CTFR 18/569,070 CTFR 82662 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Arguments 07-38-01 AIA Applicant’s arguments, see page 8 , filed 2/23/2026 , with respect to drawing objection have been fully considered and are persuasive. The objection of the drawings has been withdrawn. 07-37 AIA Applicant's arguments filed 2/23/2026 have been fully considered but they are not persuasive. The specification objection is maintained since the title is requested to be more informative for more accurate classification and searching of the claimed subject matter . Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on all references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The arguments state that the previously applied references do not disclose the features of “ preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans to a predetermined number of slices ” and “ performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis based on machine learning model trained to classify hematoma expansion .” The references of Moore and the new reference of Chilamkurhy are used to cure the deficiency of the previously applied references and will be explained below. Regarding the Moore reference, this reference performs the feature of performing preprocessing of the diagnostic scans to resize the scans to a size and it performs the re-slicing of the diagnostic scans, which is taught in ¶ [24] and [25]. In addition, it is considered in these paragraphs along with ¶ [33] that a machine learning model performs machine learning analysis on the preprocessed diagnostic scans in order to make a prediction about diagnostic scans. The prediction is based off the training of the machine learning system using the preprocessed data to perform a prediction about diagnostic scans presented to the system. However, this reference is deficient in disclosing the predetermined number of slices for the re-slicing, which is taught by Chilamkurhy. Regarding the reference of Chilamkurhy, this reference teaches preprocessing data for resizing and re-slicing for three particular windows, which is considered as the predetermined number of slices of a certain size. This is taught in col. 9, ll. 55-col. 10, ll. 4. Therefore, combining this reference with the base reference to determine hematoma expansion and the Moore reference of preprocessing diagnostic scans for resizing and re-slicing, the features of the independent claims are performed. Thus, based on the above, the features of the claims are disclosed below . Specification 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. 06-11-01 AIA The following title is suggested: Systems and Methods for Prediction of Hematoma Expansion Using Automated Deep Learning Image Analysis BY A TRAINED MACHINE LEARNING MODEL AND PERFORMING PREPROCESSING TO DIAGNOSTIC SCANS . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 1, 2, 5, 8, 9, 12, 15, 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (CN-112991320A Filing Date:4/7/2021) in view of Moore (US Pub 2019/0088359) and Chilamkurhy (USP 10475182) . Re claim 1: Zhang discloses a computer-implemented method for prediction of hematoma expansion using machine learning techniques, comprising: retrieving electronic data corresponding to one or more diagnostic scans of a patient (e.g. a CT image of a brain hematoma is obtained by the system, which is taught in ¶ [40] and [41].); [0040] The embodiment of the invention 1 provides a cerebral hemorrhage patient hematoma expanding risk prediction system, the system comprising: [0041] an obtaining module, for receiving the brain hematoma CT image and clinical information of cerebral hemorrhage patient input; preprocessing the one or more diagnostic scans of the patient (e.g. pre-processing of data occurs in ¶ [52].); [0051] training the clinical prediction model comprises: [0052] obtaining the clinical information of the patient and pre-processing, comprising data cleaning, conversion and integration, the deletion value by artificial filling method; performing, by a machine learning computing system, machine learning analysis based on machine learning model trained to classify hematoma expansion (e.g. the trained prediction model is trained to output a risk of the prediction result, which is taught in ¶ [42] and [43].); and [0042] a judging module for analyzing the input brain hematoma CT image and clinical information; outputting the risk prediction result of the hematoma of the cerebral hemorrhage patient expanding in a certain period; wherein, [0043] using the trained image prediction model; analyzing the brain hematoma CT image; obtaining the key image characteristic of the hematoma expansion; using the trained clinical prediction model; analyzing the clinical information; obtaining the key clinical features of hematoma expansion. predicting, based on the machine learning analysis of the one or more diagnostic scans, a probability of hematoma expansion for the patient (e.g. the system discloses predicting a probability of whether hematoma expansion is predicted or is stable, which is taught in ¶ [55] and [56].). [0055] The clinical information includes: patient basic information, onset time, hospital temperature, blood pressure, GCS score, life history and history, related disease history condition, anticoagulant or anti-platelet drug history, laboratory examination index condition in 24 hours. [0056] the judging module outputs the final risk prediction result in two classification form, wherein the output " 0 " represents the hematoma stable, output " 1 " represents the hematoma expansion. However, Zhang fails to specifically teach the features of retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans to a predetermined number of slices. