DETAILED ACTION
This Office Action is sent in response to the Applicant’s Communication received on 02/01/2023 for application number 18/019,115. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, IDS, and Claims.
Claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10 are directed to a method and are therefore directed towards one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1:
Claim 1 recites:
“determining if the property of the at least one input is incorrectly identified;” Determining if the property of the at least one input is incorrectly identified is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“A method comprising: developing an artificial intelligence (AI) application including at least one model, the at least one model identifies a property of at least one input captured by at least one sensor;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
“providing feedback training data in relation to the incorrectly identified property of at least one input to the at least one model;” This limitation is merely a post-solution step of storing the data—a nominal addition to the claim that does not meaningfully limit the claim. The method storing is recited at a high level of generality. Simply implementing the abstract idea in a generic method is not a practical application of the abstract idea. Therefore, storing step is an insignificant extra-solution activity. See MPEP 2106.05(g).
“retraining the at least one model with the feedback training data; and generating an improved version of the at least one model;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“A method comprising: developing an artificial intelligence (AI) application including at least one model, the at least one model identifies a property of at least one input captured by at least one sensor;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
“providing feedback training data in relation to the incorrectly identified property of at least one input to the at least one model;” These elements amount to storing… information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; See MPEP 2106.05(d) (II)(iv). The courts have recognized the computer functions of storing as well‐understood, routine, and conventional function when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
“retraining the at least one model with the feedback training data; and generating an improved version of the at least one model;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 2
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“iteratively performing the determining, providing, retraining and generating until a performance value of the improved version of the at least one model is greater than a predetermined threshold;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“iteratively performing the determining, providing, retraining and generating until a performance value of the improved version of the at least one model is greater than a predetermined threshold;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one input is at least one of an image, a sound and/or a video;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the at least one input is at least one of an image, a sound and/or a video;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the performance value is a classification accuracy value, logarithmic loss value, confusion matrix, area under curve value, F1 score, mean absolute error, mean squared error, mean average precision value, a recall value and/or, a specificity value;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the performance value is a classification accuracy value, logarithmic loss value, confusion matrix, area under curve value, F1 score, mean absolute error, mean squared error, mean average precision value, a recall value and/or, a specificity value;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“capturing the feedback training data with the at least one sensor coupled to a mobile device;” Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“capturing the feedback training data with the at least one sensor coupled to a mobile device;” Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). The additional element of “receiving” does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving steps amounts to no more than mere data gathering. This element amounts to receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II (i). This cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one sensor includes at least one of a camera, a microphone, a temperature sensor, a humidity sensor, an accelerometer and/or a gas sensor;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the at least one sensor includes at least one of a camera, a microphone, a temperature sensor, a humidity sensor, an accelerometer and/or a gas sensor;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 2A Prong 1:
Claim 7 recites:
“determining a confidence score for an output of the at least one model and, if the determined confidence score is below a predetermined threshold, prompting a user to capture and label data related to the at least one input;” Determining a confidence score for an output of the at least one model and, if the determined confidence score is below a predetermined threshold, prompting a user to capture and label data related to the at least one input are actions that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two and Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 8
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“presenting at least one of a saliency map, an attention map and/or an output of a Bayesian deep learning;” Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“presenting at least one of a saliency map, an attention map and/or an output of a Bayesian deep learning;” Insignificant extra-solution as the limitation amounts to necessary data outputting (MPEP 2106.05(g)(3)). This falls under Well-Understood, Routine, Conventional activity -see MPEP 2106.05(d)(II)(vi).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 2A Prong 1:
Claim 9 recites:
“analyzing an output of the at least one model;” Analyzing an output of the at least one model is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the output of the at least one model includes at least one of a classification and/or a regression value;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the output of the at least one model includes at least one of a classification and/or a regression value;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 2A Prong 1:
Claim 10 recites:
“enabling at least one first user to invite at least one second user to capture and label data related to the at least one input;” Enabling at least one first user to invite at least one second user to capture and label data related to the at least one input is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two and Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
In regards to claims 11-14 and 17-20, the claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claimed “system” does not demonstrate any structural recitations. Claims 11-14 and 17-20 recite “a system” comprising only software components such as “a machine learning system” and “a feedback module” and do not comprise any physical or tangible structure. Under the Broadest Reasonable Interpretation (BRI), the system could be construed as being software per se. MPEP 2106.03 states: Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations.
