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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a computer program product which does not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se. The claims do not comprise of a product that has a physical or tangible form and also does not include any structural recitations, thus, the computer program product of claims 15-20 is directed to software per se and the claim is directed to non-statutory embodiments that are not eligible for patent protection. See MPEP 2106.03(l), “As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a ”means plus function” limitation) has no physical or tangible form, and thus does not fall within any statutory category”
Claims 15-20 are further rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a computer program product comprising one or more computer readable storage media, which does not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to signals per se. Applicant’s specification para. 14 discloses “A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing.” Although applicant states the computer readable storage medium is not to be construed as a storage in the form of transitory signals per se, the claim language “computer readable storage media” encompasses transitory forms of signal transmission. See MPEP 2106.03(II), “a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).”
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claim 1 is drawn to a method (i.e., process) and Claim 8 is drawn to a system, which are all within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories). Although Claim 15 is indicated as directed to software per se and signals ser se, claim 15 includes similar limitations to claims 1 and 8 and the 101 analysis applies to claim 15 as well.
Step 2A analysis – Prong One:
Claim 1 recites:
A method of assessing a medical procedure comprising:
receiving input data corresponding to a patient having a medical procedure;
determining a plurality of predicted user actions to be performed during the medical procedure;
comparing a plurality of actual user actions performed during the medical procedure to the plurality of predicted user actions to identify one or more deviations in the medical procedure; and
alerting a user to the one or more deviations.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person gathering data corresponding to a patient, determining actions to be performed by a user during a medical procedure, and notifying a person when a deviation occurs in the procedure which encompasses a person interacting with another individual including following rules or instructions. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Determining predicted user actions and comparing the predicted actions to actual actions to identify a deviation can all be performed in the human mind, with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Claims 8 and 15 recite/describe nearly identical steps as claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claim 1 does not recite any additional elements beyond the abstract idea. Independent claims 8 and 15 recite the following additional elements beyond the abstract idea: one or more memories and at least one processor. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the processor may be of any type now known or to be developed in the future (see Applicant’s specification para. 17) and the memory is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM (see specification para. 20).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 8, and 15 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 8, and 15 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The applicant’s specification discloses: the processor may be of any type now known or to be developed in the future (see Applicant’s specification para. 17) and the memory is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM (see specification para. 20).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for assessing a medical procedure amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-7, 9-14, and 16-20 are directed towards elements used to describe the medical procedures and user actions being performed. These elements include extracting user actions from a log or image data, and the medical procedure being a radiological image analysis procedure. These elements describe managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the same scope of certain methods of organizing human activity as the independent claims.
The elements as recited above also falls within the “mental processes” grouping of abstract
ideas, and describes concepts that can be performed in the human mind through observation,
evaluation, judgement, and opinion. Extracting data from a log or an image and performing an image analysis as the medical procedure are all tasks that can be performed in the human mind. Therefore, the dependent claims recite an abstract idea of a mental process.
This judicial exception is not integrated into a practical application. Specifically, the dependent
claims recite the following additional elements beyond the abstract idea: a deep learning clustering model and a deep learning classification model. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the clustering model may be k-means clustering algorithm, a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, a gaussian mixture model algorithm, a balance iterative reducing and clustering using hierarchies (BIRCH) algorithm, an affinity propagation clustering algorithm, a means-shift clustering algorithm, an ordering points to identify the clustering structure (OPTICS) algorithm, an agglomerative hierarchy clustering algorithm, or another conventional or other clustering algorithm (see specification para. 53). The classification model may be a logistic regression model, a decision tree model, a random forest model, a support vector machine, a k-nearest neighbor model, a naive bayes classifier, or any other conventional or other deep learning model (see specification para. 58).
The additional elements do not show an improvement to the functioning of a computer or to
any other technology, rather the additional elements perform general computing functions and do not
indicate how the particular combination improves any technology or provides a technical solution to a
technical problem. Accordingly, these additional elements, when considered separately and as an
ordered combination, do not integrate the abstract idea into a practical application because they do not
impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are
directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional
elements are not integrated into a practical application).