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient (e.g. the invention discloses a data storage as a repository that stores scans from CT imaging devices, which is taught in ¶ [17] and [18].); [0017] Referring generally to FIG. 1, a block diagram depicting a medical imaging analysis system 100 configured in accordance with the present disclosure is shown. The medical imaging analysis system 100 may include one or more medical imaging devices 102 (e.g., X-ray radiography devices, ultrasound imaging devices, CT imaging devices, tomography PET devices, MRI devices, cardiac imaging devices, digital pathology devices, endoscopy devices, arthoscopy devices, medical digital photography devices, ophthalmology imaging devices or the like) in communication with an analyzer 104. The analyzer 104 may include one or more data storage devices 106 (e.g., magnetic storage devices, optical storage devices, solid-state storage devices, network-based storage devices or the like) configured to store images acquired by the medical imaging devices 102. [0018] The one or more data storage devices 106 may be further configured to serve as a data repository of historical data, which may form a large data set that may be referred to as “big data.” This large data set can be utilized to train one or more predictive models executed on one or more processing units 108 of the analyzer 104. The predictive model(s) trained in this manner may then be utilized to analyze images acquired by the medical imaging devices 102. For example, the analyzer 104 may be configured to recognize one or more features of interest in the images acquired by the medical imaging devices 102. The analyzer 104 may also be configured to determine whether the feature/features of interest represent abnormality/abnormalities. The analyzer 104 may be further configured to determine whether a feature of interest is a certain type of object (e.g., a hardware implant) inside a patient's body. Determinations made by the analyzer 104 may be recorded (e.g., into the data storage devices 106) and/or reported to a user (e.g., a radiologist, a doctor or the like) via one or more output devices 110-114. preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans (e.g. the invention discloses the preprocessing steps of resizing scans to be a certain size and re-slicing scans, which is taught in ¶ [24] and [25].). [0024] In some embodiments, the medical images retrieved from the data repository may undergo one or more data preprocessing operations in a data preprocessing step 304. It is to be understood that the data preprocessing operations depicted in the data preprocessing step 304 are presented merely for illustrative purposes and are not meant to be limiting. For example, images recorded in different formats may be extracted and converted to a common format. Images of different sizes may be resized (e.g., in the X and Y directions) and/or resliced (e.g., in the Z direction for 3D images) to a predetermined size. Additional image enhancement techniques, such as contrast adjustment for certain images (e.g., windowing for CT images), may also be applied to make certain abnormalities more readily identifiable. In still another example, data augmentation techniques, including artificially increasing the sample size by creating multiple instances of the same image using operations such as rotation, translation, mirroring, and changing reslicing parameters, may also be employed in the data preprocessing step 304, along with other optional data preprocessing techniques without departing from the spirit and scope of the present disclosure. [0025] The data prepared in this manner may be utilized to help train one or more predictive models in a model development step 306. The predictive model(s) may execute on one or more processing units (e.g., graphical processing units, or GPUs, central processing units, or CPUs), and a training process that uses machine learning may be utilized to train the predictive model(s) based on the prepared data. Suitable machine learning techniques may include, for example, a convolutional neural network (CNN), which is a type of feed-forward artificial neural network that uses multiple layers of small artificial neuron collections to process portions of the training images to build a predictive model for image recognition. The CNN architecture may include one or more convolution layers, max pooling layers, contrast normalization layers, fully connected layers and loss layers. Each of these layers may include multiple parameters. It is noted that while some of the parameters are utilized to govern the entire training process (including parameters such as choice of loss function, learning rate, weight decay coefficient, regularization parameters and the like), values applied to other parameters may be changed (e.g., CCN parameters and layers trained on head CT images may need to be changed for chest X-ray images), which may in turn change the CNN. The CNN may also be changed when the number, the size, and/or the sequence of its layers are changed, allowing the CNN to be trained accordingly. performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis; and predicting, based on the machine learning analysis of the pre-processed one or more diagnostic scans (e.g. the system discloses training the system to have the machine learning model predict a probability of an increase in the subdural hematoma when diagnostic scans are input, which is taught in ¶ [33].). [0033] It is further noted that while the aforementioned report may be presented as a text-based report, some embodiments in accordance with the present disclosure may be configured to generate a more informative, text- and/or graphic-based report depicted in FIG. 6. As shown generally in FIGS. 5 and 6, a predictive model developed based on a repository of CT images may process a CT image 600 of a patient in a step 518 and recognize that the CT image 600 of the patient exhibits a feature 602 that may be of an interest. The predictive model may also determine that there is a certain probability 604 for the feature 602 to be considered abnormal because the feature 602 is likely to represent a mild increase in the subdural hematoma overlaying the right frontoparietal convexity. Findings as such may be utilized to pre-populate certain fields on a report template and may be provided to radiologists for review. Optional and/or additional support information 606 may also be provided to help radiologists make more informed decisions. Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans; performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis; and predicting, based on the machine learning analysis of the pre-processed one or more diagnostic scans, incorporated in the device of Zhang, in order to have a storage to store data reflecting data scans that are provided to a predictive model for analysis, which can reduce workloads of healthcare professionals and improve accuracy analysis (as stated in Moore ¶ [03]). However, the combination above fails to specifically teach the features of to re-slice the one or more diagnostic scans to a predetermined number of slices. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses to re-slice the one or more diagnostic scans to a predetermined number of slices (e.g. the invention discloses in preprocessing data in resizing and re-slicing the data. The data re-slice is uses three separate windows considered as the predetermined number of slices of a certain size, which is taught in col. 9, ll. 55-col. 10, ll. 4.). (43) 1.3.4 Preprocessing (44) For a given CT scan, the non-contrast axial series which uses soft reconstruction kernel was used and resampled so that slice thickness is around 5 mm. All the slices of this series were resized to a size of 224×224 pixels before passing to our deep learning models. Instead of passing the whole dynamic range of CT densities as a single channel, the densities were windowed by using three separate windows and stacked as channels. Windows used were brain window (1=40, w=80), bone window (1=500, w=3000) and subdural window (1=175, w=50). This was because fracture visible in the bone window could indicate existence of an extra axial bleed in the brain window and conversely, presence of scalp hematoma in the brain window could correlate with a fracture. Subdural window helps differentiate between the skull and an extra axial bleed that might have been indistinguishable in a normal brain window. Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of to re-slice the one or more diagnostic scans to a predetermined number of slices, incorporated in the device of Zhang, as modified by Moore, in order to provide a number of re-slicing data to differentiate a hematoma from another injury, which can improve notification of critical findings when CT head scans are acquired (as stated in Chilamkurhy col. 3, ll. 55-col. 4, ll. 7). Re claim 2: However, Zhang fails to specifically teach the features of the computer-implemented method of claim 1, wherein preprocessing the one or more diagnostic scans comprises one or both of of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses wherein preprocessing the one or more diagnostic scans comprises one or both of of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans (e.g. the invention discloses re-sizing the medical images and changing re-slicing parameters to perform re-slicing, which is taught in ¶ [24].). [0024] In some embodiments, the medical images retrieved from the data repository may undergo one or more data preprocessing operations in a data preprocessing step 304. It is to be understood that the data preprocessing operations depicted in the data preprocessing step 304 are presented merely for illustrative purposes and are not meant to be limiting. For example, images recorded in different formats may be extracted and converted to a common format. Images of different sizes may be resized (e.g., in the X and Y directions) and/or resliced (e.g., in the Z direction for 3D images) to a predetermined size. Additional image enhancement techniques, such as contrast adjustment for certain images (e.g., windowing for CT images), may also be applied to make certain abnormalities more readily identifiable. In still another example, data augmentation techniques, including artificially increasing the sample size by creating multiple instances of the same image using operations such as rotation, translation, mirroring, and changing reslicing parameters, may also be employed in the data preprocessing step 304, along with other optional data preprocessing techniques without departing from the spirit and scope of the present disclosure. Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein preprocessing the one or more diagnostic scans comprises one or both of of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans, incorporated in the device of Zhang, in order to change the medical images via sizing or re-slicing, which can help train predictive models and aid in making abnormalities more identifiable (as stated in Moore ¶ [24] and [25]). Re claim 5: However, Zhang fails to specifically teach the features of the computer-implemented method of claim 3, further comprising creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses performing pre-processing to a CT scan (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses further comprising creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window (e.g. the reference discloses windowing a brain, bone and subdural areas as channels, which is taught in col. 3, ll. 14-17.). (20) Further, for a given head CT scan, the scan is preprocessed by windowing to three separate windows, including brain window, bone window and subdural window, and stacking the windows as channels. Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of further comprising creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window, incorporated in the device of Zhang, as modified by Moore and Wang, in order to generate windowing of subdural, brain and bone areas, which can aid in differentiation between the skull and an extra axial bleed that might have been indistinguishable in a normal brain window (as stated in Chilamkurhy col. 9, ll. 55-col. 10, ll. 4). Re claim 8: Zhang discloses a computing device for prediction of hematoma expansion using machine learning techniques, comprising: a processor; and a memory in communication with the processor and storing instructions that, when read by the processor (e.g. the system contains a processor and memory that stores instructions that are executed by the processor, which is taught in ¶ [27].), [0027] a fourth aspect, the present invention provides an electronic device, comprising the non-transitory computer readable storage medium as described above; and one or more processors capable of executing the instructions of the non-transitory computer-readable storage medium. cause the computing device to: retrieve electronic data corresponding to one or more diagnostic scans of a patient (e.g. a CT image of a brain hematoma is obtained by the system, which is taught in ¶ [40] and [41] above.); preprocess the one or more diagnostic scans of the patient (e.g. pre-processing of data occurs in ¶ [52] above.); perform machine learning analysis based on machine learning model trained to classify hematoma expansion (e.g. the trained prediction model is trained to output a risk of the prediction result, which is taught in ¶ [42] and [43] above.); and predict, based on the machine learning analysis of the one or more diagnostic scans, a probability of hematoma expansion for the patient (e.g. the system discloses predicting a probability of whether hematoma expansion is predicted or is stable, which is taught in ¶ [55] and [56] above.). However, Zhang fails to specifically teach the features of retrieve, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocess the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans; perform machine learning analysis of the pre-processed one or more diagnostic scans; and predict, based on the machine learning analysis of the pre-processed one or more diagnostic scans. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses retrieve, from a data store, electronic data corresponding to one or more diagnostic scans of a patient (e.g. the invention discloses a data storage as a repository that stores scans from CT imaging devices, which is taught in ¶ [17] and [18].); [0017] Referring generally to FIG. 1, a block diagram depicting a medical imaging analysis system 100 configured in accordance with the present disclosure is shown. The medical imaging analysis system 100 may include one or more medical imaging devices 102 (e.g., X-ray radiography devices, ultrasound imaging devices, CT imaging devices, tomography PET devices, MRI devices, cardiac imaging devices, digital pathology devices, endoscopy devices, arthoscopy devices, medical digital photography devices, ophthalmology imaging devices or the like) in communication with an analyzer 104. The analyzer 104 may include one or more data storage devices 106 (e.g., magnetic storage devices, optical storage devices, solid-state storage devices, network-based storage devices or the like) configured to store images acquired by the medical imaging devices 102. [0018] The one or more data storage devices 106 may be further configured to serve as a data repository of historical data, which may form a large data set that may be referred to as “big data.” This large data set can be utilized to train one or more predictive models executed on one or more processing units 108 of the analyzer 104. The predictive model(s) trained in this manner may then be utilized to analyze images acquired by the medical imaging devices 102. For example, the analyzer 104 may be configured to recognize one or more features of interest in the images acquired by the medical imaging devices 102. The analyzer 104 may also be configured to determine whether the feature/features of interest represent abnormality/abnormalities. The analyzer 104 may be further configured to determine whether a feature of interest is a certain type of object (e.g., a hardware implant) inside a patient's body. Determinations made by the analyzer 104 may be recorded (e.g., into the data storage devices 106) and/or reported to a user (e.g., a radiologist, a doctor or the like) via one or more output devices 110-114. preprocess the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans (e.g. the invention discloses the preprocessing steps of resizing scans to be a certain size and re-slicing scans, which is taught in ¶ [24] and [25].). perform machine learning analysis of the pre-processed one or more diagnostic scans; and predict, based on the machine learning analysis of the pre-processed one or more diagnostic scans (e.g. the system discloses training the system to have the machine learning model predict a probability of an increase in the subdural hematoma when diagnostic scans are input, which is taught in ¶ [33].). Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieve, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocess the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans; perform machine learning analysis of the pre-processed one or more diagnostic scans; and predict, based on the machine learning analysis of the pre-processed one or more diagnostic scans, incorporated in the device of Zhang, in order to have a storage to store data reflecting data scans that are provided to a predictive model for analysis, which can reduce workloads of healthcare professionals and improve accuracy analysis (as stated in Moore ¶ [03]). However, the combination above fails to specifically teach the features of to re-slice the one or more diagnostic scans to a predetermined number of slices. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses to re-slice the one or more diagnostic scans to a predetermined number of slices (e.g. the invention discloses in preprocessing data in resizing and re-slicing the data. The data re-slice is uses three separate windows considered as the predetermined number of slices of a certain size, which is taught in col. 9, ll. 55-col. 10, ll. 4 above.). Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of to re-slice the one or more diagnostic scans to a predetermined number of slices, incorporated in the device of Zhang, as modified by Moore, in order to provide a number of re-slicing data to differentiate a hematoma from another injury, which can improve notification of critical findings when CT head scans are acquired (as stated in Chilamkurhy col. 3, ll. 55-col. 4, ll. 7). Re claim 9: However, Zhang fails to specifically teach the features of the computing device of claim 8, wherein the instructions, when executed by the processor, cause the computing device to resize the one or more diagnostic scans, re-slice the one or more diagnostic scans, or resize and re-slice the one or more diagnostic scans. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses wherein the instructions, when executed by the processor, cause the computing device to resize the one or more diagnostic scans, re-slice the one or more diagnostic scans, or resize and re-slice the one or more diagnostic scans (e.g. the invention discloses re-sizing the medical images and changing re-slicing parameters to perform re-slicing, which is taught in ¶ [24] above.). Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions, when executed by the processor, cause the computing device to resize the one or more diagnostic scans, re-slice the one or more diagnostic scans, or resize and re-slice the one or more diagnostic scans, incorporated in the device of Zhang, in order to change the medical images via sizing or re-slicing, which can help train predictive models and aid in making abnormalities more identifiable (as stated in Moore ¶ [24] and [25]). Re claim 12: However, Zhang fails to specifically teach the features of the computing device of claim 10, wherein the instructions, when executed by the processor, cause the computing device to create, based on pixel data and for each slice, a bone window, a brain window, and a subdural window. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses performing pre-processing to a CT scan (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses wherein the instructions, when executed by the processor, cause the computing device to create, based on pixel data and for each slice, a bone window, a brain window, and a subdural window (e.g. the reference discloses windowing a brain, bone and subdural areas as channels, which is taught in col. 3, ll. 14-17 above.). Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions, when executed by the processor, cause the computing device to create, based on pixel data and for each slice, a bone window, a brain window, and a subdural window, incorporated in the device of Zhang, as modified by Moore and Wang, in order to generate windowing of subdural, brain and bone areas, which can aid in differentiation between the skull and an extra axial bleed that might have been indistinguishable in a normal brain window (as stated in Chilamkurhy col. 9, ll. 55-col. 10, ll. 4). Re claim 15: Zhang discloses a non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising: retrieving electronic data corresponding to one or more diagnostic scans of a patient (e.g. a CT image of a brain hematoma is obtained by the system, which is taught in ¶ [40] and [41] above.); preprocessing the one or more diagnostic scans of the patient (e.g. pre-processing of data occurs in ¶ [52] above.); performing, by a machine learning computing system, machine learning analysis based on machine learning model trained to classify hematoma expansion (e.g. the trained prediction model is trained to output a risk of the prediction result, which is taught in ¶ [42] and [43] above.); and predicting, based on the machine learning analysis of the one or more diagnostic scans, a probability of hematoma expansion for the patient (e.g. the system discloses predicting a probability of whether hematoma expansion is predicted or is stable, which is taught in ¶ [55] and [56] above.). However, Zhang fails to specifically teach the features of retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans; performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis; and predicting, based on the machine learning analysis of the pre-processed one or more diagnostic scans. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient (e.g. the invention discloses a data storage as a repository that stores scans from CT imaging devices, which is taught in ¶ [17] and [18] above.); preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans (e.g. the invention discloses the preprocessing steps of resizing scans to be a certain size and re-slicing scans, which is taught in ¶ [24] and [25] above.). performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis; and predicting, based on the machine learning analysis of the pre-processed one or more diagnostic scans (e.g. the system discloses training the system to have the machine learning model predict a probability of an increase in the subdural hematoma when diagnostic scans are input, which is taught in ¶ [33] above.). Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieving, from a data store, electronic data corresponding to one or more diagnostic scans of a patient; preprocessing the one or more diagnostic scans of the patient to resize the one or more diagnostic scans to a predefined size and to re-slice the one or more diagnostic scans; performing, by a machine learning computing system of the pre-processed one or more diagnostic scans, machine learning analysis; and predicting, based on the machine learning analysis of the pre-processed one or more diagnostic scans, incorporated in the device of Zhang, in order to have a storage to store data reflecting data scans that are provided to a predictive model for analysis, which can reduce workloads of healthcare professionals and improve accuracy analysis (as stated in Moore ¶ [03]). However, the combination above fails to specifically teach the features of to re-slice the one or more diagnostic scans to a predetermined number of slices. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses to re-slice the one or more diagnostic scans to a predetermined number of slices (e.g. the invention discloses in preprocessing data in resizing and re-slicing the data. The data re-slice is uses three separate windows considered as the predetermined number of slices of a certain size, which is taught in col. 9, ll. 55-col. 10, ll. 4 above.). Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of to re-slice the one or more diagnostic scans to a predetermined number of slices, incorporated in the device of Zhang, as modified by Moore, in order to provide a number of re-slicing data to differentiate a hematoma from another injury, which can improve notification of critical findings when CT head scans are acquired (as stated in Chilamkurhy col. 3, ll. 55-col. 4, ll. 7). Re claim 16: However, Zhang fails to specifically teach the features of the non-transitory machine-readable medium of claim 15, wherein preprocessing the one or more diagnostic scans comprises one or both of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans. However, this is well known in the art as evidenced by Moore. Similar to the primary reference, Moore discloses analysis of a medical image to predict an increase in a subdural hematoma (same field of endeavor or reasonably pertinent to the problem). Moore discloses wherein preprocessing the one or more diagnostic scans comprises one or both of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans (e.g. the invention discloses re-sizing the medical images and changing re-slicing parameters to perform re-slicing, which is taught in ¶ [24] above.). Therefore, in view of Moore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein preprocessing the one or more diagnostic scans comprises one or both of resizing the one or more diagnostic scans and re-slicing the one or more diagnostic scans, incorporated in the device of Zhang, in order to change the medical images via sizing or re-slicing, which can help train predictive models and aid in making abnormalities more identifiable (as stated in Moore ¶ [24] and [25]). Re claim 19: However, Zhang fails to specifically teach the features of the non-transitory machine-readable medium of claim 17, cause the one or more processors to perform a step of creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window. However, this is well known in the art as evidenced by Chilamkurhy. Similar to the primary reference, Chilamkurhy discloses performing pre-processing to a CT scan (same field of endeavor or reasonably pertinent to the problem). Chilamkurhy discloses cause the one or more processors to perform a step of creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window (e.g. the reference discloses windowing a brain, bone and subdural areas as channels, which is taught in col. 3, ll. 14-17.). Therefore, in view of Chilamkurhy, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of cause the one or more processors to perform a step of creating, based on pixel data and for each slice, a bone window, a brain window, and a subdural window, incorporated in the device of Zhang, as modified by Moore and Wang, in order to generate windowing of subdural, brain and bone areas, which can aid in differentiation between the skull and an extra axial bleed that might have been indistinguishable in a normal brain window (as stated in Chilamkurhy col. 9, ll. 55-col. 10, ll. 4) . 