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 .
Drawings
The subject matter of this application admits of illustration by a drawing to facilitate understanding of the invention. Applicant is required to furnish a drawing under 37 CFR 1.81(c). No new matter may be introduced in the required drawing. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The limitation “Health Insurance Portability and Accountability Act compliant” is indefinite because HIPAA compliance is a legal or regulatory standard, not a structural or functional feature of the claimed platform. The claim fails to provide objective boundaries or measurable criteria for determining whether a given platform is “HIPAA compliant,” therefore the scope of the claim is unclear to one of ordinary skill in the art. Accordingly, the claim does not satisfy the requirements of 35 U.S.C. § 112(b).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-8 and 20 are drawn to a data management platform, and claims 9-19 are drawn to a method. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 1 recites the limitation of listing component tasks and required skills for each procedure; assessing task complexity; collecting performance evaluations for the target healthcare professional and a matched peer group of the target healthcare professional, for the performance of one or more selected procedures, each procedure having one or more tasks and an assigned clinical complexity value for the procedure and the one or more tasks thereof; compiling the evaluations versus predetermined standards for the successful completion of each task and one or more steps thereof to provide performance parameters; calculating a competency score for the target healthcare professional for the procedure and each task thereof; and comparing the learning curves and skill levels for the procedure and each task thereof for the target healthcare professional to that of the matched peer group of the target healthcare professional to determine a competency score for the target healthcare professional. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind or by using a pen and paper. But for the “a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array” language, the claim encompasses a user simply collecting, organizing, comparing performance data to make a judgement about competency in their mind or by using a pen and paper. The mere nominal recitation of a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Claim 1 recites the limitation of performing a computation to produce learning curves from the performance parameters for the target healthcare professional and the matched peer group of the target healthcare professional, wherein the computation is selected from the group consisting of statistical modeling, deep learning modeling, and machine learning modeling; and from the learning curves for the target healthcare professional. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulas or equations, and mathematical calculations. The mere nominal recitation of a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array does not take the claim limitation out of the mathematical concept grouping. Thus, the claim recites mathematical concepts which are abstract ideas.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Independent claims 9 and 20 recites identical or nearly identical steps with respect to 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.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array,
wherein the platform is configured to perform steps comprising:
acquiring clinical schedules indicating clinical procedures to be performed;
listing component tasks and required skills for each procedure;
assessing task complexity;
collecting performance evaluations for the target healthcare professional and a matched peer group of the target healthcare professional, for the performance of one or more selected procedures, each procedure having one or more tasks and an assigned clinical complexity value for the procedure and the one or more tasks thereof;
compiling the evaluations versus predetermined standards for the successful completion of each task and one or more steps thereof to provide performance parameters;
performing a computation to produce learning curves from the performance parameters for the target healthcare professional and the matched peer group of the target healthcare professional, wherein the computation is selected from the group consisting of statistical modeling, deep learning modeling, and machine learning modeling;
from the learning curves for the target healthcare professional, calculating a competency score for the target healthcare professional for the procedure and each task thereof; and
comparing the learning curves and skill levels for the procedure and each task thereof for the target healthcare professional to that of the matched peer group of the target healthcare professional to determine a competency score for the target healthcare professional.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of collecting, analyzing, and comparing performance and competency data to evaluate to evaluate the skill level in a healthcare professional in a computer environment. The claimed computer components (i.e., a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array) are recited at a high level of generality and are merely invoked as tools to perform an existing process of evaluating professional competency though the organization and analysis of performance data . Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional element of acquiring clinical schedules indicating clinical procedures to be performed. This limitation is recited at a high level of generality (i.e., as a general means of gathering or retrieving data for use in the claimed analysis), and amounts to merely data collection or data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of collecting, analyzing, and comparing performance and competency data to evaluate to evaluate the skill level in a healthcare professional in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claim 1 does not include an additional element that are sufficient to amount to significantly more than the judicial exception. For the providing limitation that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The specification does not provide any indication that the limitation of providing a medication based on an efficacy score is anything other than a conventional action that simply follows from the determination of the efficacy score (see page 3 and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-4, 6-8, 10-12, and 14-19 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 5 and 13 recite the additional element of a graphical user interface (claim 5 and 13), a first element showing a staff assignment for a clinical encounter (claim 5 and 13). However, this additional element amounts to implementing an abstract idea on a generic computing device or mere displaying an output (i.e., an insignificant extra-solution activity)). As such, this additional element, when considered individually or in combination with the prior devices, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (U.S. Patent Publication 2021/0307864 A1), referred to hereinafter as Wolf, in view of Brown et al. (U.S. Patent Publication 2020/0411170 A1), referred to hereinafter as Brown.
