DETAILED ACTION
This action is in response to the original filing of 3-14-2024. Claims 1-20 are pending and have been considered below:
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 represent method, system and medium type claims. Therefore claims 1-26 are directed to either a process, machine, manufacture or composition of matter.
Regarding claims 1, 15 and 20:
2A Prong 1:
receiving, by one or more processors, data associated with a first entity from a plurality of data sources, wherein the data includes a control dataset and at least one of a system dataset or a non-system dataset; determining, by the one or more processors via input of a first subset of the control dataset , a classification of the first entity; determining, by the one or more processors via input of at least one of the system dataset or the non-system dataset , at least one of a system score for the first entity or a non-system score for the first entity, respectively, each of the system score and the non-system score representing a likelihood of burnout of the first entity; determining, by the one or more processors via input of at least one of the system score, the non-system score, or a second subset of the control dataset , a composite score for the first entity, the composite score representing a likelihood of turnover of the first entity; determining, by the one or more processors, at least one of a lateral score or a longitudinal score for the first entity based on at least one of the classification of the first entity or the composite score for the first entity; comparing, by the one or more processors, at least one of the lateral score or the longitudinal score with a pre-determined threshold;
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion-user can generate a graph of features).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, by one or more processors, data associated with a first entity from a plurality of data sources, wherein the data includes a control dataset and at least one of a system dataset or a non-system dataset (adding insignificant extra-solution activity to the judicial exception-see MPEP 2106.05 (g)).
into a first machine learning model
into a second machine learning model
into a third machine learning model
into a fourth machine learning model
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
and initiating, by the one or more processors, performance of one or more mitigation actions based on the comparison.
(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)).
one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions
A non-transitory computer readable medium, the non-transitory computer readable medium storing instructions (MERE INSTRUCTIONS TO APPLY THE EXCEPTION USING A GENERIC COMPUTER COMPONENT)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, by one or more processors, data associated with a first entity from a plurality of data sources, wherein the data includes a control dataset and at least one of a system dataset or a non-system dataset.
MPEP 2106.05 (d)(II)indicate that merely “receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner. Thereby, a conclusion that the claimed receiving step is well-understood, routine, conventional activity is supported under Berkheimer.
into a first machine learning model
into a second machine learning model
into a third machine learning model
into a fourth machine learning model
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
and initiating, by the one or more processors, performance of one or more mitigation actions based on the comparison.
(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)).
one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions
A non-transitory computer readable medium, the non-transitory computer readable medium storing instructions (MERE INSTRUCTIONS TO APPLY THE EXCEPTION USING A GENERIC COMPUTER COMPONENT)
Regarding claims 2 and 16:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
scaling the first subset of the control dataset associated with the first entity to provide an equal weight to each variable of the first subset of the control dataset; and inputting the scaled first subset of the control dataset into the first machine learning model configured to determine the classification of the first entity, wherein the first machine learning model comprises at least one of a deep embedded clustering algorithm or a K-means clustering algorithm.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
scaling the first subset of the control dataset associated with the first entity to provide an equal weight to each variable of the first subset of the control dataset; and inputting the scaled first subset of the control dataset into the first machine learning model configured to determine the classification of the first entity, wherein the first machine learning model comprises at least one of a deep embedded clustering algorithm or a K-means clustering algorithm.
(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)).
Regarding claims 3 and 17:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
comprising training the second machine learning model by: pre-processing a training system dataset to generate a first master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the first master training dataset into a first training dataset and a first validation dataset; and inputting the first training dataset into the second machine learning model to determine a training system score, wherein one or more performance parameters of the second machine learning model are measured using the first validation dataset.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
comprising training the second machine learning model by: pre-processing a training system dataset to generate a first master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the first master training dataset into a first training dataset and a first validation dataset; and inputting the first training dataset into the second machine learning model to determine a training system score, wherein one or more performance parameters of the second machine learning model are measured using the first validation dataset.
(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)).
