Office Action 1: Non-Final Rejection (2023-08-09)
Rejection types: 102 - Lack of Novelty, 103 - Obviousness

CLAIMS:
CLAIMS

What is claimed is:

1\. A method, comprising:
generating, by a system comprising a processor, a feature extraction layer portion of an image classification model based on a deep neural network (DNN) model;
extracting a group of features of a group of images by using the feature extraction layer portion;
training an output layer portion of the image classification model according to features of the group of features of training images in the group of images and according to classification labels of the training images; and
generating the image classification model by combining the feature extraction layer portion and the output layer portion.
2\. The method according to claim 1, wherein the DNN model has more categories than the image classification model.
3\. The method according to claim 1, further comprising: storing the group of features of the group of images, resulting in a stored group of features.
4\. The method according to claim 3, further comprising:
acquiring the features of the training images by accessing at least some of the stored group of features.
5\. The method according to claim 1, wherein the generating the image classification model results in a generated image classification model, and further comprising:
further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images.
6\. The method according to claim 5, wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises:
training the image classification model by using original data of the training images and the classification labels.

7\. A system, comprising:
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:
a feature extraction unit, comprising at least a portion of a deep neural network (DNN) model and configured to extract a group of features of a group of images; and
an image classification model generation unit, configured to:
train an output layer portion of an image classification model according to features of the group of features of training images in the group of images and according to labels of the training images; and
generate the image classification model by combining the at least a portion of the DNN model and the output layer portion of the trained image classification model, resulting in a generated image classification model.

8\. The system according to claim 7, wherein the DNN model classifies according to more categories than the image classification model.

9\. The system according to claim 7, wherein the at least one processing unit further comprises:
an image feature storage unit, configured to store the group of features of the group of images extracted by the feature extraction unit.

10\. The system according to claim 9, wherein the image classification model generation unit is further configured to obtain the features of the training images by accessing the image feature storage unit.

11\. 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 output layer portion of the generated image classification model based on the training images and the classification labels of the training images.

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

13\. 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 image classification model by using at least one graphics processing unit.

14\. A non-transitory computer-readable medium, comprising machine-executable instructions that, in response to execution by a device using a processor, cause the device to perform operations, comprising:
generating a feature extraction layer portion of an image classification model based on a deep neural network (DNN) model;
extracting a group of features of a group of images by using the feature extraction layer portion;
training an output layer portion of the image classification model according to features of the group of features of training images in the group of images and according to classification labels of the training images; and
generating the image classification model by combining the feature extraction layer portion and the output layer portion.

15\. The non-transitory computer-readable medium according to claim 14, wherein the DNN model has more categories than the image classification model.

16\. The non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: storing the group of features of the group of images, resulting in a stored group of features.

17\. The non-transitory computer-readable medium according to claim 16, wherein the operations further comprise:
acquiring the features of the training images by accessing at least some of the stored group of features.

18\. The non-transitory computer-readable medium according to claim 14, wherein the generating the image classification model results in a generated image classification model, and
wherein the operations further comprise:
further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images.

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

20\. The non-transitory computer-readable medium according to claim 19, wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit. - 14 - - 15 - - 16 - - 17 -