Regarding claim 1, Wolf teaches a data management platform for determining a competency score for a target healthcare professional (Wolf [0253] “In some embodiments, the stored data based on prior surgical procedures may include a machine learning model trained using a data set based on prior surgical procedures. For example, a machine learning model may be trained to process video frames and generate competency-related scores, as described below.” and Wolf [0121] At step 2460, process 2450 may include receiving a plurality of additional surgical videos from a plurality of surgical procedures performed by other medical professionals. Different portions of the plurality of additional surgical videos may correspond to at least one of intraoperative surgical events, surgical outcomes, patient characteristics, surgeon characteristics, and intraoperative surgical event characteristics. As described above, other medical professionals may be associated with a particular location, hospital, department, specialty, or residency class.”), the platform comprising:
a computer, a server or data storage system, a user interface, a non-transitory computer-readable medium storing computer program instructions, software for analyzing input data and providing an output, and a data array (Wolf [0050] “Consistent with disclosed embodiments, “at least one processor” may constitute any physical device or group of devices having electric circuitry that performs a logic operation on an input or inputs. For example, the at least one processor may include one or more integrated circuits (IC), including application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), server, virtual server, or other circuits suitable for executing instructions or performing logic operations. The instructions executed by at least one processor may, for example, be pre-loaded into a memory integrated with or embedded into the controller or may be stored in a separate memory. The memory may include a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions. In some embodiments, the at least one processor may include more than one processor. Each processor may have a similar construction or the processors may be of differing constructions that are electrically connected or disconnected from each other. For example, the processors may be separate circuits or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact.” and Wolf “[0055] In some embodiments, trained machine learning algorithms (e.g., artificial intelligence algorithms) may be used to analyze inputs and generate outputs, for example in the cases described below. In some examples, a trained machine learning algorithm may be used as an inference model that when provided with an input generates an inferred output.”),
wherein the platform is configured to perform steps comprising: acquiring clinical schedules indicating clinical procedures to be performed (Wolf [0353] “FIG. 21 shows an example schedule 2100 that may include a listing of procedures such as procedures A-C (e.g., surgical procedures, or any other suitable medical procedures that may be performed in an operating room for which schedule 2100 is used). For each procedure A-C, a corresponding starting and finishing times may be determined. For example, for a past procedure A, a starting time 2121A and a finishing time 2121B may be the actual starting and finishing times. (Since procedure A is completed, the schedule 2100 may be automatically updated to reflect actual times). FIG. 21 shows that for a current procedure B, a starting time 2123A may be actual and a finishing time 2123B may be estimated (and recorded as an estimated time). Additionally, for procedure C, that is scheduled to be performed in the future, a starting time 2125A and a finishing time 2125B may be estimated and recorded. It should be noted that schedule 2100 is not limited to displaying and/or holding listings of procedures and starting/finishing times for the procedures, but may include various other data associated with an example surgical procedure.”);
listing component tasks and required skills for each procedure (Wolf [0353] “FIG. 21 shows an example schedule 2100 that may include a listing of procedures such as procedures A-C (e.g., surgical procedures, or any other suitable medical procedures that may be performed in an operating room for which schedule 2100 is used). For each procedure A-C, a corresponding starting and finishing times may be determined. For example, for a past procedure A, a starting time 2121A and a finishing time 2121B may be the actual starting and finishing times. (Since procedure A is completed, the schedule 2100 may be automatically updated to reflect actual times). FIG. 21 shows that for a current procedure B, a starting time 2123A may be actual and a finishing time 2123B may be estimated (and recorded as an estimated time). Additionally, for procedure C, that is scheduled to be performed in the future, a starting time 2125A and a finishing time 2125B may be estimated and recorded. It should be noted that schedule 2100 is not limited to displaying and/or holding listings of procedures and starting/finishing times for the procedures, but may include various other data associated with an example surgical procedure. For example, schedule 2100 may be configured to allow a user of schedule 2100 to interact with various elements of schedule 2100 (for cases when schedule 2100 is represented by a computer based interface such as a webpage, a software application, and/or another interface). For example, a user may be allowed to click over or otherwise select areas 2113, 2115, or 2117 to obtain details for procedures A, B or C respectively. Such details may include patient information (e.g., patient's name, age, medical history, etc.), surgical procedure information (e.g., a type of surgery, type of tools used for the surgery, type of anesthesia used for the surgery, and/or other characteristics of a surgical procedure), and healthcare provider information (e.g., a name of a surgeon, a name of an anesthesiologist, an experience of the surgeon, a success rate of the surgeon, a surgeon rating based on surgical outcomes for the surgeon, and/or other data relating to a surgeon). Some or all of the forgoing information may already appear in areas 2113, 2115, and 2117, without the need for further drill down.”);