As discussed above, the identified additional elements in the dependent claims are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The use of a computer or processor to merely automate or implement the abstract idea cannot
provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the
additional limitations alone or in combination improves the functioning of a computer or any other
technology, improves another technology or technical field, or effects a transformation or reduction of a
particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of
additional claims elements is/are sufficient to ensure the claims amount to significantly more than the
abstract idea identified above (Step 2B: Dependent claims - NO).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Bernard et al. (US Patent No. US 10140421 B1) (hereinafter Bernard).
Regarding Claim 1, Bernard teaches the following:
A method of assessing a medical procedure comprising (Col. 87, lines 14-15: a method for execution by a medical scan diagnosing system):
receiving input data corresponding to a patient having a medical procedure (Col. 57, lines 22-24: The first medical scan is transmitted to a first client device associated with a user of the medical scan diagnosing system);
determining a plurality of predicted user actions to be performed during the medical procedure (Col. 57, lines 7-13, 35-37, 40-44: inference data for given medical scans used to generate and/or update diagnosis data or other corresponding data of the medical scan entry. The inference data may include medical scan inference functions);
comparing a plurality of actual user actions performed during the medical procedure to the plurality of predicted user actions to identify one or more deviations in the medical procedure (Col. 61, lines 40-45 and Col. 67, lines 20-22: Inference accuracy data can be based on a determined discrepancy determined by performing the inference data evaluation function. The inference accuracy data can be based on a magnitude of the difference between the inference data and the expert feedback data, and can be mapped to the medical scan. Received annotations are compared to corresponding known or later provided diagnosis data or other corresponding data of each medical scan.); and
alerting a user to the one or more deviations (Col. 46, lines 26-32, and Col. 59, line 67-Col. 60, lines 1-3: information can be used by the medical scan report labeling system or other subsystem to alert a medical professional and/or hospital associated with the medical report, for example, indicated in the originating entity data or annotation author data of the error, for example, by transmitting a notification to a client device of a corresponding user. The system can transmit an alert and/or an automatically generated inferred scan category to the medical entity indicating that the scan is incorrectly classified in the scan classifier data or other metadata.).
Regarding Claim 2, Bernard teaches the method of claim 1, and further teaches:
The method of claim 1, wherein a deep learning clustering model is trained to determine the plurality of predicted user actions based on the input data (Col. 77, lines 54-61: the learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 3, Bernard teaches the method of claim 2, and further teaches:
The method of claim 2, further comprising: updating the deep learning clustering model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 4, Bernard teaches the method of claim 1, and further teaches:
The method of claim 1, wherein a deep learning classification model is trained to identify the one or more deviations (Col. 88, lines 12-15, Col. 77, lines 54-61: The first medical scan inference function is selected based on the new medical scan classifier in response to determining that the first medical scan classifier compares unfavorably to the new medical scan classifier. Thus, the inference function identifies a deviation. The learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 5, Bernard teaches the method of claim 4, and further teaches:
The method of claim 4, further comprising: updating the deep learning classification model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 6, Bernard teaches the method of claim 1, and further teaches:
The method of claim 1, wherein the plurality of actual user actions are extracted from one or more from a group of: a log generated by a medical device, and image data of the medical procedure (Col. 15, lines 18-26, 34-37: Usage data can include contribution usage data, which can include a listing of, or aggregate data associated with, usages of one or more subsystems by the user, for example, where the user is generating and/or otherwise providing data and/or feedback that can is utilized by the subsystems, for example, to generate, edit, and/or confirm diagnosis data and/or to otherwise populate, modify, or confirm portions of the medical scan database or other subsystem data. The usage data can indicate one or more specific attributes of a medical scan entry that a user utilized and/or contributed to , and/or a log of the user input generated by a client device of the user).
Regarding Claim 7, Bernard teaches the method of claim 1, and further teaches:
The method of claim 1, wherein the medical procedure is a radiological image analysis procedure (Col. 5, lines 28-31: The method involves a medical scan including imaging data corresponding to a CT scan, x-ray, or any other type of radiological scan or medical scan).