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 .
Objections to the Specification:
In paragraph [0058], of line 8 contains improper grammar and is improperly omitting a coma within the phrase “and thus the computer readable medium” and in order to restore proper grammar will be interpreted as “and thus, the computer readable medium” for the purposes of this opinion.
Claim Rejections - 35 USC § 102:
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-11, 14-18 are rejected under 35 U.S.C 102(a1) as being anticipated by US 10262240 B2 to GUO, Y et al. (hereinafter “GUO”).
As per claim 1, GUO discloses a method, comprising: generating, by a system comprising a processor (a method carried out by a system comprising a processor for computing in order to generate extraction features based on the instructions executed by the processor; figure 2; abstract; column 2, lines 10-60), a feature extraction layer portion of an image classification model based on a deep neural network “DNN” model (a feature extraction process (layer) of a classifier 110 (classification model) using a deep neural network DNN model in order to train the classifier 110 on training data set 102; figure 2; column 3, lines 30-62; column 4, lines 4-25); extracting a group of features of a group of images by using the feature extraction layer portion (extracting data via the classifier 110 using input image 112 and extracting features of said image 112 in order to identify various classes 114 associated with the image 112; figure 2; column 3, lines 30-62; column 4, lines 4-25); training an output layer portion of the image classification model according to features of the group of features of training images in the group of images and according to classification labels of the training images (training via the training data set 102 such that classifier 214 can produce outputs (output layer) via the DNN wherein the output is determined via derived value features which were trained into the model during learning steps which allow the classifier model to learn various classes and other classification labels relevant to the images; figure 2; column 3, lines 13-62; column 4, lines 4-59); and generating the image classification model by combining the feature extraction layer portion and the output layer portion (wherein the feature extraction process and the output process are both conducted via the classifier 110 and are therefore combined into the classifier 110 to provide the overall image classification model; figure 2-3; column 3, lines 13-62; column 4, lines 4-59).
As per claim 2, GUO discloses the method according to claim 1, wherein the DNN model has more categories than the image classification model (a categorical distribution is utilized within the system wherein the SoftMax function determines the categorical distribution in multi class classification methods; figures 2-3; column 5, lines 4-24).
As per claim 3, GUO discloses the method according to claim 1, further comprising: storing the group of features of the group of images, resulting in a stored group of features (wherein the features selected are stored in a computer-based memory which has storage functionality to store computer executable instructions; figures 2-3; column 11, lines 19-30).
As per claim 4, GUO discloses the method according to claim 3, further comprising: acquiring the features of the training images by accessing at least some of the stored group of features (the features provided as stored instructions in mass storage device 916 adapted to act as a machine readable medium 922 storage device and has the ability to access a plurality of feature classification layers and determine the weight provided to said feature; figures 2-3; column 11, lines 19-51; CLAIM 12-13).
As per claim 7, GUO discloses a system, comprising: at least one processing unit (the system comprises a computer processor; figures 2-3; column 11, lines 19-51); 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 (a static memory 906 that includes a hardware processor 902 which executes instructions 924 via the processor 902 wherein said instructions 924 are stored on memory 906; figures 2-3; column 11, lines 19-51), the at least one processing unit comprising: a feature extraction unit (the system which includes a processor further includes a feature extraction system; figure 2; column 4, lines 4-37; column 11, lines 19-51), comprising at least a portion of a deep neural network “DNN” model and configured to extract a group of features of a group of images (a feature extraction process (layer) of a classifier 110 (classification model) using a deep neural network DNN model in order to train the classifier 110 on training data set 102; figure 2; column 3, lines 30-62; column 4, lines 4-25); and an image classification model generation unit (wherein the extracted features resulting in the production of generating an image classifier 110 (model) adapted to classify images by associated features; figure 2; column 3, lines 40-46; column 3, lines 30-62; column 4, lines 4-25), configured to: train an output layer portion of an image classification model according to features of the group of features of training images in the group of images and according to labels of the training images (training via the training data set 102 such that classifier 214 can produce outputs (output layer) via the DNN wherein the output is determined via derived value features which were trained into the model during learning steps which allow the classifier model to learn various classes and other classification labels relevant to the images; column 3, lines 13-62; column 4, lines 4-59); and generate the image classification model by combining the at least a portion of the DNN model and the output layer portion of the trained image classification model (wherein the feature extraction process and the output process are both conducted via the classifier 110 and are therefore combined into the classifier 110 to provide the overall image classification model; column 3, lines 13-62; column 4, lines 4-59), resulting in a generated image classification model (resulting in the production of generating an image classifier 110 (model) adapted to classify images by associated features; column 3, lines 40-46).