assessing task complexity (Wolf [0105] “In some embodiments, the presentation of at least part of the frames assigned to the particular surgical event-related category includes a grouping of video frames from different surgical videos. The video frames may be complied into a common file for presentation or may be extracted at the time of playback from differing files. The video footage may be stored in the same location or may be selected from a plurality of storage locations. Although not necessarily so, videos within a set may be related in some way. For example, video footage within a set may include videos, recorded by the same capture device, recorded at the same facility, recorded at the same time or within the same timeframe, depicting surgical procedures performed on the same patient or group of patients, depicting the same or similar surgical procedures, depicting surgical procedures sharing a common characteristic (such as similar complexity level, including similar events, including usages of similar techniques, including usages of similar medical instruments, etc.), or sharing any other properties or characteristics.”);
performing a computation to produce learning curves from the performance parameters for the target healthcare professional and the matched peer group of the target healthcare professional, wherein the computation is selected from the group consisting of statistical modeling, deep learning modeling, and machine learning modeling (Wolf [0092] “Further embodiments may include presenting an interface enabling the specific physician to self-compare with the average skill. The interface may be a graphical user interface, e.g., user interface 700, such as on a display of a computing device. Presenting an interface may include outputting code from at least one processor, wherein the code may be configured to cause the interface to be presented. Consistent with the disclosure above, a skill score or other measure may be calculated for the specific physician and may be displayed alongside an average score or measure for the category of physicians. For example, a specific score may be calculated for a surgeon's skill in hiatal repair, wrap creation, fundus mobilization, esophageal mobilization, or other type of surgical procedure. The specific physician score in one or more surgical categories may be displayed alongside the average score for the category of physicians. In some embodiments, the specific physician score and the average score may be displayed via alphanumeric text. In other embodiments, the specific physician score and the average score may be displayed graphically. One or more scores may be displayed simultaneously either through alphanumeric text or graphically”, Wolf [0101] “In some embodiments, displaying the surgical event-related categories for selection together with the aggregate statistic for each surgical event-related category may include displaying in a juxtaposed manner, statistics of the specific medical professional and statistics of at least one of the other medical professionals. A juxtaposed manner may include displaying information from two medical professionals in a side-by-side fashion, in a table, in a graph, or in another visual arrangement that compares data between the professionals. This display allows a medical professional to compare his or her statistics against statistics of other medical professionals. For example, the display may depict a quantity of fluid leaks associated with a particular surgeon alongside a quantity of fluid leaks associated with a different surgeon. Comparisons can be made for any surgical-event related category. Other medical professionals may be associated with a particular location, hospital, department, specialty, or residency class. In some embodiments, an interface for permitting comparison of video frames captured from the specific medical professional and the at least one other medical professional may be provided. The interface may be a graphical user interface such as on a display of a computing device, e.g., user interface 700, or may include any other mechanism for providing the user with information.” and Wolf [0188] “In one example, a model (such as a statistical model, a machine learning model, a deep learning model, etc.) may be generated based on the prior surgical procedures, and the stored data may include the generated model and/or an indication of at least part of the generated model. For example, a machine learning model and/or a deep learning model may be trained using training examples based on the prior surgical procedures. While a host of correlation models may be used for prediction as discussed throughout this disclosure, exemplary predictive models may include a statistical model fit to historical image-related data (e.g., information relating to remedial actions) and outcomes; and a machine learning models trained to predict outcomes based on image-related data using training data based on historical examples.”);
