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:


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
[Truncated to input limit]