As per claim 8, GUO discloses the system according to claim 7, wherein the DNN model classifies according to more categories than the image classification model (a categorical distribution is utilized within the system wherein the SoftMax function determines the categorical distribution in multi class classification methods; figure 2; column 5, lines 4-24).
As per claim 9, GUO discloses the system according to claim 7, wherein the at least one processing unit further comprises: an image feature storage unit, configured to store the group of features of the group of images extracted by the feature extraction unit (wherein the features selected are stored in a computer-based memory which has storage functionality to store computer executable instructions; column 11, lines 19-30).
As per claim 10, GUO discloses the system according to claim 9, wherein the image classification model generation unit is further configured to obtain the features of the training images by accessing the image feature storage unit (the features provided as stored instructions in mass storage device 916 adapted to act as a machine readable medium 922 storage device and has the ability to access a plurality of feature classification layers and determine the weight provided to said feature; column 11, lines 19-51; CLAIM 12-13).
As per claim 11, GUO 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 output layer portion of the generated image classification model based on the training images and the classification labels of the training images (the classifier model is trained via a predetermined distribution wherein the training parameters may be changed as desired wherein the parameters are learned from a predecessor network and uses an image classification probability function; column 3, lines 1-29).
As per claim 14, GUO discloses a non-transitory computer-readable medium, comprising machine-executable instructions that (a static memory 906 that includes a hardware processor 902 which executes instructions 924 via the processor 902 wherein said instructions 924 are stored on memory 906; figure 3; abstract; column 2, lines 10-60; column 11, lines 19-51), in response to execution by a device using a processor (the instructions 924 are executed via processor 902; figure 3; column 11, lines 19-51), cause the device to perform operations, comprising: generating a feature extraction layer portion of an image classification model based on a deep neural network “DNN” model (generating a feature extraction process (layer) of a classifier 110 (classification model) using a deep neural network DNN model in order to train the classifier 110 on training data set 102; figures 2-3; column 3, lines 30-62; column 4, lines 4-25); extracting a group of features of a group of images by using the feature extraction layer portion (wherein the extracted features resulting in the production of generating an image classifier 110 (model) adapted to classify images by associated features; figure 2; column 3, lines 30-62; column 4, lines 4-59); training an output layer portion of the image classification model according to features of the group of features of training images in the group of images and according to classification labels of the training images (training via the training data set 102 such that classifier 214 can produce outputs (output layer) via the DNN wherein the output is determined via derived value features which were trained into the model during learning steps which allow the classifier model to learn various classes and other classification labels relevant to the images; figures 2-3; column 3, lines 13-62; column 4, lines 4-59); and generating the image classification model by combining the feature extraction layer portion and the output layer portion (wherein the feature extraction process and the output process are both conducted via the classifier 110 and are therefore combined into the classifier 110 to provide the overall image classification model; figures 2-3; column 3, lines 40-62; column 4, lines 4-59).
As per claim 15, GUO discloses the non-transitory computer-readable medium according to claim 14, wherein the DNN model has more categories than the image classification model (a categorical distribution is utilized within the system wherein the SoftMax function determines the categorical distribution in multi class classification methods; column 5, lines 4-24).
As per claim 16, GUO discloses the non-transitory computer-readable medium according to claim 14, wherein the operations further comprise: storing the group of features of the group of images, resulting in a stored group of features (wherein the features selected are stored in a computer-based memory which has storage functionality to store computer executable instructions; column 11, lines 19-30).
As per claim 17, GUO discloses the non-transitory computer-readable medium according to claim 16, wherein the operations further comprise: acquiring the features of the training images by accessing at least some of the stored group of features (the features provided as stored instructions in mass storage device 916 adapted to act as a machine readable medium 922 storage device and has the ability to access a plurality of feature classification layers and determine the weight provided to said feature; column 11, lines 19-51; CLAIM 12-13).
As per claim 18, GUO discloses the non-transitory computer-readable medium according to claim 14, wherein the generating the image classification model results in a generated image classification model (resulting in the production of generating an image classifier 110 (model) adapted to classify images by associated features; column 3, lines 1-46), and wherein the operations further comprise: further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images (the classifier model is trained via a predetermined distribution wherein the training parameters may be changed as desired wherein the parameters are learned from a predecessor network and uses an image classification probability function; column 3, lines 1-29).
Claim Rejections – 35 USC § 103:
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 5-6, 12-13, and 19-20 are rejected under 35 USC 103 as being unpatentable over GUO in view of US 10742399 B2 to CHEN, YEN-KUANG et al. (hereinafter “CHEN”).
As per claim 5, GUO discloses the method according to claim 1. GUO fails to disclose wherein the generating the image classification model results in a generated image classification model, and further comprising: further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images.
CHEN discloses wherein the generating the image classification model results in a generated image classification model (wherein the system generates butterfly layers for image classification are adapted to separate clusters of data and combine the layers in order to classify said images; column 47, lines 3- 58), and further comprising: further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images (wherein the system can be adjusted over any number of data points wherein in the example provided the desired data points are four data points which correspond to said clusters and process the points until all of the clusters are sorted into reference labels (classification label) to sort said data; column 46, lines 20-33; column 47, lines 3- 58; column 49, lines 8-23).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of GUO to provide wherein the generating the image classification model results in a generated image classification model, and further comprising: further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model based on the training images and the classification labels of the training images, as taught by CHEN, in order to provide the ability to use a plurality of data points up to the amount desired by the user (CHEN column 49, lines 8-23).
As per claim 6, GUO discloses the method according to claim 5. GUO fails to disclose wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels.
CHEN discloses wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels (the model is trained by using fully decompressed data points (original data) of the deep learning neural networks in order to visualize data directly and to properly label and classify said data via the model; column 46, lines 20-33; column 47, lines 3- 58; column 49, lines 8-23).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of GUO to provide wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels, as taught by CHEN, in order to provide the ability to visualize data directly (column 46, lines 20-33).
As per claim 12, GUO discloses the system according to claim 11. GUO fails to disclose wherein the image classification model generation unit is further configured to train the image classification model by using original data of the training images and the classification labels.
CHEN discloses wherein the image classification model generation unit is further configured to train the image classification model by using original data of the training images and the classification labels (the model is trained by using fully decompressed data points (original data) of the deep learning neural networks in order to visualize data directly and to properly label and classify said data via the model; column 46, lines 20-33; column 47, lines 3- 58; column 49, lines 8-23).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of GUO to provide wherein the image classification model generation unit is further configured to train the image classification model by using original data of the training images and the classification labels, as taught by CHEN, in order to provide the ability to visualize data directly (column 46, lines 20-33).
As per claim 13, GUO discloses the system according to claim 11. GUO fails to disclose wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit.
CHEN discloses wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit (the system utilizes a graphics processor in order to exchange image information which includes feature data and image output data within the system; column 19, lines 7-55; column 47, lines 3- 58).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of GUO to provide wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit, as taught by CHEN, in order to provide a graphics processing unit to handle the computing resources required to analyze and sort the amount of image data available to the system (GUO column 19, lines 7-55).
As per claim 19, GUO discloses the non-transitory computer-readable medium according to claim 18. GUO fails to disclose wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels.
CHEN discloses wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels (the model is trained by using fully decompressed data points (original data) of the deep learning neural networks in order to visualize data directly and to properly label and classify said data via the model; column 46, lines 20-33; column 47, lines 3- 58; column 49, lines 8-23).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of GUO to provide wherein further adjusting the feature extraction layer portion and the output layer portion of the generated image classification model comprises: training the image classification model by using original data of the training images and the classification labels, as taught by CHEN, in order to provide the ability to visualize data directly (column 46, lines 20-33).
As per claim 20, GUO discloses the non-transitory computer-readable medium according to claim 19. GUO fails to disclose wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit.
CHEN discloses wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit (the system utilizes a graphics processor in order to exchange image information which includes feature data and image output data within the system; column 19, lines 7-55; column 47, lines 3- 58).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of GUO to provide wherein the image classification model is further configured to adjust the feature extraction layer portion and the output layer portion of the image classification model by using at least one graphics processing unit, as taught by CHEN, in order to provide a graphics processing unit to handle the computing resources required to analyze and sort the amount of image data available to the system (GUO column 19, lines 7-55).
Conclusion:

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 .
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.

CITED REFERENCE SUMMARIES: Ref US10262240B2: Claims:
A method comprising: training, by one or more processors, 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; 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 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; receiving, by the one or more processors, an image for recognition; and recognizing, by the one or more processors, the image utilizing the successor classifier.
training, by one or more processors, 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;
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;
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 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;
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;
receiving, by the one or more processors, an image for recognition; and
recognizing, by the one or more processors, the image utilizing the successor classifier.
The method as recited in claim 1 , wherein initializing parameters of the successor classifier further comprises: initializing a weight vector w k for a class k based on a set of indexes for training samples which belong to the k class.
initializing a weight vector w k for a class k based on a set of indexes for training samples which belong to the k class.
The method as recited in claim 2 , wherein initializing parameters of the successor classifier further comprises: initializing the weight vector w k for the class k further based on an average direction of vectors for the class k in the predecessor network.
initializing the weight vector w k for the class k further based on an average direction of vectors for the class k in the predecessor network.
The method as recited in claim 2 , wherein vectors for a same class are located substantially on a same hyper-sphere, wherein a hyper-plane separating classes goes through an origin point.
The method as recited in claim 1 , wherein the predecessor network includes a plurality of feature-extraction layers and the predecessor classifier, wherein the successor network includes the plurality of feature-extraction layers and the successor classifier.
The method as recited in claim 5 , wherein 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.
The method as recited in claim 5 , wherein the feature-extraction layers of the successor network share same parameters as the feature-extraction layers of the predecessor network.
A system comprising: a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising: 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; 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; receiving an image for recognition; and recognizing the image utilizing the successor classifier.
a memory comprising instructions; and
one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising: 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; 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; receiving an image for recognition; and recognizing the image utilizing the successor classifier.
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;
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;
receiving an image for recognition; and
recognizing the image utilizing the successor classifier.
The system as recited in claim 8 , wherein initializing parameters of the successor classifier further comprises: initializing a weight vector w k for a class k based on a set of indexes for training samples which belong to the k class.
The system as recited in claim 9 , wherein initializing parameters of the successor classifier further comprises: initializing the weight vector w k for the class k further based on an average direction of vectors for the class k in the predecessor network.
The system as recited in claim 9 , wherein vectors for a same class are located substantially on a same hyper-sphere, wherein a hyper-plane separating classes goes through an origin point.
The system as recited in claim 8 , wherein the predecessor network includes a plurality of feature-extraction layers and the predecessor classifier, wherein the successor network includes the plurality of feature-extraction layers and the successor classifier.
The system as recited in claim 12 , wherein 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.
A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: 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; 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; receiving an image for recognition; and recognizing the image utilizing the successor classifier.
The machine-readable storage medium as recited in claim 14 , wherein initializing parameters of the successor classifier further comprises: initializing a weight vector w k for a class k based on a set of indexes for training samples which belong to the k class.
The machine-readable storage medium as recited in claim 15 , wherein initializing parameters of the successor classifier further comprises: initializing the weight vector w k for the class k further based on an average direction of vectors for the class k in the predecessor network.
The machine-readable storage medium as recited in claim 15 , wherein vectors for a same class are located substantially on a same hyper-sphere, wherein a hyper-plane separating classes goes through an origin point.
The machine-readable storage medium as recited in claim 14 , wherein the predecessor network includes a plurality of feature-extraction layers and the predecessor classifier, wherein the successor network includes the plurality of feature-extraction layers and the successor classifier.
The machine-readable storage medium as recited in claim 18 , wherein 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.
The machine-readable storage medium as recited in claim 18 , wherein the feature-extraction layers of the successor network share same parameters as the feature-extraction layers of the predecessor network.
Ref US10742399B2: Claims:
An apparatus, comprising: a storage device to store a plurality of compressed images, wherein the plurality of compressed images comprises one or more compressed master images and one or more compressed slave images, wherein the one or more compressed slave images are compressed with reference to the one or more compressed master images; and a processor to: identify an uncompressed image; access context information associated with a plurality of images, wherein the plurality of images comprises the uncompressed image and the one or more compressed master images; determine, based at least in part on the context information, whether the uncompressed image is associated with a corresponding master image from the one or more compressed master images; 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.
