layer as seen in figure 5a is unrelated to the training of input layers 1, x1, x2, and xm to extract features with weight relating to w0, w1, w2, wm; figure 5a; column 24, line 44 – column 25, line 55); and generate a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 8, KUMAR discloses the system according to claim 7, wherein the first DNN model is trained using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 9, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured to: receive a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classify, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 10, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured receive additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); extract, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24); and train the second output layer portion based further on the respective features of the additional training images (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 11, KUMAR discloses the system according to claim 7, wherein the image classification model generation unit is further configured to further adjust the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 12, KUMAR discloses the system according to claim 11, wherein the image classification model generation unit is further configured to train the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 13, KUMAR discloses the system according to claim 11, wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the second DNN model by using at least one graphics processing unit (the image classification system utilizing two neural network models to determine a result after training wherein the output layer may have its ratios adjusted and the system comprises a GPU to change those image settings; column 45, line 46 – column 46, line 12; column 52, lines 15-41).
As per claim 14, KUMAR discloses a non-transitory computer-readable medium, comprising machine- executable instructions that, in response to execution by a system comprising a processor, cause the device to perform operations (non-transitory computer-readable memory storage, comprising processor executable instructions that a method for training an image classifier system comprising a computing processor; column 6, lines 9-60), comprising: training a first deep neural network (DNN) model for image classification of a group of categories of images (the system comprises a first deep neural network which is trained via input training data; column 46, line 60 – column 47, line 37), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (as seen in figures 6A and 6B the neural network includes summing layer 609 acting as the feature extraction layer and output layer 610 acting as the output layer; figure 6A-6B; column 46, line 60 – column 47, line 37), and wherein the feature extraction layer portion comprises an input layer and one or more hidden layers, and does not comprise any output layers of the first DNN model (the features of the image are extracted via input layers 1, x1, x2, and xm which are adapted to extract features according to predetermined weights of desired values represented as w0, w1, w2, wm and fed into a net input function of the first artificial neural network; figure 5a; column 24, line 44 – column 25, line 55); extracting the feature extraction layer portion from the first DNN model (extracting various features via the summing layer 609 including MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608; figure 6A-6B; column 51, lines 10-42); incorporating the feature extraction layer portion as an executable module in a storage device of the system (storing feature via an on board storage memory device; column 60, lines 13-34); extracting, using the feature extraction layer portion in the storage device, a group of features of respective features of images stored in the storage device by (the summing layer 609 (feature extraction layer) is stored in the systems computing memory device adapted for storing executable instructions, and using the summing layer 609 to extract various features such as MDSC layer 602, T cell layer 604, age and sex layer 606, and time and temp layer 608 from the input images; figure 6A-6B; column 51, lines 10-42); storing, in the storage device, the respective features of the images (storing feature via an on board storage memory device; column 60, lines 13-34); retrieving, from the storage device, the respective features of training images in the images (the system may retrieve from the onboard memory device data relating to the image features collected via the summing layer 609; figure 6A-6B; column 60, lines 13-34; column 44, line 25 - column 45, line 11), wherein the training images are associated with a sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11); training a second output layer portion for image classification of the sub-group of the group of categories of images (input training data samples are associated with various yield results and events and are used to further train test or validate the events, the first network then trains a second network acting as the master network training the second pre trained network for a specific task in which the second network is more likely to be correct; column 42, line 20- column 43, line 11; column 44, line 25 - column 45, line 11), based on the respective features of the training images and according to classification labels of the training images (the second trained network is adapted to perform specific tasks once trained from the master network to complete said task, the pretrained network would be trained to detect cancer tumors in a biological sample and warn of tumor bearing samples once trained by the first master network; column 45, lines 12-67), wherein the training of the second output layer portion does not comprise training the feature extraction layer portion (wherein the output layer as seen in figure 5a is unrelated to the training of input layers 1, x1, x2, and xm to extract features with weight relating to w0, w1, w2, wm; figure 5a; column 24, line 44 – column 25, line 55); and generating a second DNN model for image classification of the sub-group of the group of categories of images by combining the feature extraction layer portion and the second output layer portion (the system then creates (generates) the second network which is then adapted to classify a naïve sample of biological tissue to be tumor bearing based on the detected features of the image by combing the features learned from summing layer 609 to output a trained second neural network comprising an output layer which would out put the likelihood of the tissue being tumor bearing; column 6, lines 9-60; column 46, line 60 – column 47, line 37).