from the learning curves for the target healthcare professional, calculating a competency score for the target healthcare professional for the procedure and each task thereof (Wolf [0092] “Further embodiments may include presenting an interface enabling the specific physician to self-compare with the average skill. The interface may be a graphical user interface, e.g., user interface 700, such as on a display of a computing device. Presenting an interface may include outputting code from at least one processor, wherein the code may be configured to cause the interface to be presented. Consistent with the disclosure above, a skill score or other measure may be calculated for the specific physician and may be displayed alongside an average score or measure for the category of physicians. For example, a specific score may be calculated for a surgeon's skill in hiatal repair, wrap creation, fundus mobilization, esophageal mobilization, or other type of surgical procedure. The specific physician score in one or more surgical categories may be displayed alongside the average score for the category of physicians. In some embodiments, the specific physician score and the average score may be displayed via alphanumeric text. In other embodiments, the specific physician score and the average score may be displayed graphically. One or more scores may be displayed simultaneously either through alphanumeric text or graphically.”); and
comparing the learning curves and skill levels for the procedure and each task thereof for the target healthcare professional to that of the matched peer group of the target healthcare professional to determine a competency score for the target healthcare professional (Wolf [0092] “Further embodiments may include presenting an interface enabling the specific physician to self-compare with the average skill. The interface may be a graphical user interface, e.g., user interface 700, such as on a display of a computing device. Presenting an interface may include outputting code from at least one processor, wherein the code may be configured to cause the interface to be presented. Consistent with the disclosure above, a skill score or other measure may be calculated for the specific physician and may be displayed alongside an average score or measure for the category of physicians. For example, a specific score may be calculated for a surgeon's skill in hiatal repair, wrap creation, fundus mobilization, esophageal mobilization, or other type of surgical procedure. The specific physician score in one or more surgical categories may be displayed alongside the average score for the category of physicians. In some embodiments, the specific physician score and the average score may be displayed via alphanumeric text. In other embodiments, the specific physician score and the average score may be displayed graphically. One or more scores may be displayed simultaneously either through alphanumeric text or graphically”, Wolf [0101] “In some embodiments, displaying the surgical event-related categories for selection together with the aggregate statistic for each surgical event-related category may include displaying in a juxtaposed manner, statistics of the specific medical professional and statistics of at least one of the other medical professionals. A juxtaposed manner may include displaying information from two medical professionals in a side-by-side fashion, in a table, in a graph, or in another visual arrangement that compares data between the professionals. This display allows a medical professional to compare his or her statistics against statistics of other medical professionals. For example, the display may depict a quantity of fluid leaks associated with a particular surgeon alongside a quantity of fluid leaks associated with a different surgeon. Comparisons can be made for any surgical-event related category. Other medical professionals may be associated with a particular location, hospital, department, specialty, or residency class. In some embodiments, an interface for permitting comparison of video frames captured from the specific medical professional and the at least one other medical professional may be provided. The interface may be a graphical user interface such as on a display of a computing device, e.g., user interface 700, or may include any other mechanism for providing the user with information.”, and Wolf [0092] “Further embodiments may include presenting an interface enabling the specific physician to self-compare with the average skill. The interface may be a graphical user interface, e.g., user interface 700, such as on a display of a computing device. Presenting an interface may include outputting code from at least one processor, wherein the code may be configured to cause the interface to be presented. Consistent with the disclosure above, a skill score or other measure may be calculated for the specific physician and may be displayed alongside an average score or measure for the category of physicians. For example, a specific score may be calculated for a surgeon's skill in hiatal repair, wrap creation, fundus mobilization, esophageal mobilization, or other type of surgical procedure. The specific physician score in one or more surgical categories may be displayed alongside the average score for the category of physicians. In some embodiments, the specific physician score and the average score may be displayed via alphanumeric text. In other embodiments, the specific physician score and the average score may be displayed graphically. One or more scores may be displayed simultaneously either through alphanumeric text or graphically.”).