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 nonobviousness.
Claim(s) 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Maughan et al. (US 20170372232 A1), hereinafter Maughan, in view of Anorga et al. (US 10891485 B2), hereinafter Anorga.
Regarding claim 1, Maughan teaches,
A method [Abstract, methods… are disclosed] comprising: developing an artificial intelligence (AI) application including at least one model [Abstract, A predictive analytics module creates a machine learning model], the at least one model identifies a property of at least one input [Para 0049, the quality analysis module 202, may electronically identify one or more data quality issues in machine learning training data];
determining if the property of the at least one input is incorrectly identified [Para 0050, a data quality issue may include a unique id feature, a date feature, a categorical feature for which a cardinality violates a threshold, a feature with missing values, a feature with out-of-range values, or the like];
providing feedback training data in relation to the incorrectly identified property of at least one input to the at least one model [Para 0054, the corrective action module 204 may perform a corrective action on the training data by: excluding a feature from the training data, excluding an observation from the training data, excluding one or more values from the training data, replacing one or more values in the training data, adding one or more engineered features to the training data, and/or the like; Para 0055, the corrective action module 204 may modify training data by performing corrective actions in response to data quality issues in the training data, and may replicate the one or more corrective actions to modify workload data using the same corrective actions that were used to modify the training data];
retraining the at least one model with the feedback training data [Para 0070, the predictive analytics module 206 may update or retrain a machine learning model]; and
generating an improved version of the at least one model [Para 0060, tailoring corrective actions 204 to particular machine learning algorithms may increase the accuracy or efficiency of machine learning models generated by the predictive analytics module 206].
Maughan does not teach input captured by at least one sensor.
Anorga teaches,
input captured by at least one sensor [Col 43, lines 1-3, The I/O interface 906 can interface to other input and output devices. Some examples include one or more cameras which can capture images].
Anorga is analogous to the claimed invention as they both relate to image data processing. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Anorga and provide a sensor to capture input images in order to apply machine learning capabilities to image data for advanced decision making.
Regarding claim 2, Maughan-Anorga teach the limitations of claim 1.
Maughan further teaches,
iteratively performing the determining, providing, retraining and generating until a performance value of the improved version of the at least one model is greater than a predetermined threshold [Para 0053, the corrective action module 204 may perform corrective actions for some data quality issues, but not for others, based on whether issues satisfy a threshold for correction; Para 0070, the predictive analytics module 206 may update or retrain a machine learning model].
Regarding claim 3, Maughan-Anorga teach the limitations of claim 1.
Anorga further teaches,
wherein the at least one input is at least one of an image, a sound and/or a video Col 43, lines 1-3, The I/O interface 906 can interface to other input and output devices. Some examples include one or more cameras which can capture images].
Anorga is analogous to the claimed invention as they both relate to image data processing. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Anorga and provide a sensor to capture input images in order to apply machine learning capabilities to image data for advanced decision making.
Regarding claim 4, Maughan-Anorga teach the limitations of claim 2.
Maughan further teaches,
wherein the performance value is a classification accuracy value, logarithmic loss value, confusion matrix, area under curve value, F1 score, mean absolute error, mean squared error, mean average precision value, a recall value and/or, a specificity value [Para 0044, the predictive analytics module 206 may build learned functions for a machine learning model based on the training set, and test the accuracy of the learned functions against the holdout set; Para 0105, The predictive analytics module 206, in one embodiment, may retrain machine learning excluding one or more feature and retrain machine learning replacing drifted, changed, and/or missing values with expected values, comparing and/or evaluating predictions or other results from both and selecting the most accurate retrained machine learning for use, or the like].
Regarding claim 5, Maughan-Anorga teach the limitations of claim 1 including the providing feedback training data (Maughan, paras 0054-0055).
Maughan does not teach wherein training data includes capturing data with the at least one sensor coupled to a mobile device.