As per claim 15, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the first DNN model is training using a subset of the images (the first neural network is trained via data sets and sub-samples of said data sets; column 67, lines 10-22).
As per claim 16, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: receiving a new image (the system receives an input image; figures 6A-6B; column 6, lines 9-60; column 46, line 38 – column 47, line 24); and classifying, using the second DNN model, the new image (the system receives an input image and is classified based on the pre trained second network to determine tumor likelihood of the biological sample used; figures 6A-6B; column 46, line 38 – column 47, line 24).
As per claim 17, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: receiving additional training images (the system receives training data as image data to train the two networks respectively; column 46, line 38 – column 47, line 24); extracting, using the feature extraction layer portion, respective features of the additional training images (the summing layer 609 extracts the plurality of features or each image used for training to determine an overall outcome based on said extracted features; column 46, line 38 – column 47, line 24); and wherein the training of the second output layer portion comprises training the second output layer portion based further on the respective features of the additional training images by (the second “pretrained” network is trained by the first or “master” network based on training images and the features extracted from those images; column 46, line 38 – column 47, line 24).
As per claim 18, KUMAR discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: adjusting the feature extraction layer portion and the second output layer portion of the second DNN model based on the training images and the classification labels of the training images (adjusting the output layer of the second pretrained network a threshold may be set for sensitivity and specificity at the output layer to range from 0 to 1 with .5 being equal weights to both features; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 19, KUMAR discloses the non-transitory computer-readable medium according to claim 18, wherein adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises: training the second DNN model by using original data of the training images and the classification labels (the second pretrained neural network which is trained via the first master network is then adapted to have its ratio set to a higher sensitivity ratio on the .00 to 1.00 scale say .70 to increase detection likelihood but also increases false positive rates when classifying biological samples as cancerous tumors where the classification labels would be cancerous or cancer free based on analyzation of the features collected from the input image; column 45, line 46 – column 46, line 12; column 46, line 38 – column 47, line 24).
As per claim 20, KUMAR discloses the non-transitory computer-readable medium according to claim 19, wherein the adjusting the feature extraction layer portion and the output layer portion of the second DNN model comprises using at least one graphics processing unit (the image classification system utilizing two neural network models to determine a result after training wherein the output layer may have its ratios adjusted and the system comprises a GPU to change those image settings; column 45, line 46 – column 46, line 12; column 52, lines 15-41).
Conclusion

CITED REFERENCE SUMMARIES: Ref US10360499B2: Claims:
A computer-implemented method of training an artificial neural network, the method comprising: (a) performing, by a computer, an augmentation process comprising: (1) obtaining a biological data sample comprising measurements from a flow cytometer instrument of a plurality of event features for a plurality of events of interest in a biological sample; (2) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest from the biological data sample; (3) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein the four or more flow cytometer measurement channels produces measurements of the plurality of event features contained in the sub-sample; (4) using the measurements of the plurality of event features for the plurality of events of interest contained in the sub-sample to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest in the sub-sample is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels; and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates an event of interest in the hypervoxel; and (b) applying, by the computer, a training process to an artificial neural network, wherein the training process comprises: (1) applying, by the computer, an artificial neural network detection structure to the distribution in the feature coordinate space indicative of the event population of interest, the detection structure employing the artificial neural network to correlate the distribution in the feature coordinate space indicative of the event population of interest with a distribution in a reference feature coordinate space indicative of a reference event population, wherein the detection structure is capable of detecting shifts and rotations in the event population of interest within the feature coordinate space relative to the reference event population within the reference feature coordinate space; (2) determining, by the computer, whether the biological sample contains cells indicative of a classification of the sub-sample; and (3) using the classification of the sub-sample and the measurements of the plurality of event features in the sub-sample to train the artificial neural network.
