DETAILED ACTION
This is responsive to amendments filed on 10/30/2025 in which claims 1-20 are presented for examination; Claims 1,11 and 20 have been amended.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Is the claim to a process, machine, manufacture or composition of matter?” Yes, it’s a machine.
Step 2a Prong 1 (judicial exception)
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes , the claim comes under mental processes.
Claim 1 recites:
“A non-transitory computer readable medium storing: at least one database storing clinical competency framework profiles for clinical competencies for a plurality of clinicians at a plurality of medical facilities, wherein the database stores a table associating the linked educational content units to the clinical competency frameworks on a per-medical facility basis; and instructions readable and executable by at least one electronic processor to perform a learning activities recommendation method comprising: linking educational content units completed by clinicians to clinical competencies of the clinical competency framework profiles that are fulfilled by the completed learning activities; correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles, wherein the correlating includes updating the table to reflect mapped competencies between medical facilities; and recommending one or more of the educational content units to a clinician seeking to fulfill a clinical competency in one clinical competency framework based on the correlated clinical competency frameworks and the linked educational content units.”
All the limitations above are abstract idea related to the mental process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)) with the exception of bold and underlined limitations. Claim language pertains to recommending learning activities/educational content based on competency/skill level for clinicians in different medical facilities. Similar clinical competencies from different medical facilities could be matched and analyzed and learning activities/ educational content can be recommended. Any table can be populated with educational content and associated clinical competency using pen and paper, and this information can be modified/updated to view/show competencies between different medical facilities.
Step 2A(2): Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. NO
The claim does recite additional elements; however they don’t integrate the exception into a practical application of the exception.
non-transitory computer readable medium (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 - see MPEP 2106.05(f))
database (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 - see MPEP 2106.05(f))
electronic processor(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 - see MPEP 2106.05(f))
Step 2B: evaluate whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception? NO
As discussed previously with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 2-19, further narrows the abstract idea and add the additional elements of “clustering”, “machine-learning (ML) component”.
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely 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 - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 16, it is rejected under the same rationale as claim 1. In addition it adds the additional elements of “non-transitory computer readable medium”, “database”, “clustering”.
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely 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 - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 17-18 are rejected under the same rationale as claims 2-19.
Regarding claim 20, it is rejected under the same rationale as claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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 16-17 are rejected under 35 U.S.C. 102a(2) as anticipated by Jesneck et al. ( US 20240249831 A1)
Regarding claim 16, Jesneck teaches a non-transitory computer readable medium storing:
at least one database storing (i) clinical competency framework profiles for clinical competencies for a plurality of clinicians at a plurality of medical facilities, (ii) educational content units for consumption by the clinicians((para, “[0013] …... The invention is flexible and can use many types of evaluations, rather than requiring a defined evaluation type up front. The invention can be used to create scores and profiles for medical expertise and autonomy, i.e., competency, which can be used in a variety of ways. Also, the platform and evaluations are not dependent on predefined job goals. Additionally, the following features describe the methods and systems of the present invention.”
Also, para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups. [0193] 6. Assemble the expertise (competency) and learning profiles into a live dashboard for tracking clinical activities and learning rates. From the Firefly™ (the present invention) targeted education system, include targeted educational content, learning milestones, and suggestions, as appropriate for each medical learner.”
Also, para, “[0009] Furthermore, there is a need to assess the safety or risk associated with a particular healthcare professional, or groups or a team of healthcare professionals, performing a particular medical procedure, or even with a clinic or hospital in performing a particular type of medical procedure. This risk may be quantified in a risk score that can be used to predict the probability of a clinical event achieving a relevant metric, such as patient outcome. The risk score could be used to make informed staffing and hiring decisions at a hospital or clinic, for determining when a patient should stay in-house or be transferred to another facility for medical care, and to align financial incentives, such relative value units (RVUs), to optimize hospital/clinic efficiency, maximize revenue, and reduce risk. Another contemplated application is in the optimization of insurance policies and insurance rates. A further contemplated application is in the maintenance of certification and continuing education for medical providers and instructors.”));
and instructions readable and executable by at least one electronic processor to perform a learning activities recommendation method comprising: linking educational content units completed by clinicians to clinical competencies of the clinical competency framework profiles that are fulfilled by the completed learning activities(para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”);
correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles(para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… );
clustering clinical competency frameworks with similar linked educational content units(para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… );
and recommending one or more of the clustered educational content units to a clinician seeking to fulfill a clinical competency in one clinical competency framework based on the correlated clinical competency frameworks and the linked educational content units((para, “[0304] …The current results show that with the use of a dynamic and widely-implemented framework of operative skills assessment and active modeling of lab-based training experiences, operative skill and autonomy can be improved after having been defined as insufficient. …. In some settings, such labels have implications such as reportability to regulatory bodies, and can have further implications to future licensure or credentialing. None of the residents for whom data are reported here were identified as “failing” and the subjective observations made about the observed skills were generally in the context of expected level-appropriate skills. None of the learning plans were presented to participating residents as “remediation.” The learning plans were formalized, however, with specific requirements, the most important of which was the message that supplemental training was mandatory and compliance would be monitored. In all instances, supplemental training occurred over a period of months and, in some situations, residents had to be reminded to resume sessions after missed sessions were reported by the Simulation Center staff.”
Para, “[0186] A system to index, match, and suggest educational content for the medical practitioner based on her/his clinical/surgical schedule, specialty, and current level of competency. And also a system to characterize the clinical/surgical experience and performance of a group of medical professionals, and to normalize the expertise (competency) level of each professional according to that of his/her matched peers.”
Para, “[0298] O-SCORE data for these four residents were extracted from the peer data for other residents, which were used as a control dataset for comparison purposes. Numerical O-SCORE individual skills deemed relevant to their lab-based training as well as overall scores were analyzed. Numerical data are expressed as mean±standard error (or 95% confidence intervals for graphed data), and compared before and after supplemental educational interventions (paired Student's t-tests). These scores were also compared to aggregate scores in the non-intervention group (unpaired Student's t-tests). Grouped learning curves were modeled from longitudinal assessments and logged case numbers for individual residents. Our methodology enables the calculation of the most likely learning curve for each resident group. By fitting the curve to the observed evaluation scores, it calculates the most likely values for the residents' learning rates and predicted maximum autonomy levels. We used a generalized logistic curve under a statistical framework to compensate for the reality of fewer assessments than logged relevant cases.”)
Regarding claim 17, Jesneck teaches a non-transitory computer readable medium of claim 16.
Jesneck further teaches wherein the at least one database stores the clinical competency framework profiles as a table associating the linked educational content units to the clinical competency frameworks on a per-medical facility basis ((para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”
Also, para, “[0202] Other features of the systems and methods of the present invention include the capability for modeling for resident learning and autonomy. See FIG. 16. This shows the resident autonomy level using the evaluation scale of Table 2 on the y-axis versus case complexity on the x-axis. The title of the doctor (practitioner) is also shown, i.e., medical student, junior resident, senior resident, chief resident, and fellow/attending surgeon. There is the capability for self-assembling consensus evaluations, links to educational content from the schedules, a targeted education library of curated content, and an active research feature with a content recommendation engine.”
Also, para “[0194] In one exemplary application, the platform combined disparate data across 37 institutions, comprising 47 surgical departments and 100 surgical services, aggregating 278,410 surgical operative cases with 340,128 associated procedures, and 493,807 case assignments. From these, 184,318 resident cases were logged with the ACGME, and 17,969 cases were logged to the American College of Surgeon's (ACS) Surgeon Specific Registry. The platform helped the teaching faculty submit 4,285 resident operative performance evaluations, enabling the construction of 165 procedure-specific learner profiles. Additionally, the platform aggregated 54, 126 data points from resident surgical simulation exercises, including virtual reality laparoscopic simulations.”)
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.