Anorga teaches,
wherein training data includes capturing data with the at least one sensor coupled to a mobile device [Col 37, lines 23-26, a client/server architecture can be used, e.g., a mobile computing device (as a client device) sends user input data to a server device and receives from the server the final output data for output (e.g., for display); Col 43, lines 1-3, The I/O interface 906 can interface to other input and output devices. Some examples include one or more cameras which can capture images].
Anorga is analogous to the claimed invention as they both relate to image data processing. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Anorga and provide a sensor to capture input images in order to apply machine learning capabilities to image data for advanced decision making.
Regarding claim 6, Maughan-Anorga teach the limitations of claim 5.
Anorga further teaches,
wherein the at least one sensor includes at least one of a camera, a microphone, a temperature sensor, a humidity sensor, an accelerometer and/or a gas sensor [Col 43, lines 1-5, The I/O interface 906 can interface to other input and output devices. Some examples include one or more cameras which can capture images. Some implementations can provide a microphone for capturing sound (e.g., as a part of captured images, voice commands, etc.)].
Anorga is analogous to the claimed invention as they both relate to image data processing. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Anorga and provide a sensor to capture input images in order to apply machine learning capabilities to image data for advanced decision making.
Regarding claim 7, Maughan-Anorga teach the limitations of claim 1 the determining if the property of the at least one input is incorrectly identified (Maughan, para 0050).
Maughan further teaches,
determining a confidence score for an output of the at least one model and, if the determined confidence score is below a predetermined threshold, prompting a user to capture and label data related to the at least one input [Para 0094, the quality analysis module 202 may monitor and/or analyze confidence metrics from the machine learning model to detect drift; Para 0097, the quality analysis module 202 may set a drift flag or other indicator in a response (e.g., with or without a prediction or other result); send a user a text, email, push notification, pop-up dialogue, and/or another message (e.g., within a graphical user interface (GUI) of the predictive analytics apparatus 102 or the like); and/or may otherwise notify a user or other client of a drift or other change; Para 0053, A corrective action may be based on, or in response to, a data quality issue if the corrective action corrects, improves, or otherwise affects the data quality issue… the corrective action module 204 may perform one or more corrective actions for each identified problem, potential problem, or other data quality issue… the corrective action module 204 may perform corrective actions for some data quality issues, but not for others, based on whether issues satisfy a threshold for correction, based on user input regarding how to respond to issues, or the like].
Regarding claim 8, Maughan-Anorga teach the limitations of claim 7 including the determining if the property of the at least one input is incorrectly identified (Claim 1: Maughan, para 0050).
Anorga further teaches,
presenting at least one of a saliency map [Col 31, lines 36-57, FIG. 5B is a diagrammatic illustration of another example user interface 530 that includes an image with a plurality of default actions and suggested actions, according to some implementations. User interface 530 includes an image 531, e.g., a photo captured using a camera… in FIG. 5B, it may be determined that the portion inside circle 534 is a salient portion of the image, e.g., from which information is extracted based on image analysis. Further, highlighting may be based on determining a category for the image, e.g., book cover. While FIG. 5B shows a single highlighted portion, some images may be displayed with multiple highlighted portions, e.g., an image that includes three books may include three highlighted portions], an attention map and/or an output of a Bayesian deep learning.
Anorga is analogous to the claimed invention as they both relate to image data processing. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Anorga and provide a saliency map in order to improve model performance by enhancing interpretability and transparency.
Regarding claim 9, Maughan-Anorga teach the limitations of claim 1 including the determining if the property of the at least one input is incorrectly identified (Maughan, para 0050).
Maughan further teaches,
analyzing an output of the at least one model, wherein the output of the at least one model includes at least one of a classification and/or a regression value [Para 0090, The quality analysis module 202 may monitor one or more inputs (e.g., client data, initialization data, training data, test data, workload data, labeled data, unlabeled data, or the like) and/or outputs (e.g., predictions or other results) of the predictive analytics module 206, to detect one or more changes (e.g., drifting) in the one or more inputs and/or outputs; Para 0034, predictive models may be constructed to solve at least two general problem types: Regression and Classification.].
