Office Action 3: Non-Final Rejection (2024-03-27)
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 of categories of images, wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion, 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;
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, wherein the training of the second output layer portion does not comprise training the feature extraction layer portion; 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.

2\. (Previously Presented) The method according to claim 1, wherein the first DNN model is trained using a subset of the images.

3\. (Previously Presented) 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\. (Previously Presented) The method according to claim 1, 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.

5\. (Previously Presented) 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\. (Previously Presented) 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, 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; anda feature extraction unit configured to:
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
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 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,
wherein the training of the second output layer portion does not comprise training the feature extraction layer portion; andgenerate 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\. (Previously Presented) The system according to claim 7, wherein the first DNN model is trained using a subset of the images.

9\. (Previously Presented) 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\. (Previously Presented) The system according to claim 7, wherein the image classification model generation unit is further configured to:receive additional training images;
extract, using the feature extraction layer portion, 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\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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 comprising a 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, 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;
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;
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 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, wherein the training of the second output layer portion does not comprise training the feature extraction layer portion; 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\. (Previously Presented) The non-transitory computer-readable medium according to claim 14, wherein the first DNN model is training using a subset of the images.

16\. (Previously Presented) 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\. (Previously Presented) The non-transitory computer-readable medium according to claim 14, 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.

18\. (Previously Presented) 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\. (Previously Presented) 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\. (Previously Presented) 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.