Regarding Claim 8, Bernard teaches the following:
A system for assessing a medical procedure (Col. 2, line 58: a medical scan processing system) comprising:
one or more memories (Col. 3, lines 19-20: one or more memory devices of one or more subsystems);
at least one processor coupled to the one or more memories, wherein the at least one processor (Col. 72, lines 31-34: medical scan image analysis system can include a processing system that includes a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations) is configured to:
receive input data corresponding to a patient having a medical procedure (Col. 57, lines 22-24: The first medical scan is transmitted to a first client device associated with a user of the medical scan diagnosing system);
determine a plurality of predicted user actions to be performed during the medical procedure (Col. 57, lines 7-13, 35-37, 40-44: inference data for given medical scans used to generate and/or update diagnosis data or other corresponding data of the medical scan entry. The inference data may include medical scan inference functions);
compare a plurality of actual user actions performed during the medical procedure to the plurality of predicted user actions to identify one or more deviations in the medical procedure (Col. 61, lines 40-45 and Col. 67, lines 20-22: Inference accuracy data can be based on a determined discrepancy determined by performing the inference data evaluation function. The inference accuracy data can be based on a magnitude of the difference between the inference data and the expert feedback data, and can be mapped to the medical scan. Received annotations are compared to corresponding known or later provided diagnosis data or other corresponding data of each medical scan.); and
alert a user to the one or more deviations (Col. 46, lines 26-32, and Col. 59, line 67-Col. 60, lines 1-3: information can be used by the medical scan report labeling system or other subsystem to alert a medical professional and/or hospital associated with the medical report, for example, indicated in the originating entity data or annotation author data of the error, for example, by transmitting a notification to a client device of a corresponding user. The system can transmit an alert and/or an automatically generated inferred scan category to the medical entity indicating that the scan is incorrectly classified in the scan classifier data or other metadata.).
Regarding Claim 9, Bernard teaches the system of claim 8, and further teaches:
The system of claim 8, wherein a deep learning clustering model is trained to determine the plurality of predicted user actions based on the input data (Col. 77, lines 54-61: the learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 10, Bernard teaches the system of claim 9, and further teaches:
The system of claim 9, wherein the at least one processor is further configured to: update the deep learning clustering model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 11, Bernard teaches the system of claim 8, and further teaches:
The system of claim 8, wherein a deep learning classification model is trained to identify the one or more deviations (Col. 88, lines 12-15, Col. 77, lines 54-61: The first medical scan inference function is selected based on the new medical scan classifier in response to determining that the first medical scan classifier compares unfavorably to the new medical scan classifier. Thus, the inference function identifies a deviation. The learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 12, Bernard teaches the system of claim 11, and further teaches:
The system of claim 11, wherein the at least one processor is further configured to: updating the deep learning classification model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 13, Bernard teaches the system of claim 8, and further teaches:
The system of claim 8, wherein the plurality of actual user actions are extracted from one or more from a group of: a log generated by a medical device, and image data of the medical procedure (Col. 15, lines 18-26, 34-37: Usage data can include contribution usage data, which can include a listing of, or aggregate data associated with, usages of one or more subsystems by the user, for example, where the user is generating and/or otherwise providing data and/or feedback that can is utilized by the subsystems, for example, to generate, edit, and/or confirm diagnosis data and/or to otherwise populate, modify, or confirm portions of the medical scan database or other subsystem data. The usage data can indicate one or more specific attributes of a medical scan entry that a user utilized and/or contributed to , and/or a log of the user input generated by a client device of the user).
Regarding Claim 14, Bernard teaches the system of claim 8, and further teaches:
The system of claim 8, wherein the medical procedure is a radiological image analysis procedure (Col. 5, lines 28-31: The method involves a medical scan including imaging data corresponding to a CT scan, x-ray, or any other type of radiological scan or medical scan).