Regarding claim 11, Maughan further teaches,
A system [Abstract, systems… are disclosed] comprising: a machine learning system that develops an artificial intelligence (Al) application including at least one model, the at least one model identifies a property of at least one input [Para 0049, the quality analysis module 202, may electronically identify one or more data quality issues in machine learning training data]; and
a feedback module that determines if the property of the at least one input is incorrectly identified and provides feedback training data in relation to the incorrectly identified property of at least one input to the at least one model [Para 0050, a data quality issue may include a unique id feature, a date feature, a categorical feature for which a cardinality violates a threshold, a feature with missing values, a feature with out-of-range values, or the like; Para 0054, the corrective action module 204 may perform a corrective action on the training data by: excluding a feature from the training data, excluding an observation from the training data, excluding one or more values from the training data, replacing one or more values in the training data, adding one or more engineered features to the training data, and/or the like; Para 0055, the corrective action module 204 may modify training data by performing corrective actions in response to data quality issues in the training data, and may replicate the one or more corrective actions to modify workload data using the same corrective actions that were used to modify the training data];
wherein the machine learning system retrains the at least one model with the feedback training data and generates an improved version of the at least one model [Para 0070, the predictive analytics module 206 may update or retrain a machine learning model; Para 0060, tailoring corrective actions 204 to particular machine learning algorithms may increase the accuracy or efficiency of machine learning models generated by the predictive analytics module 206].
Claims 12-19 are systems claims that recite similar limitations to method claims 2-9. Therefore, claims 12-19 are rejected using the same rationale as claim 2-9.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maughan in view of Anorga, and in further view of Reiner (US 20070237378 A1), hereinafter Reiner.
Regarding claim 10, Maughan-Anorga teach the limitations of claim 1 including the providing feedback training data (Maughan, para 0054) and the at least one input (Anorga, Col 43, lines 1-3).
Maughan-Anorga do not teach enabling at least one first user to invite at least one second user to capture and label data related to input.
Reiner teaches,
enabling at least one first user to invite at least one second user (Para 0070, the technologist may so order, and the imaging dataset would be transferred to the MPR workstation… processing/reconstructions would be performed by a specialist) to capture (Para 0075, pull up a previous image, then either redraw the appropriate gestures, symbols, or annotations, or re-propagate gestures, symbols, or annotations from the prior study) and label (Para 0073, The radiologist will make his/her own GBR annotations) data related to input [Para 0070, After imaging review and annotation are completed by the technologist, the technologist may make a determination as to whether specialized image processing is required in step 210. If additional image processing is required, the technologist may so order, and the imaging dataset would be transferred to the MPR workstation by the program 10 in step 211. The image processing/reconstructions would be performed by a specialist, who would insert annotations using GBR symbols, onto the images in step 212. The completed imaging dataset (with annotations) would be then transferred to the PACS 30 for interpretation by the radiologist, in step 213; Para 0073, Assuming the GBR is "On", the radiologist would review the images with the annotations made by the technologist, on the PACS workstation 30. The radiologist will make his/her own GBR annotations on "key images" in his/her review process; Para 0075, if the radiologist is correlating with a historical comparison study and notices some previously reported findings remain, he/she can pull up a previous image, then either redraw the appropriate gestures, symbols, or annotations, or re-propagate gestures, symbols, or annotations from the prior study by dragging them over to the new study using the (programmable) stylus 104. Thus, the program 110 will allow the movement of gestures, symbols, or annotations from one image to another, in a similar fashion to a "copy" or "cut and paste" function in word processing].
Reiner is analogous to the claimed invention as they both relate to supervised training methodologies for machine learning. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maughan’s teachings to incorporate the teachings of Reiner and provide one user to allow input from another user in order to [Reiner, para 0070] allow for experts of varying fields to improve annotated data.
Claim 20 is a system claim that recites similar limitations to the method claim 10. Therefore, claim 20 is rejected using the same rationale as claim 10.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED RAYHAN AHMED whose telephone number is (571)270-0286. The examiner can normally be reached Mon-Fri ET.
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, David Yi can be reached at (571) 270-7519. 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.
/SYED RAYHAN AHMED/Examiner, Art Unit 2126
/VAN C MANG/Primary Examiner, Art Unit 2126