Wolf fails to explicitly teach collecting performance evaluations for the target healthcare professional and a matched peer group of the target healthcare professional, for the performance of one or more selected procedures, each procedure having one or more tasks and an assigned clinical complexity value for the procedure and the one or more tasks thereof; and
compiling the evaluations versus predetermined standards for the successful completion of each task and one or more steps thereof to provide performance parameters.
Brown teaches collecting performance evaluations for the target healthcare professional and a matched peer group of the target healthcare professional, for the performance of one or more selected procedures, each procedure having one or more tasks and an assigned clinical complexity value for the procedure and the one or more tasks thereof (Brown [0068] “In some implementations of these embodiments, the healthcare worker information 204 can further include preference information for the respective tasks each worker is capable/qualified to performed regarding a preference of performance of the respective tasks. For example, the preference information can include system defined preference information for each task/worker combination that reflects the healthcare system or operating entity's preference for utilization the healthcare worker for the specific task relative to other tasks the healthcare worker is capable/qualified to perform. For instance, assume a surgeon is capable of performing a highly complex procedure as well as various general clinical tasks. With this example, the system can prefer the surgeon perform the highly complex procedure over the general clinical tasks, as this would in most scenarios be the most useful application of the surgeon's skills. Thus, the system can associate a higher preference rating with the complex procedure relative to the general clinical tasks. The preference information can also include worker preference rating information that provides a worker defined preference rating for performing different task reflective of the workers' personal preferences for performing certain tasks over others that the worker is capable/qualified to perform.” and Brown [0069] “The task capability information can further include information associated with the respective tasks that a worker is capable/qualified to perform regarding historically tracked and/or scored performance metrics for the respective tasks. For example, the performance metrics can include a general performance rating provided by the healthcare system that reflects the performance quality (e.g., measured by system review, employee feedback, patient feedback, etc.), efficiency, and proficiency of the healthcare worker in association with performance of each task. The performance metrics can also include information regarding the number and/or frequency of performance of the respective tasks. The performance metrics can also include information regarding historical error/complication rate. The task capability information can also include information regarding compensation schemes for compensating the healthcare worker for performing the different tasks. For example, in some implementations, a healthcare worker can be paid the same rate regardless of the specific task that the worker performs. In other embodiments, the healthcare worker can be paid different rates depending on the type of task and/or the specific task.”);
compiling the evaluations versus predetermined standards for the successful completion of each task and one or more steps thereof to provide performance parameters (Brown [0068] “In some implementations of these embodiments, the healthcare worker information 204 can further include preference information for the respective tasks each worker is capable/qualified to performed regarding a preference of performance of the respective tasks. For example, the preference information can include system defined preference information for each task/worker combination that reflects the healthcare system or operating entity's preference for utilization the healthcare worker for the specific task relative to other tasks the healthcare worker is capable/qualified to perform.”, Brown [0101] “In some embodiments, the attribute defining component 312 can also associate grouping and/or ordering attributes with two or more tasks regarding the grouping and/or ordering constraints determined by the task grouping component 308 and/or the task ordering component 310. In some embodiments, the attribute defining component 312 can also determine and associate resource attributes with the healthcare tasks that identify or indicate requirements for resources to be used for the tasks, including requirements for healthcare workers authorized to perform the tasks (e.g., determined using the healthcare worker information 204) as well as requirements for non-human resources, such as required medications, medical supplies, devices, equipment, technology and the like for use in association with performing the respective tasks (e.g., determined using the task definitions/requirements data 202 and/or the regulatory information 208).”, Brown [0071] The regulatory information 208 can include defined rules or regulations that provide guidelines regarding how to perform specific task and/or procedures circumstances. These rules or regulations are generally referred to as standard operating procedures (SOPs). For example, emergency room physicians have SOPs for patients who are brought in an unconscious state; nurses in an operating theater have SOPs for the forceps and swabs that they hand over to the operating surgeons; and laboratory technicians have SOPs for handling, testing, and