Claims 1, 4, 7, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jesneck et al. ( US 20240249831 A1) in view of Van (US 20140195450 A1)
Regarding claim 1, Jesneck teaches a non-transitory computer readable medium storing:
at least one database storing clinical competency framework profiles for clinical competencies for a plurality of clinicians at a plurality of medical facilities (para, “[0013] …... The invention is flexible and can use many types of evaluations, rather than requiring a defined evaluation type up front. The invention can be used to create scores and profiles for medical expertise and autonomy, i.e., competency, which can be used in a variety of ways. Also, the platform and evaluations are not dependent on predefined job goals. Additionally, the following features describe the methods and systems of the present invention.”
Para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups. [0193] 6. Assemble the expertise (competency) and learning profiles into a live dashboard for tracking clinical activities and learning rates. From the Firefly™ (the present invention) targeted education system, include targeted educational content, learning milestones, and suggestions, as appropriate for each medical learner.”
Para, “[0009] Furthermore, there is a need to assess the safety or risk associated with a particular healthcare professional, or groups or a team of healthcare professionals, performing a particular medical procedure, or even with a clinic or hospital in performing a particular type of medical procedure. This risk may be quantified in a risk score that can be used to predict the probability of a clinical event achieving a relevant metric, such as patient outcome. The risk score could be used to make informed staffing and hiring decisions at a hospital or clinic, for determining when a patient should stay in-house or be transferred to another facility for medical care, and to align financial incentives, such relative value units (RVUs), to optimize hospital/clinic efficiency, maximize revenue, and reduce risk. Another contemplated application is in the optimization of insurance policies and insurance rates. A further contemplated application is in the maintenance of certification and continuing education for medical providers and instructors.”);
wherein the database stores a table associating the linked educational content units to the clinical competency frameworks on a per-medical facility basis (para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”
Also, para, “[0202] Other features of the systems and methods of the present invention include the capability for modeling for resident learning and autonomy. See FIG. 16. This shows the resident autonomy level using the evaluation scale of Table 2 on the y-axis versus case complexity on the x-axis. The title of the doctor (practitioner) is also shown, i.e., medical student, junior resident, senior resident, chief resident, and fellow/attending surgeon. There is the capability for self-assembling consensus evaluations, links to educational content from the schedules, a targeted education library of curated content, and an active research feature with a content recommendation engine.”
Also, para “[0194] In one exemplary application, the platform combined disparate data across 37 institutions, comprising 47 surgical departments and 100 surgical services, aggregating 278,410 surgical operative cases with 340,128 associated procedures, and 493,807 case assignments. From these, 184,318 resident cases were logged with the ACGME, and 17,969 cases were logged to the American College of Surgeon's (ACS) Surgeon Specific Registry. The platform helped the teaching faculty submit 4,285 resident operative performance evaluations, enabling the construction of 165 procedure-specific learner profiles. Additionally, the platform aggregated 54, 126 data points from resident surgical simulation exercises, including virtual reality laparoscopic simulations.”)
and instructions readable and executable by at least one electronic processor to perform a learning activities recommendation method comprising: linking educational content units completed by clinicians to clinical competencies of the clinical competency framework profiles that are fulfilled by the completed learning activities (para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”);
correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles (para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… );
and recommending one or more of the educational content units to a clinician seeking to fulfill a clinical competency in one clinical competency framework based on the correlated clinical competency frameworks and the linked educational content units (para, “[0304] …The current results show that with the use of a dynamic and widely-implemented framework of operative skills assessment and active modeling of lab-based training experiences, operative skill and autonomy can be improved after having been defined as insufficient. …. In some settings, such labels have implications such as reportability to regulatory bodies, and can have further implications to future licensure or credentialing. None of the residents for whom data are reported here were identified as “failing” and the subjective observations made about the observed skills were generally in the context of expected level-appropriate skills. None of the learning plans were presented to participating residents as “remediation.” The learning plans were formalized, however, with specific requirements, the most important of which was the message that supplemental training was mandatory and compliance would be monitored. In all instances, supplemental training occurred over a period of months and, in some situations, residents had to be reminded to resume sessions after missed sessions were reported by the Simulation Center staff.”
Para, “[0186] A system to index, match, and suggest educational content for the medical practitioner based on her/his clinical/surgical schedule, specialty, and current level of competency. And also a system to characterize the clinical/surgical experience and performance of a group of medical professionals, and to normalize the expertise (competency) level of each professional according to that of his/her matched peers.”
Para, “[0298] O-SCORE data for these four residents were extracted from the peer data for other residents, which were used as a control dataset for comparison purposes. Numerical O-SCORE individual skills deemed relevant to their lab-based training as well as overall scores were analyzed. Numerical data are expressed as mean±standard error (or 95% confidence intervals for graphed data), and compared before and after supplemental educational interventions (paired Student's t-tests). These scores were also compared to aggregate scores in the non-intervention group (unpaired Student's t-tests). Grouped learning curves were modeled from longitudinal assessments and logged case numbers for individual residents. Our methodology enables the calculation of the most likely learning curve for each resident group. By fitting the curve to the observed evaluation scores, it calculates the most likely values for the residents' learning rates and predicted maximum autonomy levels. We used a generalized logistic curve under a statistical framework to compensate for the reality of fewer assessments than logged relevant cases.”)
Jesneck doesn’t explicitly teach:
wherein the correlating includes updating the table to reflect mapped competencies between medical facilities.
Van teaches:
wherein the correlating includes updating the table to reflect mapped competencies between medical facilities (para, “[0018] In an implementation, equivalent courses may be identified based on course description, course prerequisites, course objectives, course location, term, enrollment process dates, enrollment steps, enrollment requirements, student information, first institution information, second institution information, credential requirements at the first and second institutions, and/or history of previous courses offered by the second institution and accepted as equivalent courses to courses offered by the first institution.”
Para, “[0021] An implementation of the disclosed subject matter provides a system including a processor configured to receive a list of courses offered by a first institution and receive course information for each of the courses in the list. Equivalent courses may be identified for each of the courses in the list offered by a second institution and a map of course equivalencies may be generated based on the equivalent courses identified.”)
It would have been obvious for a person of ordinary skill in the art to apply course equivalencies teachings of Pelt into the teachings of Jesneck at the time the application was filed in order to determine equivalent course work at different institution/facility. (Para, “[0021] … Equivalent courses may be identified for each of the courses in the list offered by a second institution and a map of course equivalencies may be generated based on the equivalent courses identified.”)
Examiner Note: Claim 4 language is substantially same as claim 1; however, claim 4 further clarifies that “competency framework profiles as a table”, thus it is not rejected under U.S.C 112 (d).
Regarding claim 4, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches wherein the at least one database stores the clinical competency framework profiles as a table associating the linked educational content units to the clinical competency frameworks on a per-medical facility basis(para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”
Also, para, “[0202] Other features of the systems and methods of the present invention include the capability for modeling for resident learning and autonomy. See FIG. 16. This shows the resident autonomy level using the evaluation scale of Table 2 on the y-axis versus case complexity on the x-axis. The title of the doctor (practitioner) is also shown, i.e., medical student, junior resident, senior resident, chief resident, and fellow/attending surgeon. There is the capability for self-assembling consensus evaluations, links to educational content from the schedules, a targeted education library of curated content, and an active research feature with a content recommendation engine.”
Also, para “[0194] In one exemplary application, the platform combined disparate data across 37 institutions, comprising 47 surgical departments and 100 surgical services, aggregating 278,410 surgical operative cases with 340,128 associated procedures, and 493,807 case assignments. From these, 184,318 resident cases were logged with the ACGME, and 17,969 cases were logged to the American College of Surgeon's (ACS) Surgeon Specific Registry. The platform helped the teaching faculty submit 4,285 resident operative performance evaluations, enabling the construction of 165 procedure-specific learner profiles. Additionally, the platform aggregated 54, 126 data points from resident surgical simulation exercises, including virtual reality laparoscopic simulations.”)