07-22-aia AIA Claim (s) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, as modified by Moore and Chilamkurhy , as applied to claim s 1, 8 and 15 above, and further in view of Wang (CN 113706441 (Pub Date: 3/15/2021)) . Re claim 3: However, Zhang fails to specifically teach the features of the computer-implemented method of claim 1, further comprising providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient. However, this is well known in the art as evidenced by Wang. Similar to the primary reference, Wang discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Wang discloses further comprising providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient (e.g. the system discloses a map or mask being synthesized on an original natural image in order to create an image to convey the hematoma expansion probability. This is taught in ¶ [98] and [101].). [0098] Referring to FIG. 2, FIG. 2 is an application scene diagram of image prediction based on medical image in the embodiment of the present application, as shown in FIG. 2 (A), the patient A on 2021, 3, 6, 18, 48 minutes 50 seconds of a brain electronic computer tomography (Computed Tomography, CT) image, the brain CT image comprises a hematoma part. inputting the brain CT image into the trained area segmentation model; outputting the first mask image through the area segmentation model; then inputting the first mask image and the brain CT image to the area prediction model; outputting the second mask image through the area prediction model; the second mask image is covered on the original natural image, so as to obtain the synthetic image shown in FIG. 2 (B), visible, the future 24 hours the hematoma part will continue to expand. [0100] one, online image prediction; [0101] the image prediction system comprises a server and a terminal device, the terminal device collects the to-be-predicted image, then uploading the to-be-predicted image to the server, the server uses the trained area segmentation model and the area prediction model to process the to-be-predicted image; and then feeding back the processed mask image (or composite image) to the terminal device, displaying the mask image (or composite image) by the terminal device. Therefore, in view of Wang, it would have been obvious to one of ordinary before the effective filing date of the claimed invention was made to have the feature of further comprising providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to display the combined mask image showing the hematoma expansion, which contribute to the improve efficiency of the image analysis (as stated in Wang ¶ [79]). Re claim 10: However, Zhang fails to specifically teach the features of the computing device of claim 8, wherein the instructions, when executed by the processor, cause the computing device to provide, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient. However, this is well known in the art as evidenced by Wang. Similar to the primary reference, Wang discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Wang discloses wherein the instructions, when executed by the processor, cause the computing device to provide, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient (e.g. the system discloses a map or mask being synthesized on an original natural image in order to create an image to convey the hematoma expansion probability. This is taught in ¶ [98] and [101] above.). Therefore, in view of Wang, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions, when executed by the processor, cause the computing device to provide, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to display the combined mask image showing the hematoma expansion, which contribute to the improve efficiency of the image analysis (as stated in Wang ¶ [79]). Re claim 17: However, Zhang fails to specifically teach the features of the non-transitory machine-readable medium of claim 15, wherein the instructions further cause the one or more processors to perform a step of providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient. However, this is well known in the art as evidenced by Wang. Similar to the primary reference, Wang discloses determining hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Wang discloses wherein the instructions further cause the one or more processors to perform a step of providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient (e.g. the system discloses a map or mask being synthesized on an original natural image in order to create an image to convey the hematoma expansion probability. This is taught in ¶ [98] and [101] above.). Therefore, in view of Wang, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions further cause the one or more processors to perform a step of providing, via a display, the probability of hematoma expansion for the patient as a combination of a heat map and a diagnostic scan of the one or more diagnostic scans of the patient, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to display the combined mask image showing the hematoma expansion, which contribute to the improve efficiency of the image analysis (as stated in Wang ¶ [79]) . 07-22-aia AIA Claim (s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, as modified by Moore, Chilamkurhy and Wang , as applied to claim s 3, 10 and 17 above, and further in view of Pattnaik (IDS NPL titled “Predicting Tuberculosis Related Lunfg Deformities from CT Scan Images Using 3D CNN” dated 2019) . Re claim 4: However, Zhang fails to specifically teach the features of the computer-implemented method of claim 3, wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing. However, this is well known in the art as evidenced by Pattnaik. Similar to the primary reference, Pattnaik discloses performing pre-processing to CT scans (same field of endeavor or reasonably pertinent to the problem). Pattnaik discloses wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing (e.g. the reference on page 3 discloses the resizing of slices of the CT images. This is described in the Resizing section on page 3. On page 4 in the Concatenation section, in the last sentence in the third paragraph, the patient images who had less than 18 slices were dropped from being evaluated further for segmentation.). Therefore, in view of Pattnaik, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing, incorporated in the device of Zhang, as modified by Moore, Chilamkurhy and Wang, in order to drop the patient images for segmentation, which ensures that the patients evaluated have a constant number of slices per patient and reduce processing of images that are not with this predetermined amount (as stated in Pattnaik page 4). Re claim 11: However, Zhang fails to specifically teach the features of the computing device of claim 10, wherein the instructions, when executed by the processor, cause