subsequently discarding body fluids obtained from patients. Medical procedures can also be associated with SOPs that provide guidelines that define how to perform the procedure (e.g., steps to perform and how to perform them), how to respond to different patient conditions in association with performance of the procedure, how to respond to complications that arise, and other type of events that may arise over the course of the procedure, and the like. Some healthcare organizations can also establish or adopt SOPs for medical conditions that can define standard medical practices for treating patient's having the medical condition and the respective medical conditions. Some healthcare organizations can also have SOPs regarding providing healthcare to patients having two or more medical conditions (e.g., referred to as comorbidity). In this regard, the regulatory information 208 can include information that identifies and/or defines one or more standardized or defined protocols for following in association with performance of a procedure, treating a patient with a condition, and/or responding to a clinical scenario.”),
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to combine Wolf’s surgical skill scoring platform with Brown’s system for collecting and comparing healthcare worker evaluations and task complexity data, because both references are directed to assessing clinical or procedural performance of healthcare professionals using data driven models. A PHOSITA would have been motivated to combine Wolf’s competency modeling with Brown’s performance evaluation framework to enhance the accuracy and contextual relevance of competency scoring across tasks and peer groups, yielding predictable results in improved assessment of practitioner skill levels.
Regarding claim 2, Wolf and Brown teach the invention in claim 1, as discussed above, and further teach wherein the computation to produce learning curves is a deep learning curve modeling comprising the step of performing a statistical sampling method calculation to produce one or more learning curves for the target healthcare professional and the matched peer group of the target healthcare professional (Wolf [0092] “Further embodiments may include presenting an interface enabling the specific physician to self-compare with the average skill. The interface may be a graphical user interface, e.g., user interface 700, such as on a display of a computing device. Presenting an interface may include outputting code from at least one processor, wherein the code may be configured to cause the interface to be presented. Consistent with the disclosure above, a skill score or other measure may be calculated for the specific physician and may be displayed alongside an average score or measure for the category of physicians. For example, a specific score may be calculated for a surgeon's skill in hiatal repair, wrap creation, fundus mobilization, esophageal mobilization, or other type of surgical procedure. The specific physician score in one or more surgical categories may be displayed alongside the average score for the category of physicians. In some embodiments, the specific physician score and the average score may be displayed via alphanumeric text. In other embodiments, the specific physician score and the average score may be displayed graphically. One or more scores may be displayed simultaneously either through alphanumeric text or graphically”, Wolf [0101] “In some embodiments, displaying the surgical event-related categories for selection together with the aggregate statistic for each surgical event-related category may include displaying in a juxtaposed manner, statistics of the specific medical professional and statistics of at least one of the other medical professionals. A juxtaposed manner may include displaying information from two medical professionals in a side-by-side fashion, in a table, in a graph, or in another visual arrangement that compares data between the professionals. This display allows a medical professional to compare his or her statistics against statistics of other medical professionals. For example, the display may depict a quantity of fluid leaks associated with a particular surgeon alongside a quantity of fluid leaks associated with a different surgeon. Comparisons can be made for any surgical-event related category. Other medical professionals may be associated with a particular location, hospital, department, specialty, or residency class. In some embodiments, an interface for permitting comparison of video frames captured from the specific medical professional and the at least one other medical professional may be provided. The interface may be a graphical user interface such as on a display of a computing device, e.g., user interface 700, or may include any other mechanism for providing the user with information.” and Wolf [0188] “In one example, a model (such as a statistical model, a machine learning model, a deep learning model, etc.) may be generated based on the prior surgical procedures, and the stored data may include the generated model and/or an indication of at least part of the generated model. For example, a machine learning model and/or a deep learning model may be trained using training examples based on the prior surgical procedures. While a host of correlation models may be used for prediction as discussed throughout this disclosure, exemplary predictive models may include a statistical model fit to historical image-related data (e.g., information relating to remedial actions) and outcomes; and a machine learning models trained to predict outcomes based on image-related data using training data based on historical examples.”), Wolf [0054] “Machine learning