Regarding claim 7, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches :
wherein the at least one database further stores the educational content units for consumption by the clinicians(para, “[0156]……... The platform is designed to synchronize with operating room schedules and populates case logs across resident and attending case-logging databases. The platform automatically juxtaposes operating room cases with multiple types of evaluations, and matches cases with relevant educational content, for example surgical videos, journal articles, anatomical illustrations, etc. for resident preparedness. Patient-identifying data are protected and removed from analysis wherever possible.”)
and the correlating includes: clustering clinical competency frameworks with similar linked educational content units(para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.…”)
Regarding claim 9, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches wherein correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles includes: performing the correlating with a machine-learning (ML) component(para,”[0168]…… The Firefly™ targeted education system associates each piece of content with relevant medical activities, using techniques including machine learning and natural language processing. [0171] 3. The platform connects with case logging systems for automated storage and reconciling of a provider's clinical experience. The Firefly™ case reconciling system performs data curation and automatically identifies and merges duplicate case records. ….”)
Regarding claim 15, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches wherein the method further includes:
identifying a cluster of clinical competency frameworks most commonly occurring (para, “……[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. ….”);
and recommending one or more educational content units for each clinical competency framework in the cluster (para,“…..[0145] Suggest likely tasks: The Firefly system considers the tasks added so far, and suggests appropriate tasks to add next, based on task patterns from similar procedures. [0146] Search task databank: The SME uses the Firefly system to search for tasks. The search algorithm is smart, by ordering the most relevant tasks first, based on task patterns in similar procedures. [0147] Add task: The SME adds a task to the procedure. This task can be either an existing task from the task suggestions or search, or a new task that the SME creates. [0148] List tasks: Show the procedure's tasks and layout structure. One example is shown in FIG. 1, which illustrates the flow for calculating the competency score. [0149] Suggest task transition paths: The Firefly system suggests transition paths between tasks, based on task paths in similar procedures. [0150] Draw task transition paths: The SME can either accept the suggested paths, or draw new paths between tasks. [0151] Reorder tasks if needed: The SME can use the graphical interface to drag tasks into new ordered positions and update the task paths appropriately. [0152] Estimate task transition probabilities. The Firefly system predicts and suggests task transition probabilities based on paths and transition probabilities in similar procedures. “))
Claims 2-3, 5- 6,13 -14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jesneck as modified by Van and in view of MISLER et al.(US 20230259883 A1)
Regarding claim 2, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches wherein the correlating includes clustering clinical competency frameworks with similar [textual] descriptions (para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… )
Jesneck as modified by Van doesn’t explicitly teaches [wherein the correlating includes clustering clinical competency frameworks with similar] textual [descriptions].
Misler teaches [wherein the correlating includes clustering clinical competency frameworks with similar] textual [descriptions] (para, “[0043] In one embodiment, the recommendations module 206 may first be configured to perform natural language processing (NLP) on the description metadata of available profile states and associated description along with the profile attributes of each of the requesting user profile 305 and corresponding subset of similar users as determined from the clusters 304 to determine respective textual context of each. and then performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”)
It would have been obvious for a person of ordinary skill in the art to apply texual description clustering teachings of Misler into the teachings of Jesneck as modified by Van at the time the application was filed in order to create grouped clusters having similar attributes. (para 0008, “…The method also includes clustering, using the machine learning model and based on the profile attributes of the plurality of users, to create grouped clusters of users within the entity having similar profile attributes within each cluster…”)
Regarding claim 3, Jesneck as modified by Van and Misler teaches the non-transitory computer readable medium of claim 2.
Jesneck further teaches wherein the correlating includes identifying frameworks having similar [textual] descriptions based on the clustering (para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… )
Jesneck as modified by Van and Misler doesn’t explicitly teaches [wherein the correlating includes identifying frameworks having similar] textual [descriptions based on the clustering].
Misler further teaches [wherein the correlating includes identifying frameworks having similar] textual [descriptions based on the clustering] (para, “[0043] In one embodiment, the recommendations module 206 may first be configured to perform natural language processing (NLP) on the description metadata of available profile states and associated description along with the profile attributes of each of the requesting user profile 305 and corresponding subset of similar users as determined from the clusters 304 to determine respective textual context of each. and then performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”)
It would have been obvious for a person of ordinary skill in the art to apply texual description clustering teachings of Misler into the teachings of Jesneck as modified by Van at the time the application was filed in order to create grouped clusters having similar attributes. (para 0008, “…The method also includes clustering, using the machine learning model and based on the profile attributes of the plurality of users, to create grouped clusters of users within the entity having similar profile attributes within each cluster…”)
Regarding claim 5, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 4.
Jesneck as modified by Van does not explicitly teaches further teaches wherein the method further includes:
receiving one or more inputs from an employee of the medical facility, the inputs indicative of competency framework profiles for its clinical competencies being entered;
and updating the table based on the received one or more inputs.
Misler teaches further teaches wherein the method further includes:
receiving one or more inputs from an employee of the medical facility, the inputs indicative of competency framework profiles for its clinical competencies being entered (para, “[0018] The metadata features of the profile data may be obtained and attributed to a particular user of the device while any of the computing devices (e.g. entity devices 114 or requesting computer device 106) interacts with the environment 100. In at least some aspects, at least some of the profile data features may result from offerings provided by the application server 112 in response to career related requests, which may include providing one or more software applications 112 to the devices on the environment 100 in response to a request for modifying and updating the profile status of a user such as by requesting to update the resume, mandatory training, elective education, performance metrics, proficiency quiz, and/or competency assessments. For example, the software application provided may include additional training for a particular skill or interest or certification for an individual of an entity device 114 or the requesting computer device 106. In another example, the software application(s) provided by the application server 112 may be periodically requested by the entity as triggered by the data processing server 104, such as to require competency assessments, performance metrics, or proficiency tests which may be provided in the form of native software applications or links to software applications as provided by the application server 112 for the relevant computing device (e.g. entity device 114 or requesting computer device 106). In at least some aspects, the profile data may include, online behavior information related to a user relating to any of the resume information metadata such as contact information, education, training, performance metrics, etc. and/or interactions with websites associated with the entity for which the user currently holds a position within. Such computerized interactions may include requests for training or educational resources provided online from application server 112, browsing one or more websites relating to job postings such as may be provided by the available profile server 110, which provides profile information of available jobs for the entity and associated features such as education requirements, professional requirements, etc.”);
and updating the table based on the received one or more inputs (para, “[0018] The metadata features of the profile data may be obtained and attributed to a particular user of the device while any of the computing devices (e.g. entity devices 114 or requesting computer device 106) interacts with the environment 100. In at least some aspects, at least some of the profile data features may result from offerings provided by the application server 112 in response to career related requests, which may include providing one or more software applications 112 to the devices on the environment 100 in response to a request for modifying and updating the profile status of a user such as by requesting to update the resume, mandatory training, elective education, performance metrics, proficiency quiz, and/or competency assessments. For example, the software application provided may include additional training for a particular skill or interest or certification for an individual of an entity device 114 or the requesting computer device 106. In another example, the software application(s) provided by the application server 112 may be periodically requested by the entity as triggered by the data processing server 104, such as to require competency assessments, performance metrics, or proficiency tests which may be provided in the form of native software applications or links to software applications as provided by the application server 112 for the relevant computing device (e.g. entity device 114 or requesting computer device 106). In at least some aspects, the profile data may include, online behavior information related to a user relating to any of the resume information metadata such as contact information, education, training, performance metrics, etc. and/or interactions with websites associated with the entity for which the user currently holds a position within. Such computerized interactions may include requests for training or educational resources provided online from application server 112, browsing one or more websites relating to job postings such as may be provided by the available profile server 110, which provides profile information of available jobs for the entity and associated features such as education requirements, professional requirements, etc.” Also, see para 0007)
It would have been obvious for a person of ordinary skill in the art to apply update teachings of Misler into the teachings of Jesneck as modified by Van at the time the application was filed in order to show historical progression of the user. (para 0008, “…the computer-implemented method also includes receiving and applying profile attributes of a plurality of users may include career related information as input to a machine learning model, the profile attributes further defining a historical progression of actions taken online by each user over a past time period to reach a current profile state within an entity….”)
Regarding claim 6, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 4.
Jesneck as modified by Van does not explicitly teaches wherein the clinical competency framework profiles comprise a textual description of the clinical competency using the terminology employed at the medical facility.