a storage device to store a plurality of compressed images, wherein the plurality of compressed images comprises one or more compressed master images and one or more compressed slave images, wherein the one or more compressed slave images are compressed with reference to the one or more compressed master images; and
a processor to: identify an uncompressed image; access context information associated with a plurality of images, wherein the plurality of images comprises the uncompressed image and the one or more compressed master images; determine, based at least in part on the context information, whether the uncompressed image is associated with a corresponding master image from the one or more compressed master images; 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.
identify an uncompressed image;
access context information associated with a plurality of images, wherein the plurality of images comprises the uncompressed image and the one or more compressed master images;
determine, based at least in part on the context information, whether the uncompressed image is associated with a corresponding master image from the one or more compressed master images;
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.
The apparatus of claim 1 , wherein the processor to determine, based at least in part on the context information, whether the uncompressed image is associated with the corresponding master image from the one or more compressed master images is further to: identify a correlation between the context information associated with each of the uncompressed image and the corresponding master image; identify a correlation between a plurality of image features associated with each of the uncompressed image and the corresponding master image; and determine, based at least in part on the correlation between the context information and the correlation between the plurality of image features, that the uncompressed image is associated with the corresponding master image.
identify a correlation between the context information associated with each of the uncompressed image and the corresponding master image;
identify a correlation between a plurality of image features associated with each of the uncompressed image and the corresponding master image; and
determine, based at least in part on the correlation between the context information and the correlation between the plurality of image features, that the uncompressed image is associated with the corresponding master image.
The apparatus of claim 2 , wherein the context information comprises the following for at least one image of the plurality of images: a time; and a location.
The apparatus of claim 3 , wherein the context information further comprises the following for the at least one image of the plurality of images: image pixel data; or extracted image feature data.
image pixel data; or
extracted image feature data.
The apparatus of claim 3 , wherein the context information further comprises a mobile device orientation for the at least one image of the plurality of images.
The apparatus of claim 3 , wherein the processor to identify the correlation between the context information associated with each of the uncompressed image and the corresponding master image is further to: determine, based on the context information, that the uncompressed image and the corresponding master image were captured at a similar time and in a similar location.
determine, based on the context information, that the uncompressed image and the corresponding master image were captured at a similar time and in a similar location.
The apparatus of claim 1 , wherein the processor is further to: upon compressing the uncompressed image into the corresponding compressed image with reference to the corresponding master image, designate the corresponding compressed image with a slave image designation; and upon compressing the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images, designate the corresponding compressed image with a master image designation.
upon compressing the uncompressed image into the corresponding compressed image with reference to the corresponding master image, designate the corresponding compressed image with a slave image designation; and
upon compressing the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images, designate the corresponding compressed image with a master image designation.
The apparatus of claim 7 , wherein the processor is further to: determine that the uncompressed image is not associated with the corresponding master image from the one or more compressed master images; compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and designate the corresponding compressed image with the master image designation.
determine that the uncompressed image is not associated with the corresponding master image from the one or more compressed master images;
compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and
designate the corresponding compressed image with the master image designation.
The apparatus of claim 8 , wherein the processor is further to: identify a second uncompressed image; determine that the second uncompressed image is associated with the corresponding compressed image with the master image designation; and compress the second uncompressed image with reference to the corresponding compressed image with the master image designation.
identify a second uncompressed image;
determine that the second uncompressed image is associated with the corresponding compressed image with the master image designation; and
compress the second uncompressed image with reference to the corresponding compressed image with the master image designation.
The apparatus of claim 1 , wherein: the processor to compress the uncompressed image into the corresponding compressed image with reference to the corresponding master image is further to compress the uncompressed image using inter-frame encoding; and the processor to compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images is further to compress the uncompressed image using intra-frame encoding.
the processor to compress the uncompressed image into the corresponding compressed image with reference to the corresponding master image is further to compress the uncompressed image using inter-frame encoding; and
the processor to compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images is further to compress the uncompressed image using intra-frame encoding.
The apparatus of claim 10 , wherein the processor to compress the uncompressed image using inter-frame encoding is further to compress the uncompressed image using a video codec.
The apparatus of claim 1 , further comprising a communication interface to receive the uncompressed image and the co
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