algorithms (also referred to artificial intelligence) may be employed for the purposes of analyzing the video to identify surgical events. Such algorithms be trained using training examples, such as described below. Some non-limiting examples of such machine learning algorithms may include classification algorithms, data regressions algorithms, image segmentation algorithms, visual detection algorithms (such as object detectors, face detectors, person detectors, motion detectors, edge detectors, etc.), visual recognition algorithms (such as face recognition, person recognition, object recognition, etc.), speech recognition algorithms, mathematical embedding algorithms, natural language processing algorithms, support vector machines, random forests, nearest neighbors algorithms, deep learning algorithms, artificial neural network algorithms, convolutional neural network algorithms, recursive neural network algorithms, linear machine learning models, non-linear machine learning models, ensemble algorithms, and so forth. For example, a trained machine learning algorithm may comprise an inference model, such as a predictive model, a classification model, a regression model, a clustering model, a segmentation model, an artificial neural network (such as a deep neural network, a convolutional neural network, a recursive neural network, etc.), a random forest, a support vector machine, and so forth. In some examples, the training examples may include example inputs together with the desired outputs corresponding to the example inputs. Further, in some examples, training machine learning algorithms using the training examples may generate a trained machine learning algorithm, and the trained machine learning algorithm may be used to estimate outputs for inputs not included in the training examples. In some examples, engineers, scientists, processes and machines that train machine learning algorithms may further use validation examples and/or test examples. For example, validation examples and/or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and/or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and/or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and/or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison. In some examples, a machine learning algorithm may have parameters and hyper parameters, where the hyper parameters may be set manually by a person or automatically by a process external to the machine learning algorithm (such as a hyper parameter search algorithm), and the parameters of the machine learning algorithm may be set by the machine learning algorithm according to the training examples. In some implementations, the hyper-parameters may be set according to the training examples and the validation examples, and the parameters may be set according to the training examples and the selected hyper-parameters.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement the computation to produce learning curves using a deep learning modeling approach that includes statistical sampling methods, as taught by Wolf, because machine learning and statistical modeling techniques were well known in the art for analyzing performance data and generating predictive learning curves. A PHOSITA would have been motivated to use deep learning and sampling to improve model accuracy and generalization across practitioners, a predictable enhancement to existing competency analysis systems.
Regarding claim 3, Wolf and Brown teach the invention in claim 1, as discussed above, and further teach wherein the healthcare professional is selected from the group consisting of medical students, interns, residents, fellows, doctors, physician assistants, nurses, nurses' aides, and medical technicians (Wolf [0077] “Aspects of the present disclosure may involve medical professionals performing surgical procedures. A medical professional may include, for example, a surgeon, a surgical technician, a resident, a nurse, a physician's assistant, an anesthesiologist, a doctor, a veterinarian surgeon, and so forth.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to apply the competency scoring platform to any healthcare professional type (such as medical students, residents, or nurses) because Wolf teaches that the same performance assessment framework can be used across various medical professionals performing surgical procedures. It would have been a routine design choice to extend the system to all practitioner categories engaged in clinical procedures, yielding predictable results.
Regarding claim 4, Wolf and Brown teach the invention in claim 1, as discussed above, and further teach involving a teaching situation involving an evaluator healthcare professional and a target healthcare professional (Wolf [0239] In surgical procedures, it is important that the surgeon is competent. Thus, evaluation of the performance of health care providers (such as interns, residents, attendings, etc.) in surgeries is an import part in training of physicians and in the management of health care organizations.” and Brown [0069] “The task capability information can further include information associated with the respective tasks that a worker is capable/qualified to perform regarding historically tracked and/or scored performance metrics for the respective tasks. For example, the performance metrics can include a general performance rating provided by the healthcare system that reflects the performance quality (e.g., measured by system review, employee feedback, patient feedback, etc.), efficiency, and proficiency of the healthcare worker in association with performance of each task.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement the competency assessment in a teaching or training environm