Misler teaches wherein the clinical competency framework profiles comprise a textual description of the clinical competency using the terminology employed at the medical facility (para, “[0042] In at least some aspects, the recommendation module 206, accesses the available profile states 306 via a database of career positions open for application within the entity, such as the available profile state repository storing such information, and retrieves therefrom associated description metadata.
[0043] In one embodiment, the recommendations module 206 may first be configured to perform natural language processing (NLP) on the description metadata of available profile states and associated description along with the profile attributes of each of the requesting user profile 305 and corresponding subset of similar users as determined from the clusters 304 to determine respective textual context of each. and then performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”)
It would have been obvious for a person of ordinary skill in the art to apply update teachings of Misler into the teachings of Jesneck as modified by Van at the time the application was filed in order to show historical progression of the user. (para 0008, “…the computer-implemented method also includes receiving and applying profile attributes of a plurality of users may include career related information as input to a machine learning model, the profile attributes further defining a historical progression of actions taken online by each user over a past time period to reach a current profile state within an entity….”)
Regarding claim 13, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck further teaches wherein the method further includes:
receiving a [textual] description of a new clinical competency framework (para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”);
receiving a selection of one or more educational content units to be included with the new clinical competency framework (para, “[0127] Returning to FIG. 1, the platform then lists required skills associated with the task or tasks. In this step, the process flow looks up the skills required to perform the task successfully. These required skills have been determined by subject matter experts (SMEs). Next, based on the number and difficulty level of the required skills, the platform computes and assesses task complexity. For example, a procedure for a laparoscopic cholecystectomy (i.e., a minimally invasive approach to removing a sick gallbladder) may include the component tasks of port placement, patient positioning, removing adhesions, dividing the cystic artery, etc., and require a skill level (competency score) of at least 0.75 on a 0-1 scale. Next, the platform calculates performance parameters based on a set of performance evaluations for the medical trainees and practitioners. The performance evaluations may include manual evaluations from medical professionals, augmented evaluations where a machine learning system facilitates a medical professional to complete the evaluation, or evaluations from autonomous machine learning systems. (The inventor's Firefly™ platform facilitates these evaluations by providing “smart” evaluations that are specific to the learner and the medical procedure and sends the evaluations to the teaching faculty soon after the procedure is competed.)…”
Also, para “[0186] A system to index, match, and suggest educational content for the medical practitioner based on her/his clinical/surgical schedule, specialty, and current level of competency. And also a system to characterize the clinical/surgical experience and performance of a group of medical professionals, and to normalize the expertise (competency) level of each professional according to that of his/her matched peers.”)
Jesneck as modified by Van doesn’t explicitly teaches textual description.
Misler teaches textual description (para, “[0043] In one embodiment, the recommendations module 206 may first be configured to perform natural language processing (NLP) on the description metadata of available profile states and associated description along with the profile attributes of each of the requesting user profile 305 and corresponding subset of similar users as determined from the clusters 304 to determine respective textual context of each. and then performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”)
It would have been obvious for a person of ordinary skill in the art to apply texual description clustering teachings of Misler into the teachings of Jesneck as modified by Van at the time the application was filed in order to create grouped clusters having similar attributes. (para 0008, “…The method also includes clustering, using the machine learning model and based on the profile attributes of the plurality of users, to create grouped clusters of users within the entity having similar profile attributes within each cluster…”)
Regarding claim 14, Jesneck as modified by Van and Misler teaches the non-transitory computer readable medium of claim 13.
Jesneck further teaches wherein the method further includes:
identifying a cluster of frameworks most closely matching the new clinical competency (para, “[0118]……… Let the curators and SME review the data trends and outliers, and either accept the outliers flag them for exclusion from consideration when creating statistical and machine learning models. [0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs…..”)
and recommending one or more educational content units occurring most frequently in the cluster for inclusion in the new clinical competency framework (para, “…..[0145] Suggest likely tasks: The Firefly system considers the tasks added so far, and suggests appropriate tasks to add next, based on task patterns from similar procedures. [0146] Search task databank: The SME uses the Firefly system to search for tasks. The search algorithm is smart, by ordering the most relevant tasks first, based on task patterns in similar procedures. [0147] Add task: The SME adds a task to the procedure. This task can be either an existing task from the task suggestions or search, or a new task that the SME creates. [0148] List tasks: Show the procedure's tasks and layout structure. One example is shown in FIG. 1, which illustrates the flow for calculating the competency score. [0149] Suggest task transition paths: The Firefly system suggests transition paths between tasks, based on task paths in similar procedures. [0150] Draw task transition paths: The SME can either accept the suggested paths, or draw new paths between tasks. [0151] Reorder tasks if needed: The SME can use the graphical interface to drag tasks into new ordered positions and update the task paths appropriately. [0152] Estimate task transition probabilities. The Firefly system predicts and suggests task transition probabilities based on paths and transition probabilities in similar procedures.”)
Regarding claim 18, Jesneck teaches the non-transitory computer readable medium of claim 17.
Jesneck does not explicitly teaches further teaches wherein the method further includes:
receiving one or more inputs from an employee of the medical facility, the inputs indicative of competency framework profiles for its clinical competencies being entered;
and updating the table based on the received one or more inputs.
Misler teaches:
receiving one or more inputs from an employee of the medical facility, the inputs indicative of competency framework profiles for its clinical competencies being entered (para, “[0018] The metadata features of the profile data may be obtained and attributed to a particular user of the device while any of the computing devices (e.g. entity devices 114 or requesting computer device 106) interacts with the environment 100. In at least some aspects, at least some of the profile data features may result from offerings provided by the application server 112 in response to career related requests, which may include providing one or more software applications 112 to the devices on the environment 100 in response to a request for modifying and updating the profile status of a user such as by requesting to update the resume, mandatory training, elective education, performance metrics, proficiency quiz, and/or competency assessments. For example, the software application provided may include additional training for a particular skill or interest or certification for an individual of an entity device 114 or the requesting computer device 106. In another example, the software application(s) provided by the application server 112 may be periodically requested by the entity as triggered by the data processing server 104, such as to require competency assessments, performance metrics, or proficiency tests which may be provided in the form of native software applications or links to software applications as provided by the application server 112 for the relevant computing device (e.g. entity device 114 or requesting computer device 106). In at least some aspects, the profile data may include, online behavior information related to a user relating to any of the resume information metadata such as contact information, education, training, performance metrics, etc. and/or interactions with websites associated with the entity for which the user currently holds a position within. Such computerized interactions may include requests for training or educational resources provided online from application server 112, browsing one or more websites relating to job postings such as may be provided by the available profile server 110, which provides profile information of available jobs for the entity and associated features such as education requirements, professional requirements, etc.”);
and updating the table based on the received one or more inputs (para, “[0018] The metadata features of the profile data may be obtained and attributed to a particular user of the device while any of the computing devices (e.g. entity devices 114 or requesting computer device 106) interacts with the environment 100. In at least some aspects, at least some of the profile data features may result from offerings provided by the application server 112 in response to career related requests, which may include providing one or more software applications 112 to the devices on the environment 100 in response to a request for modifying and updating the profile status of a user such as by requesting to update the resume, mandatory training, elective education, performance metrics, proficiency quiz, and/or competency assessments. For example, the software application provided may include additional training for a particular skill or interest or certification for an individual of an entity device 114 or the requesting computer device 106. In another example, the software application(s) provided by the application server 112 may be periodically requested by the entity as triggered by the data processing server 104, such as to require competency assessments, performance metrics, or proficiency tests which may be provided in the form of native software applications or links to software applications as provided by the application server 112 for the relevant computing device (e.g. entity device 114 or requesting computer device 106). In at least some aspects, the profile data may include, online behavior information related to a user relating to any of the resume information metadata such as contact information, education, training, performance metrics, etc. and/or interactions with websites associated with the entity for which the user currently holds a position within. Such computerized interactions may include requests for training or educational resources provided online from application server 112, browsing one or more websites relating to job postings such as may be provided by the available profile server 110, which provides profile information of available jobs for the entity and associated features such as education requirements, professional requirements, etc.” Also, see para 0007)
It would have been obvious for a person of ordinary skill in the art to apply update teachings of Misler into the teachings of Jesneck at the time the application was filed in order to show historical progression of the user. (para 0008, “…the computer-implemented method also includes receiving and applying profile attributes of a plurality of users may include career related information as input to a machine learning model, the profile attributes further defining a historical progression of actions taken online by each user over a past time period to reach a current profile state within an entity….”)