Regarding claims 4 and 18:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
comprising training the third machine learning model by: pre-processing a training non-system dataset to generate a second master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the second master training dataset into a second training dataset and a second validation dataset; and inputting the second training dataset into the third machine learning model to determine a training non-system score, wherein one or more performance parameters of the third machine learning model are measured using the second validation dataset.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
comprising training the third machine learning model by: pre-processing a training non-system dataset to generate a second master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the second master training dataset into a second training dataset and a second validation dataset; and inputting the second training dataset into the third machine learning model to determine a training non-system score, wherein one or more performance parameters of the third machine learning model are measured using the second validation dataset.
(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)).
Regarding claims 5 and 19:
2A Prong 1:
No Additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
comprising training the fourth machine learning model by: pre-processing at least one of a training system score, a training non-system score, or a subset of a training control dataset to generate a third master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the third master training dataset into a third training dataset and a third validation dataset; and inputting the third training dataset into the fourth machine learning model to determine a training composite score, wherein one or more performance parameters of the fourth machine learning model are measured using the third validation dataset.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
comprising training the fourth machine learning model by: pre-processing at least one of a training system score, a training non-system score, or a subset of a training control dataset to generate a third master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the third master training dataset into a third training dataset and a third validation dataset; and inputting the third training dataset into the fourth machine learning model to determine a training composite score, wherein one or more performance parameters of the fourth machine learning model are measured using the third validation dataset.
(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)).
Regarding claim 6:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein initiating the performance of the one or more mitigation actions comprises: determining, by the one or more processors, the one or more mitigation actions upon determining at least one of the lateral score or the longitudinal score exceeds the pre-determined threshold, wherein the one or more mitigation actions include at least one of intervention by a second entity for managing a workload of the first entity, modifying work schedules of the first entity, delegating activities determined as detrimental to mental health of the first entity, or recommending a time-off or stress-relieving activities.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein initiating the performance of the one or more mitigation actions comprises: determining, by the one or more processors, the one or more mitigation actions upon determining at least one of the lateral score or the longitudinal score exceeds the pre-determined threshold, wherein the one or more mitigation actions include at least one of intervention by a second entity for managing a workload of the first entity, modifying work schedules of the first entity, delegating activities determined as detrimental to mental health of the first entity, or recommending a time-off or stress-relieving activities.
(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)).
Regarding claim 7:
2A Prong 1:
analyzing, by the one or more processors, at least one of the lateral score or the longitudinal score to measure efficacy of the one or more mitigation actions or adherence to the one or more mitigation actions.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion-user can analyze scores mentally).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 8:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
updating, by the one or more processors, at least one of the system score or the non-system score in real-time, near real-time, or on a scheduled basis to dynamically determine the likelihood of burnout of the first entity.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
updating, by the one or more processors, at least one of the system score or the non-system score in real-time, near real-time, or on a scheduled basis to dynamically determine the likelihood of burnout of the first entity.
(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)).
Regarding claim 9:
2A Prong 1:
wherein the lateral score includes a comparison between one or more scores associated with the first entity and one or more scores associated with other entities within a pre-determined time threshold.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion-user can compare scores).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 10:
2A Prong 1:
wherein the longitudinal score includes a comparison between multiple scores associated with the first entity within a pre-determined time threshold.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion-user can compare scores).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 11:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the control dataset includes at least one of patient panel data, provider registry data, patient complexity data, staffing ratio data, specialty data, patient volume data, patient social determinants of health (SDOH) data, or patient panel race, language, and ethnicity (RLE) data.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the control dataset includes at least one of patient panel data, provider registry data, patient complexity data, staffing ratio data, specialty data, patient volume data, patient social determinants of health (SDOH) data, or patient panel race, language, and ethnicity (RLE) data.
(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)).
Regarding claim 12:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the system dataset includes at least one of electronic health record (EHR) data, clinical data, national provider identifier (NPI) data, intervention adherence data, net prompter score (NPS) data, historical training data, or task data.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the system dataset includes at least one of electronic health record (EHR) data, clinical data, national provider identifier (NPI) data, intervention adherence data, net prompter score (NPS) data, historical training data, or task data.
(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)).
Regarding claim 13:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the non-system dataset includes at least one of environmental data, social determinants of health (SDOH) data, financial data, health condition data, or activity data.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the non-system dataset includes at least one of environmental data, social determinants of health (SDOH) data, financial data, health condition data, or activity data.
(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)).