the computing device to discard a diagnostic scan with less than 18 slices after re-sizing. However, this is well known in the art as evidenced by Pattnaik. Similar to the primary reference, Pattnaik discloses performing pre-processing to CT scans (same field of endeavor or reasonably pertinent to the problem). Pattnaik discloses wherein the instructions, when executed by the processor, cause the computing device to discard a diagnostic scan with less than 18 slices after re-sizing (e.g. the reference on page 3 discloses the resizing of slices of the CT images. This is described in the Resizing section on page 3. On page 4 in the Concatenation section, in the last sentence in the third paragraph, the patient images who had less than 18 slices were dropped from being evaluated further for segmentation.). Therefore, in view of Pattnaik, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions, when executed by the processor, cause the computing device to discard a diagnostic scan with less than 18 slices after re-sizing, incorporated in the device of Zhang, as modified by Moore, Chilamkurhy and Wang, in order to drop the patient images for segmentation, which ensures that the patients evaluated have a constant number of slices per patient and reduce processing of images that are not with this predetermined amount (as stated in Pattnaik page 4). Re claim 18: However, Zhang fails to specifically teach the features of the non-transitory machine-readable medium of claim 17, wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing. However, this is well known in the art as evidenced by Pattnaik. Similar to the primary reference, Pattnaik discloses performing pre-processing to CT scans (same field of endeavor or reasonably pertinent to the problem). Pattnaik discloses wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing (e.g. the reference on page 3 discloses the resizing of slices of the CT images. This is described in the Resizing section on page 3. On page 4 in the Concatenation section, in the last sentence in the third paragraph, the patient images who had less than 18 slices were dropped from being evaluated further for segmentation.). Therefore, in view of Pattnaik, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein preprocessing the one or more diagnostic scans comprises discarding a diagnostic scan with less than 18 slices after re-sizing, incorporated in the device of Zhang, as modified by Moore, Chilamkurhy and Wang, in order to drop the patient images for segmentation, which ensures that the patients evaluated have a constant number of slices per patient and reduce processing of images that are not with this predetermined amount (as stated in Pattnaik page 4) . 07-22-aia AIA Claim (s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, as modified by Moore and Chilamkurhy , as applied to claim s 1, 8 and 15 above, and further in view of Chen (IDS reference Chen et al. titled ”Predictors of hematoma expansion predictors after intracerebral hemorrhage” Dated: 3/24/2017.) . Re claim 6: However, Zhang fails to specifically teach the features of the computer-implemented method of claim 1, wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL. However, this is well known in the art as evidenced by Chen. Similar to the primary reference, Chen discloses creating a threshold for hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chen discloses wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL (e.g. the NPL discloses determining optional Hematoma Expansion as greater than or equal to 3 ml, which is taught on page 2.). Therefore, in view of Chen, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to classify hematoma expansion using a specific expansion cutoff, which can aid in maximizing specificity and sensitivity when detecting hematoma expansion (as stated in Chen page 2). Re claim 13: However, Zhang fails to specifically teach the features of the computing device of claim 8, wherein the instructions, when executed by the processor, cause the computing device to classify mages as likely to have subsequent hematoma expansion greater than or equal to 3 mL. However, this is well known in the art as evidenced by Chen. Similar to the primary reference, Chen discloses creating a threshold for hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chen discloses wherein the instructions, when executed by the processor, cause the computing device to classify mages as likely to have subsequent hematoma expansion greater than or equal to 3 mL (e.g. the NPL discloses determining optional Hematoma Expansion as greater than or equal to 3 ml, which is taught on page 2.). Therefore, in view of Chen, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the instructions, when executed by the processor, cause the computing device to classify mages as likely to have subsequent hematoma expansion greater than or equal to 3 mL, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to classify hematoma expansion using a specific expansion cutoff, which can aid in maximizing specificity and sensitivity when detecting hematoma expansion (as stated in Chen page 2). Re claim 20: However, Zhang fails to specifically teach the features of the non-transitory machine-readable medium of claim 15, wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL. However, this is well known in the art as evidenced by Chen. Similar to the primary reference, Chen discloses creating a threshold for hematoma expansion (same field of endeavor or reasonably pertinent to the problem). Chen discloses wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL (e.g. the NPL discloses determining optional Hematoma Expansion as greater than or equal to 3 ml, which is taught on page 2.). Therefore, in view of Chen, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the predicting of the probability of hematoma expansion for the patient comprises classifying images as likely to have subsequent hematoma expansion greater than or equal to 3 mL, incorporated in the device of Zhang, as modified by Moore and Chilamkurhy, in order to classify hematoma expansion using a specific expansion cutoff, which can aid in maximizing specificity and sensitivity when detecting hematoma expansion (as stated in Chen page 2) . 