Claims 8,10,11-12, 19 -20 are rejected under 35 U.S.C. 103 as being unpatentable over Jesneck et al. ( US 20240249831 A1) as modified by Van and in view of Arnold (US 20240055084 A1)
Regarding claim 8, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 7.
Jesneck does not explicitly teaches wherein the method further includes:
tracking the educational content units completed by a clinician;
and updating a profile of the clinician based on the tracked completed educational content units.
Arnold teaches:
tracking the educational content units completed by a clinician (para, “[0023]….. Referring back to the previous example, after a medical entity completes recommended competency adjustment 168, an updated readiness score of medical entity should fall within competency threshold, such as, for example, updated readiness score being an 8 out of 10 after completion of competency adjustment. Thus, an increase in readiness score occurs after completing competency adjustment 168. In one or more embodiments, if readiness score 144 exceeds preconfigured threshold by a predetermined maximum magnitude, then competency adjustment 168 may include transferring the pediatric patient to another medical entity, such as a second medical entity, that may properly treat pediatric patient. For example, and without limitation, a readiness score of a medical entity may be a numerical value such as a 2 out of a scale of 10 for treating a pediatric patient with pneumonia. A competency threshold may include a minimum readiness score of a 6 out of 10 for a medical entity to be considered ready to treat a pediatric patient with pneumonia. Thus, the readiness score of the medical entity would be outside of the predetermined competency threshold and would not be considered competent enough to treat such a pediatric patient with pneumonia until a recommended competency adjustment is executed and/or completed. Furthermore, a predetermined maximum magnitude may be considered a specific standard deviation from the preconfigured threshold, for example, a standard deviation of more than 3 outside of the preconfigured threshold, results in computing device 104 recommending transferring pediatric patient to a nearby second medical entity with a higher and acceptable readiness score. A competency adjustment 168 may still be provided to medical entity even if readiness score 144 exceeds maximum magnitude, so that medical entity may prepare for future pediatric patients with similar conditions.”);
and updating a profile of the clinician based on the tracked completed educational content units (para, “[0023]….. Referring back to the previous example, after a medical entity completes recommended competency adjustment 168, an updated readiness score of medical entity should fall within competency threshold, such as, for example, updated readiness score being an 8 out of 10 after completion of competency adjustment. Thus, an increase in readiness score occurs after completing competency adjustment 168…”)
It would have been obvious for a person of ordinary skill in the art to apply competency assessment teachings of Arnold into the teachings of Jesneck as modified by Van at the time the application was filed in order to determine the current expertise status. (Abstract, “..to determine a real-time and/or continuous competency assessment of a medical entity.”)
Regarding claim 10, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 1.
Jesneck as modified by Van does not explicitly teach:
determining clinical competencies held by an onboarding clinician at a previous medical facility who is now working at a current medical facility,
matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility.
Arnold further teaches :
determining clinical competencies held by an onboarding clinician at a previous medical facility who is now working at a current medical facility(para, “[0022] With continued reference to FIG. 1, computing device 104 is configured to identify a readiness score 144 of medical entity for patient care as a function of pediatric patient profile 132, current expertise status 244, and optimal expertise criterion 140. For the purposes of this disclosure, a “readiness score” is a qualitative or quantitative value representing a readiness of a medical entity to care for a pediatric patient with a particular condition. Thus, readiness score may be a qualitative and/or quantitative representation of whether or not a current pediatric care capability of a medical entity meets a recommended or required pediatric care standard for pediatric care under particular circumstances. Readiness score 144 may be a numerical value on a predetermined scale that ranks a readiness of medical entity. For example, a readiness score of a medical entity may be a numerical value, such as a 2 out of a scale of 10, for treating a pediatric patient with pneumonia. Readiness score 144 may be a score, such as a numerical value, that ranks a competency or capability of a medical entity associated with a certain category or in totality compared to an optimal expertise criterion and/or standard for that category or in totality.”
Also, para “[0026] In some embodiments, a recommendation score of a competency adjustment may also be based on a competency adjustment and/or readiness score of one or more other medical entities. For example, and without limitation, a readiness score of a first medical entity may be 6 out of 10 at the current time, while the readiness score of a second entity may be 8 out of 10 at the current time. However, a machine-learning model may be trained to consider the readiness score at various times. For example, the readiness score of the second medical entity may be 4 out of 10 by the time the pediatric patient is transferred to the second entity because the criticalness of the condition of the pediatric patient will have increased within the time necessary to transfer the patient from the first medical entity to the second medical entity. Thus, a recommendation score for a competency adjustment may also be a function of a readiness score of a plurality of medical entities over time.”
matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility(para, “[0026] In some embodiments, a recommendation score of a competency adjustment may also be based on a competency adjustment and/or readiness score of one or more other medical entities. For example, and without limitation, a readiness score of a first medical entity may be 6 out of 10 at the current time, while the readiness score of a second entity may be 8 out of 10 at the current time. However, a machine-learning model may be trained to consider the readiness score at various times. For example, the readiness score of the second medical entity may be 4 out of 10 by the time the pediatric patient is transferred to the second entity because the criticalness of the condition of the pediatric patient will have increased within the time necessary to transfer the patient from the first medical entity to the second medical entity. Thus, a recommendation score for a competency adjustment may also be a function of a readiness score of a plurality of medical entities over time.” Note: medical entity could be medical provider or institution that offers health services. See para 0018.
Also, para “[0022]……. For example, a readiness score of a medical entity may be a numerical value, such as a 2 out of a scale of 10, for treating a pediatric patient with pneumonia. Readiness score 144 may be a score, such as a numerical value, that ranks a competency or capability of a medical entity associated with a certain category or in totality compared to an optimal expertise criterion and/or standard for that category or in totality.”
Also, para “[0023] In one or more embodiments, computing device 104 may determine a competency adjustment 168 as a function of readiness score 144. Computing device 104 may determine competency adjustment 168 using a machine-learning model 148, as discussed further in this disclosure. Competency adjustment 168 may be determine by comparing readiness score 144 to a preconfigured competency threshold and, if readiness score 144 is outside of preconfigured competency threshold, then competency adjustment 168 is suggested by computing device 104….”)
It would have been obvious for a person of ordinary skill in the art to apply competency assessment teachings of Arnold into the teachings of Jesneck as modified by Van at the time the application was filed in order to determine the current expertise status. (Abstract, “..to determine a real-time and/or continuous competency assessment of a medical entity.”)
Regarding claim 11, Jesneck as modified by Van and Arnold teaches the non-transitory computer readable medium of claim 10.
Jesneck further teaches wherein matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility includes: clustering the corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility to identify clinical competencies the clinician qualifies for at the current medical facility (para, “[0033] In one or more embodiments, computing device 104 may use a machine-learning model to determine competency adjustment 168. For example, and without limitation, computing device 104 may use competency machine-learning model 148 to obtain competency adjustment. A “competency machine-learning model” is a machine-learning model to produce a competency adjustment output given at least current expertise status 136 and optimal expertise criterion 140. This is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Competency machine-learning model 148 may include one or more competency machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that computing device 104 and/or a remote device may or may not use in the determination of competency adjustment 168. A competency machine-learning process may include, without limitation, machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.”
para, “[0023] In one or more embodiments, computing device 104 may determine a competency adjustment 168 as a function of readiness score 144. Computing device 104 may determine competency adjustment 168 using a machine-learning model 148, as discussed further in this disclosure. Competency adjustment 168 may be determine by comparing readiness score 144 to a preconfigured competency threshold and, if readiness score 144 is outside of preconfigured competency threshold, then competency adjustment 168 is suggested by computing device 104. …”)
Regarding claim 12, Jesneck as modified by Van and Arnold teaches the non-transitory computer readable medium of claim 11.