Regarding Claim 15, Bernard teaches the following:
A computer program product for performing a workload with dynamic resource adjustment (Col. 104, lines 10-17: "processing unit" may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, graphics processing unit, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals), the computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (Col. 105, lines 60-61 and Col. 106, line 1: a computer readable memory includes one or more memory elements that stores digital information), the program instruction executable by at least one processor to cause the at least one processor to ( Col. 105, lines 23-25: processors executing appropriate software and the like or any combination thereof):
receive input data corresponding to a patient having a medical procedure (Col. 57, lines 22-24: The first medical scan is transmitted to a first client device associated with a user of the medical scan diagnosing system);
determine a plurality of predicted user actions to be performed during the medical procedure (Col. 57, lines 7-13, 35-37, 40-44: inference data for given medical scans used to generate and/or update diagnosis data or other corresponding data of the medical scan entry. The inference data may include medical scan inference functions);
compare a plurality of actual user actions performed during the medical procedure to the plurality of predicted user actions to identify one or more deviations in the medical procedure (Col. 61, lines 40-45 and Col. 67, lines 20-22: Inference accuracy data can be based on a determined discrepancy determined by performing the inference data evaluation function. The inference accuracy data can be based on a magnitude of the difference between the inference data and the expert feedback data, and can be mapped to the medical scan. Received annotations are compared to corresponding known or later provided diagnosis data or other corresponding data of each medical scan.); and
alert a user to the one or more deviations (Col. 46, lines 26-32, and Col. 59, line 67-Col. 60, lines 1-3: information can be used by the medical scan report labeling system or other subsystem to alert a medical professional and/or hospital associated with the medical report, for example, indicated in the originating entity data or annotation author data of the error, for example, by transmitting a notification to a client device of a corresponding user. The system can transmit an alert and/or an automatically generated inferred scan category to the medical entity indicating that the scan is incorrectly classified in the scan classifier data or other metadata.).
Regarding Claim 16, Bernard teaches the computer program product of claim 15, and further teaches:
The computer program product of claim 15, wherein a deep learning clustering model is trained to determine the plurality of predicted user actions based on the input data (Col. 77, lines 54-61: the learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 17, Bernard teaches the computer program product of claim 16, and further teaches:
The computer program product of claim 16, wherein the program instructions further cause the at least one processor to: update the deep learning clustering model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 18, Bernard teaches the computer program product of claim 15, and further teaches:
The computer program product of claim 15, wherein a deep learning classification model is trained to identify the one or more deviations (Col. 88, lines 12-15, Col. 77, lines 54-61: The first medical scan inference function is selected based on the new medical scan classifier in response to determining that the first medical scan classifier compares unfavorably to the new medical scan classifier. Thus, the inference function identifies a deviation. The learning model can additionally or alternatively include one or more of a Bayesian model, a support vector machine model, a cluster analysis model, or other supervised or unsupervised learning model. the model parameter data can be utilized to determine the corresponding medical scan image analysis functions.).
Regarding Claim 19, Bernard teaches the computer program product of claim 18, and further teaches:
The computer program product of claim 19, wherein the program instructions further cause the at least one processor to: updating the deep learning classification model (Col. 86, lines 1-3, Col. 87, lines 37-40, Col. 88, lines 40-43: generate an updated set of neural network parameters based on a calculated set of parameter errors and the preliminary set of neural network parameters. An updated first medical scan inference function is generated in response to determining the first review data indicates that the first diagnosis data is incorrect. The first medical scan inference function is updated in response to determining that the model quality check data compares unfavorably to the truth diagnosis data) based on one or more from a group of: user feedback relating to the medical procedure (Col. 48, lines 65-66, Fig. 9B, Fig. 11B: Feedback from the expert via the interactive interface can be used to generate model accuracy data), and user interactions performed during the medical procedure (Col. 96, lines 10-15: User performance data corresponding to the fourth user in the user database is updated based on the annotation accuracy score).
Regarding Claim 20, Bernard teaches the computer program product of claim 15, and further teaches:
The computer program product of claim 15, wherein the plurality of actual user actions are extracted from one or more from a group of: a log generated by a medical device, and image data of the medical procedure (Col. 15, lines 18-26, 34-37: Usage data can include contribution usage data, which can include a listing of, or aggregate data associated with, usages of one or more subsystems by the user, for example, where the user is generating and/or otherwise providing data and/or feedback that can is utilized by the subsystems, for example, to generate, edit, and/or confirm diagnosis data and/or to otherwise populate, modify, or confirm portions of the medical scan database or other subsystem data. The usage data can indicate one or more specific attributes of a medical scan entry that a user utilized and/or contributed to , and/or a log of the user input generated by a client device of the user).
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Lyman et al. (US 2022/0005187) discloses a medical scan viewing system that generates inference data based on receiver operating characteristic parameters, which utilizes deep learning mechanisms for producing the outputs.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
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, Marc Jimenez can be reached at (571) 272-4530. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.K.V./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681