(a) performing, by a computer, an augmentation process comprising: (1) obtaining a biological data sample comprising measurements from a flow cytometer instrument of a plurality of event features for a plurality of events of interest in a biological sample; (2) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest from the biological data sample; (3) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein the four or more flow cytometer measurement channels produces measurements of the plurality of event features contained in the sub-sample; (4) using the measurements of the plurality of event features for the plurality of events of interest contained in the sub-sample to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest in the sub-sample is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels; and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates an event of interest in the hypervoxel; and
(1) obtaining a biological data sample comprising measurements from a flow cytometer instrument of a plurality of event features for a plurality of events of interest in a biological sample;
(2) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest from the biological data sample;
(3) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein the four or more flow cytometer measurement channels produces measurements of the plurality of event features contained in the sub-sample;
(4) using the measurements of the plurality of event features for the plurality of events of interest contained in the sub-sample to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest in the sub-sample is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels; and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates an event of interest in the hypervoxel; and
(i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels; and
(ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates an event of interest in the hypervoxel; and
(b) applying, by the computer, a training process to an artificial neural network, wherein the training process comprises: (1) applying, by the computer, an artificial neural network detection structure to the distribution in the feature coordinate space indicative of the event population of interest, the detection structure employing the artificial neural network to correlate the distribution in the feature coordinate space indicative of the event population of interest with a distribution in a reference feature coordinate space indicative of a reference event population, wherein the detection structure is capable of detecting shifts and rotations in the event population of interest within the feature coordinate space relative to the reference event population within the reference feature coordinate space; (2) determining, by the computer, whether the biological sample contains cells indicative of a classification of the sub-sample; and (3) using the classification of the sub-sample and the measurements of the plurality of event features in the sub-sample to train the artificial neural network.
(1) applying, by the computer, an artificial neural network detection structure to the distribution in the feature coordinate space indicative of the event population of interest, the detection structure employing the artificial neural network to correlate the distribution in the feature coordinate space indicative of the event population of interest with a distribution in a reference feature coordinate space indicative of a reference event population, wherein the detection structure is capable of detecting shifts and rotations in the event population of interest within the feature coordinate space relative to the reference event population within the reference feature coordinate space;
(2) determining, by the computer, whether the biological sample contains cells indicative of a classification of the sub-sample; and
(3) using the classification of the sub-sample and the measurements of the plurality of event features in the sub-sample to train the artificial neural network.
The method of claim 1 , comprising repeating step (a)(2) at least one time to generate sibling samples, wherein the sibling samples are at least two sub-samples generated from the biological data sample, wherein the sibling samples comprise a first sibling sample and a second sibling sample, wherein the first sibling sample comprises a number (N) of measurements of the biological sample and a second sibling sample, wherein the second sibling sample comprises at least N/4 measurements of the biological sample.
The method of claim 1 , further comprising: (c) determining a status of a condition in a subject by applying, by the computer, the artificial neural network to data obtained from a naïve biological sample from the subject, wherein the data comprise a plurality of event features for a plurality of events from the subject; and (d) generating, by the computer, a report comprising the status of the condition in the subject.
(c) determining a status of a condition in a subject by applying, by the computer, the artificial neural network to data obtained from a naïve biological sample from the subject, wherein the data comprise a plurality of event features for a plurality of events from the subject; and
(d) generating, by the computer, a report comprising the status of the condition in the subject.
The method of claim 3 , wherein the status of the condition comprises a presence of the condition, an absence of the condition, an increase in severity of the condition, a decrease in severity of the condition, a stage of the condition, a status associated with a prognosis, or a response to a therapy.
The method of claim 2 , wherein the training process in (b) further comprises updating the artificial neural network to improve a performance characteristic of the artificial neural network.
A computer-implemented method of using a trained artificial neural network to determine a status of a condition of a subject, the method comprising: (a) performing, by a computer, an analysis of a biological sample from a subject, the analysis comprising: (1) obtaining a biological data sample comprising measurements obtained from a flow cytometer instrument of a plurality of event features for a plurality of events of interest from the biological sample; (2) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein each of the four or more flow cytometer measurement channels produces measurements of the plurality of event features, and (3) using the measurements of the plurality of event features for the plurality of events of interest to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels, and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates the event of interest in the hypervoxel; (b) applying, by the computer, an artificial neural network to determine whether the biological sample contains cells indicative of the status of the condition of the subject; and (c) automatically generating, by the computer, a report comprising the status of the condition of the subject.