07-22-aia AIA Claim (s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, as modified by Moore and Chilamkurhy , as applied to claim s 1 and 8 above, and further in view of Hassanpour (US Pub 2022/0148164 filing date: 2/21/2020) . Re claim 7: Zhang discloses the computer-implemented method of claim 1, wherein the machine learning computing system provides a prediction of hematoma expansion (e.g. the system discloses predicting a probability of whether hematoma expansion is predicted or is stable, which is taught in ¶ [55] and [56] above.). However, Zhang fails to specifically teach the features of in less than 0.5 seconds. However, this is well known in the art as evidenced by Hassanpour. Similar to the primary reference, Hassanpour discloses processing time for classification (same field of endeavor or reasonably pertinent to the problem). Hassanpour discloses in less than 0.5 seconds (e.g. a deep neural network framework is used to process a CT scan in 0.02 seconds, which is taught in ¶ [37]. Incorporating this speed with the function of the primary reference would perform the feature of the claims.). [0037] It should be clear that the above-described system and method and associated analytic techniques provides a highly useful and robust technique for identifying and diagnosing OVFs (and other related orthopedic conditions) in patients who have undergone CT and related imagery. This system and method, unlike prior art implementations, advantageously trains and operates the entire system over a unified deep neural network framework, and makes the final diagnosis on CT volumes from the patient's imagery, rather than basing a diagnosis on single CT slices, or small slice patches. Using whole CT volumes for diagnosis is particularly instrumental in cases of patients with deformed spines (e.g., scoliosis). Of note, analysis of each CT scan in the best of the aforementioned models took approximately five minutes to process on a high-performance computer, while the exemplary system and method reduces this time to less than 0.02 second on average for the full analysis of a CT scan, which does not stall the clinical workflow. Therefore, in contrast to the previous methods, the illustrative holistic, deep learning approach of the system and method presents a fast, efficient, and accurate diagnostic tool in this domain. Hence, the exemplary, automatic detection system and method herein can potentially reduce the time and the manual burden on radiologists for OVF screening, as well as reducing the potential false negative errors arising in asymptomatic early stage vertebral fracture diagnoses. This can help to provide better early detection and treatment of osteoporosis, leading to a decrease in the overall socio-economic burden of osteoporosis, and a significant improvement in associated health outcomes. Moreover, this system and method can provide a platform to improve the quality of care for rural, small, and poor communities worldwide, where access to radiology expertise is limited. Therefore, in view of Hassanpour, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of in less than 0.5 seconds, incorporated in the device of Zhang, as modified by Moore, in order to perform the a diagnoses in less than 0.5 seconds, which provide a fast, efficient and accurate diagnosis (as stated in Hassanpour ¶ [37]). Re claim 14: Zhang discloses the computing device of claim 8, wherein a prediction of hematoma expansion is provided (e.g. the system discloses predicting a probability of whether hematoma expansion is predicted or is stable, which is taught in ¶ [55] and [56] above.). However, Zhang fails to specifically teach the features of in less than 0.5 seconds. However, this is well known in the art as evidenced by Hassanpour. Similar to the primary reference, Hassanpour discloses processing time for classification (same field of endeavor or reasonably pertinent to the problem). Hassanpour discloses in less than 0.5 seconds (e.g. a deep neural network framework is used to process a CT scan in 0.02 seconds, which is taught in ¶ [37] above. Incorporating this speed with the function of the primary reference would perform the feature of the claims.). Therefore, in view of Hassanpour, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of in less than 0.5 seconds, incorporated in the device of Zhang, as modified by Moore, in order to perform the a diagnoses in less than 0.5 seconds, which provide a fast, efficient and accurate diagnosis (as stated in Hassanpour ¶ [37]) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Huang discloses predicting hematoma expansion. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD S DICKERSON whose telephone number is (571)270-1351. The examiner can normally be reached Monday-Friday 10AM-6PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abderrahim Merouan can be reached at 571-270-5254 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHAD DICKERSON/ Primary Examiner, Art Unit 2682 Application/Control Number: 18/569,070 Page 2 Art Unit: 2683 Application/Control Number: 18/569,070 Page 3 Art Unit: 2683 Application/Control Number: 18/569,070 Page 4 Art Unit: 2683 Application/Control Number: 18/569,070 Page 5 Art Unit: 2683 Application/Control Number: 18/569,070 Page 6 Art Unit: 2683 Application/Control Number: 18/569,070 Page 7 Art Unit: 2683 Application/Control Number: 18/569,070 Page 8 Art Unit: 2683 Application/Control Number: 18/569,070 Page 9 Art Unit: 2683 Application/Control Number: 18/569,070 Page 10 Art Unit: 2683 Application/Control Number: 18/569,070 Page 11 Art Unit: 2683 Application/Control Number: 18/569,070 Page 12 Art Unit: 2683 Application/Control Number: 18/569,070 Page 13 Art Unit: 2683 Application/Control Number: 18/569,070 Page 14 Art Unit: 2683 Application/Control Number: 18/569,070 Page 15 Art Unit: 2683 Application/Control Number: 18/569,070 Page 16 Art Unit: 2683 Application/Control Number: 18/569,070 Page 17 Art Unit: 2683 Application/Control Number: 18/569,070 Page 18 Art Unit: 2683 Application/Control Number: 18/569,070 Page 19 Art Unit: 2683 Application/Control Number: 18/569,070 Page 20 Art Unit: 2683 Application/Control Number: 18/569,070 Page 21 Art Unit: 2683 Application/Control Number: 18/569,070 Page 22 Art Unit: 2683 Application/Control Number: 18/569,070 Page 23 Art Unit: 2683 Application/Control Number: 18/569,070 Page 24 Art Unit: 2683 Application/Control Number: 18/569,070 Page 25 Art Unit: 2683 Application/Control Number: 18/569,070 Page 26 Art Unit: 2683 Application/Control Number: 18/569,070 Page 27 Art Unit: 2683 Application/Control Number: 18/569,070 Page 28 Art Unit: 2683 Application/Control Number: 18/569,070 Page 29 Art Unit: 2683 Application/Control Number: 18/569,070 Page 30 Art Unit: 2683 Application/Control Number: 18/569,070 Page 31 Art Unit: 2683 Application/Control Number: 18/569,070 Page 32 Art Unit: 2683 Application/Control Number: 18/569,070 Page 33 Art Unit: 2683 Application/Control Number: 18/569,070 Page 34 Art Unit: 2683 Application/Control Number: 18/569,070 Page 35 Art Unit: 2683 Application/Control Number: 18/569,070 Page 36 Art Unit: 2683
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