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

APPLICANT REBUTTAL: REMARKS
Claims 1-20 are currently pending in the subject application and are presently under consideration.
Assignee's representative thanks Examiner Dhooge for courtesies extended during the telephonic interview conducted on June 18, 2024. Examiner was contacted to discuss the claim rejections under 35 U.S.C. § 102. During the interview, it was agreed that the herein arguments overcome the rejections under 35 U.S.C. § 102 identified in the Office Action. 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 herein.
I.
Rejection of Claims 1-20 under 35 U.S.C.
§
102(a)(1)
Claims 1-20 stand rejected under 35 U.S.C. § 102(a)(1) as allegedly being anticipated by Kumar et al. (US Patent No. 10,360,499 B2 to A). It is respectfully submitted that this rejection should be withdrawn for at least the following reasons. Kumar 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, and
wherein the feature extraction la
v
er portion comprises an input la
v
er and one or more hidden la
y
ers, and does not
comprise any output la
y
ers of the first DNN model;
extracting, by the system, the
feature extraction la
ver
portion
from the first DNN model;
incorporating, by the system, the feature extraction
lav
er portion as an executable module in a stora
g
e device of the
system
;
extractin
g
, by the system, usin
g
the feature extraction
lay
er portion in the storage device, respective features of
ima
g
es stored in the storage device
;
storin
g,
by the system, in the storage device, the respective features of the ima
ges;
retrievin
g,
by the system, from the stora
g
e device, the respective features of trainin
g
ima
g
es in the ima
g
es,
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,
wherein the trainin
g
of the second output
lay
er portion does not comprise trainin
g
the feature extraction
lay
er portion;
and
generating, by the system, a second DNN model for image classification of the sub-group of the group of categories of images by
combinin
g
the feature extraction la
y
er portion
and the second output la
v
er portion
(emphasis added)
Kumar et al. does not disclose all elements as recited in the subject claim. Figures 5A,
and 6B, column 24, line 44 to column 25, line 55, and column 46, lines 60 to column 47, line of Kumar et al. state:
Inputs   Weights
Net input
Activation
-}
output
FIG. 5A
linear systems in which the relationship among the variables is unknown or very complex. ANNs are trained using a data set and a target. The data set is conventionally divided into a training set, a test set, and, in some cases, a validation set. A target is specified that contains the correct classification of each sample in the data set. In particular, a type of neural network called a feed-forward back-propagation classifier can be trained on an input data set to classify input samples as belonging to a pre-defined category according to a target. The category can also refer to a disease or a condition. A set of samples from multiple categories is repeatedly presented to the ANN classifier input, and for each sample presented during training, the output generated by the ANN is compared with the desired target. The difference between the target and the set of input samples is calculated, and the ANN is modified using the back-propagation algorithm to cause the output to more closely approximate the desired target value. After a large number of training iterations, the ANN output will closely match
the desired target for each sample in the input training set.
Subsequently, when a new sample, not used during training, is presented to the ANN, it may generate an output classification value indicating which of the categories the new sample is most likely to fall into. The ANN is said to be able to "generalize" from its training to new, previously unseen input samples. This feature of ANNs allows them to be used to classify almost any input data which has a mathematically formulatable relationship to the
category to which it can be assigned.
A neural network is comprised of a series of layers of neurons.
A
_
typical neuron
in an ANN is shown in FIG. 5A. As illustrated in FIG. 5B, there is an
in
p
ut la
y
er,
to which data is presented; one or more
internal, or "hidden," la
y
ers;
and an
output la
y
er.
A neuron may be connected to neurons in other layers via connections that have weights, which are parameters that control the strength of the connection. The number of neurons in each layer may be related to the complexity of the problem to be solved.
The minimum number of neurons required in a layer may be determined by the problem complexity, and the maximum number may be limited by the ability of the neural network to generalize.
Excessive numbers of neurons may improve performance on the training set, but may result in poorer performance on new samples.
The actual number in a specific example is generally determined through a process of trial and error, following general rules well known in the art. The input neurons may receive data from the sample being presented and transmit that data to the first hidden layer through connections weights, which are modified during training. The first hidden layer may process the data and transmit
its result to the next layer through a second set of weighted connections. Each subsequent layer may "pool" the results from
the previous layers into more complex relationships.
Whereas conventional software programs may require writing specific instructions to perform  a function, ANNs may be programmed by training them with a known sample set and allowing them to modify themselves during training so as to provide a desired output such as a classification value. After training, when they are presented with new sample data, they can generalize what they have learned during training to be able to
classify the new previously unseen data.
ANNs have been applied to a number of problems in medicine, including image analysis, biochemical analysis, drug design, and diagnostics. ANNs have recently begun to be utilized for medical diagnostic problems. ANNs have the
ability to identify
relationships between patient data and disease and generate a diagnosis based exclusively on objective data input to the ANN.
The input data will typically consist of symptoms, biochemical
analysis, and other features such as age, sex, medical history, etc.
(column 46, lines 64 to column 47, line 24)
...the second network is more likely to be correct, because it is detecting a feature or features in that particular test data sample
that it generalizes better on than the first network.
One architecture for an ensemble neural network architecture is shown in FIG. 6B.
Pretrained networks 1 through n 628
,
,
have been previously trained on a first training data set 620, 622, 624, 626, which are presented as various
input la
y
ers 621
,
,
,
They are then incorporated in the master neural network as shown in the FIG. 6B. The
master neural network is then trained
on a second training data set 620, 622, 624, 626, which are presented as various
input la
y
ers 621
,
,
,