Jesneck as modified by Van and Arnold does not explicitly teach:
wherein recommending one or more additional clinical competencies for the clinician to obtain based on the matching includes:
recommending to an employee of the current medical facility that the clinician be recognized for the clinical competencies held by the clinician at the previous medical facility;
and recommending the one or more educational content units to allow the clinician to obtain the additional clinical competencies.
Arnold further teaches :
recommending to an employee of the current medical facility that the clinician be recognized for the clinical competencies held by the clinician at the previous medical facility(para, “[0022] With continued reference to FIG. 1, computing device 104 is configured to identify a readiness score 144 of medical entity for patient care as a function of pediatric patient profile 132, current expertise status 244, and optimal expertise criterion 140. For the purposes of this disclosure, a “readiness score” is a qualitative or quantitative value representing a readiness of a medical entity to care for a pediatric patient with a particular condition. Thus, readiness score may be a qualitative and/or quantitative representation of whether or not a current pediatric care capability of a medical entity meets a recommended or required pediatric care standard for pediatric care under particular circumstances. Readiness score 144 may be a numerical value on a predetermined scale that ranks a readiness of medical entity. For example, a readiness score of a medical entity may be a numerical value, such as a 2 out of a scale of 10, for treating a pediatric patient with pneumonia. Readiness score 144 may be a score, such as a numerical value, that ranks a competency or capability of a medical entity associated with a certain category or in totality compared to an optimal expertise criterion and/or standard for that category or in totality.”
para, “[0023] In one or more embodiments, computing device 104 may determine a competency adjustment 168 as a function of readiness score 144. Computing device 104 may determine competency adjustment 168 using a machine-learning model 148, as discussed further in this disclosure. Competency adjustment 168 may be determine by comparing readiness score 144 to a preconfigured competency threshold and, if readiness score 144 is outside of preconfigured competency threshold, then competency adjustment 168 is suggested by computing device 104. “);
and recommending the one or more educational content units to allow the clinician to obtain the additional clinical competencies(para, “[0031]……. In one or more embodiments, staffing may include a type of pediatric healthcare professional required and/or or recommended for treating a pediatric patient with a specific condition and/or pediatric patient profile 132. Staffing 160 may also include providing training and or certification necessary for a healthcare professional to be considered a pediatric-trained staff member. In one or more embodiments, determining competency adjustment 104 includes providing training for medical staff. Competency adjustment 168 may include providing medical entity or staff required or recommended curriculum, such as training or certification curriculum. Curriculum may be provided by computing device, such as in the form of one or more learning modules, or curriculum may be provided by a third-party, such as a certification board, accredited program, or educational institution. For example, and without limitation, training for medical personnel may include recommending certification programs offered by a third party. In another example, and without limitation, training for medical personnel may also include offering a training program, such as a simulation, that allows medical personnel to learn a proper procedure or protocol to provide proper pediatric care to a patient, such as the patient described in a pediatric patient profile. In one or more embodiments, scheduling 164 may include computing device 104 providing rotations and/or scheduling for a plurality of pediatric-trained staff for a particular treatment plan of a pediatric patient.”)
It would have been obvious for a person of ordinary skill in the art to apply competency assessment teachings of Arnold into the teachings of Jesneck as modified by Van and Arnold at the time the application was filed in order to determine the current expertise status. (Abstract, “..to determine a real-time and/or continuous competency assessment of a medical entity.”)
Regarding claim 19, Jesneck as modified by Van teaches the non-transitory computer readable medium of claim 15.
Jesneck does not explicitly teaches wherein the method further includes:
tracking the educational content units completed by a clinician;
and updating a profile of the clinician based on the tracked completed educational content units.
Arnold teaches:
tracking the educational content units completed by a clinician (para, “[0023]….. Referring back to the previous example, after a medical entity completes recommended competency adjustment 168, an updated readiness score of medical entity should fall within competency threshold, such as, for example, updated readiness score being an 8 out of 10 after completion of competency adjustment. Thus, an increase in readiness score occurs after completing competency adjustment 168. In one or more embodiments, if readiness score 144 exceeds preconfigured threshold by a predetermined maximum magnitude, then competency adjustment 168 may include transferring the pediatric patient to another medical entity, such as a second medical entity, that may properly treat pediatric patient. For example, and without limitation, a readiness score of a medical entity may be a numerical value such as a 2 out of a scale of 10 for treating a pediatric patient with pneumonia. A competency threshold may include a minimum readiness score of a 6 out of 10 for a medical entity to be considered ready to treat a pediatric patient with pneumonia. Thus, the readiness score of the medical entity would be outside of the predetermined competency threshold and would not be considered competent enough to treat such a pediatric patient with pneumonia until a recommended competency adjustment is executed and/or completed. Furthermore, a predetermined maximum magnitude may be considered a specific standard deviation from the preconfigured threshold, for example, a standard deviation of more than 3 outside of the preconfigured threshold, results in computing device 104 recommending transferring pediatric patient to a nearby second medical entity with a higher and acceptable readiness score. A competency adjustment 168 may still be provided to medical entity even if readiness score 144 exceeds maximum magnitude, so that medical entity may prepare for future pediatric patients with similar conditions.”);
and updating a profile of the clinician based on the tracked completed educational content units (para, “[0023]….. Referring back to the previous example, after a medical entity completes recommended competency adjustment 168, an updated readiness score of medical entity should fall within competency threshold, such as, for example, updated readiness score being an 8 out of 10 after completion of competency adjustment. Thus, an increase in readiness score occurs after completing competency adjustment 168…”)
It would have been obvious for a person of ordinary skill in the art to apply competency assessment teachings of Arnold into the teachings of Jesneck at the time the application was filed in order to determine the current expertise status. (Abstract, “..to determine a real-time and/or continuous competency assessment of a medical entity.”)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Jesneck et al. ( US 20240249831 A1) in view of Arnold (US 20240055084 A1)
Regarding claim 20, Jesneck teaches a learning activities recommendation method, comprising:
linking educational content units completed by clinicians to clinical competencies of clinical competency framework profiles that are fulfilled by the completed learning activities, the clinical competency framework profiles comprising clinical competencies for a plurality of clinicians at a plurality of medical facilities (para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”);
correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles (para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… );
and recommending the one or more of the educational content units based on the matching (para, “[0304] …The current results show that with the use of a dynamic and widely-implemented framework of operative skills assessment and active modeling of lab-based training experiences, operative skill and autonomy can be improved after having been defined as insufficient. …. In some settings, such labels have implications such as reportability to regulatory bodies, and can have further implications to future licensure or credentialing. None of the residents for whom data are reported here were identified as “failing” and the subjective observations made about the observed skills were generally in the context of expected level-appropriate skills. None of the learning plans were presented to participating residents as “remediation.” The learning plans were formalized, however, with specific requirements, the most important of which was the message that supplemental training was mandatory and compliance would be monitored. In all instances, supplemental training occurred over a period of months and, in some situations, residents had to be reminded to resume sessions after missed sessions were reported by the Simulation Center staff.”
Para, “[0186] A system to index, match, and suggest educational content for the medical practitioner based on her/his clinical/surgical schedule, specialty, and current level of competency. And also a system to characterize the clinical/surgical experience and performance of a group of medical professionals, and to normalize the expertise (competency) level of each professional according to that of his/her matched peers.”
Para, “[0298] O-SCORE data for these four residents were extracted from the peer data for other residents, which were used as a control dataset for comparison purposes. Numerical O-SCORE individual skills deemed relevant to their lab-based training as well as overall scores were analyzed. Numerical data are expressed as mean±standard error (or 95% confidence intervals for graphed data), and compared before and after supplemental educational interventions (paired Student's t-tests). These scores were also compared to aggregate scores in the non-intervention group (unpaired Student's t-tests). Grouped learning curves were modeled from longitudinal assessments and logged case numbers for individual residents. Our methodology enables the calculation of the most likely learning curve for each resident group. By fitting the curve to the observed evaluation scores, it calculates the most likely values for the residents' learning rates and predicted maximum autonomy levels. We used a generalized logistic curve under a statistical framework to compensate for the reality of fewer assessments than logged relevant cases.”)