(a) performing, by a computer, an analysis of a biological sample from a subject, the analysis comprising: (1) obtaining a biological data sample comprising measurements obtained from a flow cytometer instrument of a plurality of event features for a plurality of events of interest from the biological sample; (2) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein each of the four or more flow cytometer measurement channels produces measurements of the plurality of event features, and (3) using the measurements of the plurality of event features for the plurality of events of interest to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels, and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates the event of interest in the hypervoxel;
(1) obtaining a biological data sample comprising measurements obtained from a flow cytometer instrument of a plurality of event features for a plurality of events of interest from the biological sample;
(2) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein each of the four or more flow cytometer measurement channels produces measurements of the plurality of event features, and
(3) using the measurements of the plurality of event features for the plurality of events of interest to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels, and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates the event of interest in the hypervoxel;
(i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the feature coordinate space into a plurality of hypervoxels, and
(ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates the event of interest in the hypervoxel;
(b) applying, by the computer, an artificial neural network to determine whether the biological sample contains cells indicative of the status of the condition of the subject; and
(c) automatically generating, by the computer, a report comprising the status of the condition of the subject.
The method of claim 6 , the method further comprising performing, by the computer, an augmentation process comprising: (a)(1)(A) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest; and (a)(1)(B) repeating step (a)(1)(A) to generate sibling samples, wherein the sibling samples are two or more sub-samples from the biological data sample, wherein the artificial neural network provides the status of the condition for at least three sibling samples from the same biological data sample to give a global status category of the biological data sample, wherein the global status category comprises the most probable category based on a frequency of the statuses of the condition of the sibling samples.
(a)(1)(A) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest; and
(a)(1)(B) repeating step (a)(1)(A) to generate sibling samples, wherein the sibling samples are two or more sub-samples from the biological data sample, wherein the artificial neural network provides the status of the condition for at least three sibling samples from the same biological data sample to give a global status category of the biological data sample, wherein the global status category comprises the most probable category based on a frequency of the statuses of the condition of the sibling samples.
The method of claim 6 , wherein the artificial neural network is one of at least three artificial neural networks that are used by a master network where the at least three artificial networks vary in at least one of number of network layers, number of neurons used, training function, method of output error calculation, weight and bias updating method, number of inputs, type of inputs, and combinations thereof.
The method of claim 6 , wherein determining whether the biological sample contains cells indicative of the status of the condition of the subject comprises identifying, by the computer, characteristic event features indicative of the status of the condition, thereby providing the status of the condition of the biological data sample and diagnosing the status of the condition in the subject.
The method of claim 6 , wherein automatically generating, by the computer, the report further comprises a treatment recommendation comprising an effective amount of a therapeutic agent for the status of the condition in the subject, wherein the generating is based on characteristic event features indicative of the status of the condition, and wherein the effective amount of the therapeutic agent is administered to the subject to treat the condition in the subject.
The method of claim 6 , wherein the status of the condition comprises a presence of the condition, an absence of the condition, an increase in severity of the condition, a decrease in severity of the condition, a stage of the condition, a status associated with a prognosis, or a response to a therapy.
The method of claim 6 , wherein multiple artificial neural networks are applied to the biological data sample and used to provide multiple statuses of the condition, wherein the multiple artificial neural networks are trained separately, wherein the multiple artificial neural networks provide an independent status of the condition, wherein the statuses of the condition by the multiple artificial neural networks are analyzed by a master neural network to give a global status category of the biological sample, and wherein the global status category is the most probable category based on a frequency of the statuses of the condition of the biological data sample.
The method of claim 6 , wherein the artificial neural network comprises a network output classifier, the network output classifier comprising a sigmoid activation function, wherein the sigmoid activation function comprising a network sigmoid output value over a threshold is classified into a first status and the value at or below the threshold is classified into a second status.

[Truncated to input limit]