627 and are presented to the master network and to the
p
retrained networks simultaneously. The input la
y
ers and the
p
retrained networks can be
p
resented to a summing la
v
er 632 and then to the output la
v
er 632 of the artificial neural network
to provide levels of confidence in the classification of the sample, as normal 633 or as having cancer 634. The master network undergoes training as usual, but the pretrained networks retain the trained parameters they received in their pretraining. The
master network will learn
that when presented with a new biological data sample having certain characteristics, the most accurate condition category result will be a product of
its own training
on the input biological data sample and by learning to
g
ive the most weight
to the particular pretrained network that gives the most accurate output for that type of biological data sample.
The master network will therefore have the benefit of input from a plurality of specialized networks
and from its own training on a combination of a training set and the outputs
from the pretrained networks.
(emphasis added)
Fig 5A. does not disclose, as asserted in the Office Action:
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, xl, x21 and Xm   which  are adapted to extract features according to predetermined weights of desired values represented as w0, wl, w21 Wm and fed into a net input function of the first artificial neural network; figure Sa; column 24, line 44 -column 25, line 55).
Wherein, it is clearly seen that output layer in figure Sa 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 Sa.
(emphasis added)
Contrary to assertions in the Office Action, Kumar et al. clearly states that Fig. SA
discloses a single neuron (e.g. node) of an ANN, and that 1, xl, x21 and Xm are inputs to the single neuron, which produces an output that is fed to another neuron as shown in Fig. SB of Kumar et al.
Furthermore, the above cited paragraphs of Kumar et al. disclose a process whereby input layers from a previously trained artificial neural network are combined with pretrained networks, a summing layer, and an output layer to form a master neural network. The entire master neural network is then run through a training process, not just the output layer of the master neural network, thus requiring a more complex and resource intensive training.
In stark contrast, the claim discloses a previously trained first DNN model comprising a first output layer portion and a feature extraction layer portion comprising an input layer and one or more hidden layers. In order to create a second trained DNN model, only a second output layer portion is trained using training images and features extracted from
the training images that are already stored in the storage device. The training of the second output layer portion does not involve retraining the feature extraction layer portion. The feature extraction layer portion from the first DNN model and the second output layer portion are then combined to form the second DNN model that is trained for a different purpose than the first DNN model. Limiting the training to only the second output layer portion significantly reduces the computing resources and time in generating the trained second DNN model. Therefore, Kumar 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, and
wherein the feature extraction la
v
er portion comprises an input la
v
er and one or more hidden la
y
ers, and does not comprise any output la
y
ers of the first DNN model;
extracting, by the system, the
feature extraction la
v
er portion
from the first DNN model;
incorporating, by the system, the feature extraction la
v
er portion as an executable module in a storage device of the system; extracting, by the system, using the feature extraction la
v
er 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,
wherein the training of the second output la
v
er portion does not comprise training the feature extraction la
v
er portion;
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 la
v
er portion and the second output la
v
er portion
"
as
recited in claim 1, and similarly claims 7 and 14.
In view of the foregoing, Assignee's representative respectfully submits that Kumar 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.
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 .
Response to Amendment
This communication is filed in response to the arguments and amendments filed on 06/19/2024.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments filed on 06/19/2024 on pages 8-14, under REMARKS with respect to 35
U.S.C. 102 rejections to claims 1-20 have been fully considered and are persuasive. The rejections
to the claims have been withdrawn.
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiners statement of reason for allowance: the cited art of record the primary art of record US 10,360,499 B2 to A, KUMAR et al. (hereinafter “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 (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 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 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).
The second closest prior art of record US 10,467,501 B2 to N et al. (hereinafter “N”) discloses a method, comprising: training, by a system comprising a processor (column 16, lines 25-50), a first deep neural network (DNN) model for image classification of a group of categories of images (column 16, lines 25-55), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion, training, by the system (figures 12 and 14; column 11, lines 5-20; column 15, lines 14-52), a second output layer portion for image classification of the sub- group of the group of categories of image (figure 14; column 10, lines 15-64; column 12, lines 9-30). It is noted that prior art reference N discloses at figure 14 the single step of training only a loss layer which is single layer separated from the other groups of layers (1402A and 1402B) of the model however this singular training layer does not occur on an output layer.
The third closest prior art of record US 10,242,443 B2 to HSIEH et al. (hereinafter “HSIEH”) discloses a method, comprising: training, by a system comprising a processor (figure 18; column 8, lines 3-32; column 16, lines 43-64), a first deep neural network (DNN) model for image classification of a group of categories of images (column 20, lines 33-50), wherein the first DNN model comprises a feature extraction layer portion and a first output layer portion (column 38, lines 22-47; column 40, lines 8-18).
Further, these cited arts of the record fail to disclose the limitations/features of “and wherein the feature extraction layer portion only comprises an input layer and one or more hidden layers, wherein the training of the second output layer portion does not comprise training the feature extraction layer portion;”, recited in claim 1. Independent claims 7 and 14 are allowed for similar reasoning as independent claim 1. Dependent claims 2-6, 8-14, and 15-20 are allowed for the same reasons as the corresponding independent claims.
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.

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