Jesneck does not explicitly teaches:
determining clinical competencies held by an onboarding clinician at a previous medical facility who is now working at a current medical facility;
matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility;
Arnold teaches :
determining clinical competencies held by an onboarding clinician at a previous medical facility who is now working at a current medical facility (para, “[0022] With continued reference to FIG. 1, computing device 104 is configured to identify a readiness score 144 of medical entity for patient care as a function of pediatric patient profile 132, current expertise status 244, and optimal expertise criterion 140. For the purposes of this disclosure, a “readiness score” is a qualitative or quantitative value representing a readiness of a medical entity to care for a pediatric patient with a particular condition. Thus, readiness score may be a qualitative and/or quantitative representation of whether or not a current pediatric care capability of a medical entity meets a recommended or required pediatric care standard for pediatric care under particular circumstances. Readiness score 144 may be a numerical value on a predetermined scale that ranks a readiness of medical entity. For example, a readiness score of a medical entity may be a numerical value, such as a 2 out of a scale of 10, for treating a pediatric patient with pneumonia. Readiness score 144 may be a score, such as a numerical value, that ranks a competency or capability of a medical entity associated with a certain category or in totality compared to an optimal expertise criterion and/or standard for that category or in totality.”
Also, para “[0026] In some embodiments, a recommendation score of a competency adjustment may also be based on a competency adjustment and/or readiness score of one or more other medical entities. For example, and without limitation, a readiness score of a first medical entity may be 6 out of 10 at the current time, while the readiness score of a second entity may be 8 out of 10 at the current time. However, a machine-learning model may be trained to consider the readiness score at various times. For example, the readiness score of the second medical entity may be 4 out of 10 by the time the pediatric patient is transferred to the second entity because the criticalness of the condition of the pediatric patient will have increased within the time necessary to transfer the patient from the first medical entity to the second medical entity. Thus, a recommendation score for a competency adjustment may also be a function of a readiness score of a plurality of medical entities over time.”
matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility (para, “para “[0026] In some embodiments, a recommendation score of a competency adjustment may also be based on a competency adjustment and/or readiness score of one or more other medical entities. For example, and without limitation, a readiness score of a first medical entity may be 6 out of 10 at the current time, while the readiness score of a second entity may be 8 out of 10 at the current time. However, a machine-learning model may be trained to consider the readiness score at various times. For example, the readiness score of the second medical entity may be 4 out of 10 by the time the pediatric patient is transferred to the second entity because the criticalness of the condition of the pediatric patient will have increased within the time necessary to transfer the patient from the first medical entity to the second medical entity. Thus, a recommendation score for a competency adjustment may also be a function of a readiness score of a plurality of medical entities over time.” Note: medical entity could be medical provider or institution that offers health services. See para 0018.
Also, para “[0022]……. For example, a readiness score of a medical entity may be a numerical value, such as a 2 out of a scale of 10, for treating a pediatric patient with pneumonia. Readiness score 144 may be a score, such as a numerical value, that ranks a competency or capability of a medical entity associated with a certain category or in totality compared to an optimal expertise criterion and/or standard for that category or in totality.”
Also, para “[0023] In one or more embodiments, computing device 104 may determine a competency adjustment 168 as a function of readiness score 144. Computing device 104 may determine competency adjustment 168 using a machine-learning model 148, as discussed further in this disclosure. Competency adjustment 168 may be determine by comparing readiness score 144 to a preconfigured competency threshold and, if readiness score 144 is outside of preconfigured competency threshold, then competency adjustment 168 is suggested by computing device 104….”)
It would have been obvious for a person of ordinary skill in the art to apply competency assessment teachings of Arnold into the teachings of Jesneck at the time the application was filed in order to determine the current expertise status. (Abstract, “..to determine a real-time and/or continuous competency assessment of a medical entity.”)
Response to Arguments
Applicant's arguments filed on 10/30/2025 have been fully considered but they are not persuasive.
Remarks - 35 USC § 101
In remarks, Pg. 1, applicant contends:
“The claimed method includes steps such as linking educational content units completed by clinicians to clinical competencies, correlating clinical competency frameworks across different medical facilities, and recommending educational content units based on these correlations. These steps require the use of computer technology, databases, and machine learning components, and cannot be performed entirely in the human mind, especially at the scale and complexity described in the claims and specification and certainly not sufficient to create statistically significant correlations in clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies.”
The examiner have not rejected the use of computer technology, databases etc.… rather, the examiner have indicated these technology to be additional limitations, that are merely being used as tool at apply it level. The claims, or specification does not provide any detail as to how the above mentioned technologies are being improved, rather it is clear that generically available component are being used to execute the abstract idea.
In remarks, Pg. 2, applicant contends: “The Federal Circuit has held that claims reciting specific improvements to computer- related technology are not abstract ideas (Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016)). The present claims recite a specific method for recommending learning activities based on correlated competency frameworks, which is a technical improvement over prior art systems.”
The applicant is merely providing conclusory statement; from applicant’s arguments, claims or specification, it is not clear what/how technology is being improved. Furthermore, the applicant provides no correlation between claimed invention and cited case laws. In Addition, one can’t show improvement to abstract idea by pointing to abstract idea as technical improvement. One can recommend learning activities based on correlated competencies using paper and pen; in fact claim makes it clear that these information are saved in profile as a table (see,. Claim 4).
In remarks, Pg. 4, applicant contends: “The claimed method includes steps such as linking educational content units completed by clinicians to clinical competencies, correlating clinical competency frameworks across different medical facilities, and recommending educational content units based on these correlations. These steps require the use of computer technology, databases, and machine learning components, and cannot be performed entirely in the human mind, especially at the scale and complexity described in the claims and specification and certainly not sufficient to create statistically significant correlations in clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies.”
As mentioned above, such correlation and mapping can be achieved via table on paper using paper and pen, thus argued subject matter falls under abstract idea itself, and can’t be used to show the improvement. The examiner agree that one can’t perform these steps in mind at the scale and complexity, and computer can make this process efficient; however, that would be using the computer as tool, rather than an improvement to the computer technology.
In Pgs. 1-4, applicant argues that claimed limitations, can’t be performed in mind, claims uses database, processor, machine learning etc.… The examiner have addressed these additional limitation in the 35 U.S.C 101 analysis above, the claims don’t provide any details as to how the database, processor, or machine learning technology is being improved; merely applying the above cited technology doesn’t show an improvement. Furthermore, the applicant states that use of data structure is novel, however the claimed makes it clear that data is being saved in profile using table, which is generic way of saving data in all the relational databases.
Remarks - 35 USC § 103
In remarks, Pg. 5, applicant contends “In particular, Jesneck fails to teach or suggest the critical limitation of: "...correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles."
As cited in the previous office action, para 0121 teaches:
para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions. [0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.” Para 0110-0117 teaches different type of data processing including textual description, such as fixing misspelling for common medical terms, synonyms, and abbreviation of medical terms etc.… )
As can be seen, first the reference teaches that “procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type.” Here, different procedures across various hospitals/facilities is being standardized/correlated. Further the references teaches “for the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions.” Here, evaluation performance ratings of trainees is clustered, to define matched groups for learners across institution (correlating clinical competency across facilities). Furthermore, the reference teaches “Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program.” Thus, by requiring minimum requirements, it explicitly teaches defining similar clinical competencies across the medical facilities. Also, note, the secondary reference Pelt also teaches equivalency of courses between two different institutes for the degree (not part of rejection, just to emphasize both references teach the claimed limitation, using different terminology).
In remarks, Pg. 5-8, applicant contends “Jesneck describes collecting and storing data related to clinical schedules, performance evaluations, and competency scores. Jesneck discusses data aggregation and standardization, and mentions "databases" and "arrays". However, Jesneck does not disclose or suggest a database structure that specifically stores clinical competency framework profiles as a table associating linked educational content units to clinical competency frameworks on a per-medical facility basis. Jesneck's data structures are focused on tracking performance, evaluations, and case logs, not on associating educational content units to competency frameworks per facility.”
Jesneck, as cited literally discloses:
“para, “[0187] FIG. 11 shows the data flow and processing system for quantifying medical expertise (competency) and constructing medical learner profiles. The data flow and steps can be summarized as follows: [0188] 1. For each medical practitioner, gather clinical and surgical experience, including patient volume, case types with procedure information, and patient outcomes. [0189] 2. Gather evaluation data, including evaluations of clinical and surgical performance, self-evaluations, and peer assessments. [0190] 3. In addition to clinical information, also gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications. [0191] 4. Perform the statistical modeling and construction of learning curves on each relevant medical task and procedure, as described above. [0192] 5. From these learning curves, construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”
As can be seen, the reference explicitly teaches medical expertise profile (competency profile), and learner profile. It further states “construct medical expertise profiles and learner profiles, to summarize each practitioner's expertise levels (competency scores) and to compare to relevant peer groups.”
The claim states “wherein the at least one database stores the clinical competency framework profiles as a table (see profiles in above citation) associating the linked educational content units to the clinical competency frameworks (gather available data on medical and graduate education, research outcomes (e.g. publications, posters, conference talks, and grants), and professional licenses and certifications) on a per-medical facility basis.”
In addition, para “[0194] In one exemplary application, the platform combined disparate data across 37 institutions, comprising 47 surgical departments and 100 surgical services, aggregating 278,410 surgical operative cases with 340,128 associated procedures, and 493,807 case assignments.
Furthermore, para “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type. Numerical values are also clustered. For the evaluation performance ratings of trainees are clustered, to define matched peer groups for learners across institutions.”
As can be seen performance rating (competency) of trainees can be matched across institution, thus profile have data on a per medical facility basis (institution). Also, see para 0110 which teaches data can be stored and extracted from table format, such as CSZV, xlsx.
In remarks, Pg. 9, applicant contends “However, Misler's context is career progression and user profiles within an entity (e.g., employees in a company), not clinical competency frameworks in a healthcare or medical education context. While Misler discusses clustering and NLP, it does so for the purpose of recommending career actions or training to users based on their profile states and the textual context of job descriptions.”
As stated in rejection, the Jesneck already teaches clustering clinical competencies. For example:
“para, “[0121] Cluster and classify data: Cluster the data values, for inclusion into groups for computation. For example, procedure names can be grouped by procedure type, so that all the synonyms of the procedure across various hospitals are marked as belonging to the same procedure type….”
Here, clinical competency across various hospitals are being being grouped/clustered, for example by procedure type. Further, it even discloses that minimum requirement to achieve the competency can be identified.
“[0122] Help SMEs prioritize tasks: Medical educators can review and search through hundreds of medical procedures and tasks, and indicate which ones are especially important for training programs. Often trainees must achieve specific minimum requirements, such as a minimum number of various procedures, in order to graduate from their training program. The local educators may also prioritize procedures and tasks according to their local educational initiatives.”
Thus, there is no question about correlating competency framework of different medical facilities, and grouping/clustering (also, disclosed in claim 1); with regard to claim 2, the difference being though the reference teaches analyzing the textual description, it is more for purposes of misspells, synonyms etc.. (para 0110-0117); whereas the claim is clustering based on similar description. For this notion, Misler teaches that clustering can be done based on similar description.
“[0043] In one embodiment, the recommendations module 206 may first be configured to perform natural language processing (NLP) on the description metadata of available profile states and associated description along with the profile attributes of each of the requesting user profile 305 and corresponding subset of similar users as determined from the clusters 304 to determine respective textual context of each. and then performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”)
As can be seen from above disclosure, Misler teaches “performing the recommendations based on the determined textual context of the description metadata of a particular career position having more than a predefined degree of match with the first user profile (e.g. the requesting user profile 305) and second user profile attributes (e.g. similar users determined via the clusters 304).”
The applicant seems to argue the intended use, where it state that Misler is doing NPL and clustering for providing training to user; whereas the instant claimed invention does for clinical competency in healthcare or medical context. The flaw with such reasoning is that, first of all Jesneck already teaches clustering for clinical competency as explained above; secondly, the clustering is being performed on data by performing natural processing; the technique is same regardless if the data is regarding the training for user, and for medical competencies. In addition, this is combination rejection, where the teaching of Misler is being incorporated into the Jesneck reference, by applying the disclosed technique on the data available in Jesneck reference. The applicant is providing conclusory statement, by looking at each reference in vacuum.
In remarks, Pg. 13, applicant contends, “However, there is no motivation or reasonable expectation of success in combining these teachings to arrive at the claimed invention. The technical problems and data structures in Jesneck (medical competencies, frameworks, and educational content) are fundamentally different from those in Misler (career profiles, job descriptions, and user actions). Adapting Misler's approach to the context of clinical competency frameworks would require more than routine skill; it would require significant redesign and adaptation, and there is no suggestion in either reference to do so.”
As can be seen in above argument, the applicant is merely providing conclusory statement without any elaboration. The applicant states “the technical problems and data structures in Jesneck (medical competencies, frameworks, and educational content)” and in Misler “career profiles, job descriptions, and user actions.” The applicant is portraying this to be different technical data structures, however they both present data that can be analyzed using NLP. In fact, Jesneck makes it clear that NLP for clustering can be applied to description, just as Misler is doing; the only difference being that in Jesneck, the reference only teaches for misspelling or synonym purposes. There is already proof that NLP can be performed on data presented in Jesneck, so it would be obvious to use the teaching provider in Misler to do that on any data description to determine the similarity.
In remarks, Pg. 13, applicant contends, “Neither Jesneck nor Misler, nor their combination, teaches or suggests clustering clinical competency frameworks with similar linked educational content units. Jesneck's clustering is not based on the educational content linked to frameworks, but on procedure types and numerical data. Misler's clustering is applied to user profiles and career states, not to clinical competency frameworks or their linked educational content.”
The claim language states “wherein the at least one database further stores the educational content units for consumption by the clinicians, and the correlating includes: clustering clinical competency frameworks with similar linked educational content units.”
Here, what is being clustered is clinical competency, which under BRI can be clustering of procedures. Furthermore, the clinical competency frameworks are with similar linked education content units. Note, here if one is determining correlation based on procedure types, one is basing on similar linked educational content units. You will not have procedure related to heart competency grouped based on information related to Kidney. Furthermore, para 0015 states:
“[0015] The present invention provides a platform that uses medical and educational activity data, such as clinical schedules, training exercises, and educational activities, to track professional medical tasks and assess their complexity. By pairing these tasks with performance evaluations for healthcare practitioners, the platform uses computational models for each type of medical task to construct learning curves and calculates the practitioner's individual competency score and outcome risk score. Future scores are predicted using learning curves and future scheduled activities.”
Thus the competency framework considers the linked education content. If the applicant is trying to convey course/education units equivalency between the institutes, such concept have been addressed in amended claim 1 in view of Pelt.
In remarks, Pg. 14, applicant contends “While Arnold describes onboarding and matching competencies for clinicians moving between facilities, and Jesneck describes a platform for tracking competencies and recommending educational content, neither reference-nor their combination-teaches or suggests the claimed method of storing and updating a per-facility table associating educational content units to clinical competency frameworks, and using this table to automate the mapping and recommendation process for onboarding clinicians.”
Claim 10 recites: “wherein recommending includes: determining clinical competencies held by an onboarding clinician at a previous medical facility who is now working at a current medical facility; matching corresponding clinical competency frameworks at the new medical facility with the clinical competency frameworks of the previous medical facility.”
As can be seen, claim 10 does not require teaching “storing and updating a per-facility table associating educational content units to clinical competency frameworks.”
This language closely related to amended claim 1, which claim 10 depends on; and it has been addressed with regard to claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMA WASEEM whose telephone number is (571)272-1316. The examiner can normally be reached Monday-Friday(9:00am - 5:00 pm) EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B. Dunham can be reached on (571) 272-8109. 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.
/HUMA WASEEM/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686