Regarding claim 14:
2A Prong 1:
No additional abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein at least one of the first, the second, or the third machine learning model is at least one of Gradient Boosting Machine (GBM), LightGBM (LGBM), or Extra Trees Classifier.
(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)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein at least one of the first, the second, or the third machine learning model is at least one of Gradient Boosting Machine (GBM), LightGBM (LGBM), or Extra Trees Classifier.
(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)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 6-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fogarty et al. (“Fogarty” 12125067 B1) in view of Ahmadi et al. (“Ahmadi” 20240096466 A1) and Wiggermann et al. (“Wiggermann” 20230114135 A1).
Claim 1: Fogarty discloses a computer-implemented method comprising: receiving, by one or more processors, data associated with a first entity from a plurality of data sources, wherein the data includes a control dataset and at least one of a system dataset or a non-system dataset (Figure 8, Column 22, Lines 4-12; multiple database(dataset) sources);
and initiating, by the one or more processors, performance of one or more mitigation actions based on the comparison (Fogarty: Column 42, Lines 25-35).
Fogarty may not explicitly disclose the feature below, Ahmadi is provided because it discloses a method for reducing burnout utilizing a machine learning model (Paragraph 25 and 67). Ahmadi discloses determining, by the one or more processors via input of a first subset of the control dataset into a first machine learning model (Ahmadi: Paragraphs 13 and 25; dataset provided to ML), a classification of the first entity (Ahmadi: Paragraph 54; classification of caregiver regarding burnout);
determining, by the one or more processors via input of at least one of the system dataset into a second machine learning model or the non-system dataset into a third machine learning model (Ahmadi: Paragraph 13; data provided to one or more ML models), at least one of a system score for the first entity or a non-system score for the first entity, respectively, each of the system score and the non-system score representing a likelihood of burnout of the first entity (Ahmadi: Paragraph 13; score assist in determining burnout);
determining, by the one or more processors via input of at least one of the system score, the non-system score, or a second subset of the control dataset into a fourth machine learning model, a composite score for the first entity, the composite score representing a likelihood of turnover of the first entity (Ahmadi: Paragraph 13-14 (score determined from proprietary data)and 57-58; overall and sub-score determined from the one or more ML); determining, by the one or more processors, at least one of a lateral score or a longitudinal score for the first entity based on at least one of the classification of the first entity or the composite score for the first entity (Ahmadi: Paragraph 58 (overall/composite score)); comparing, by the one or more processors, at least one of the lateral score or the longitudinal score with a pre-determined threshold (Ahmadi: Paragraph 25; looks for reading above threshold).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide a models which determines a score for burnout/undesired outcomes in Fogarty.
One would have been motivated to provide the functionality as a way to better determine burnout causes for improved mitigation options.
Wiggermann is also provided because it discloses a method for monitoring burnout risk (abstract). Wiggermann also discloses a functionality utilizing machine learning (Paragraph 109) and providing a scoring for risk of burn out (Paragraphs 17-19 and 42-44; overall burnout risk score (lateral score or a longitudinal score)). The system further provides an alert to assist a caregiver (Paragraphs 88-89 and 116; alert for options such as time off).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide a score determination for burnout/undesired outcomes in Fogarty.
One would have been motivated to provide the functionality as a way to better determine burnout causes in order to preemptively address caregivers risk.
Claim 2: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein determining the classification of the first entity comprises: scaling the first subset of the control dataset associated with the first entity to provide an equal weight to each variable of the first subset of the control dataset; and inputting the scaled first subset of the control dataset into the first machine learning model configured to determine the classification of the first entity, wherein the first machine learning model comprises at least one of a deep embedded clustering algorithm or a K-means clustering algorithm (Fogarty: Column 52, Lines 38-2; k-means clustering and Wiggermann: Paragraphs 44, 64 and 76; weighting and scaling of data).
Claim 6: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein initiating the performance of the one or more mitigation actions comprises: determining, by the one or more processors, the one or more mitigation actions upon determining at least one of the lateral score or the longitudinal score exceeds the pre-determined threshold, wherein the one or more mitigation actions include at least one of intervention by a second entity for managing a workload of the first entity, modifying work schedules of the first entity, delegating activities determined as detrimental to mental health of the first entity, or recommending a time-off or stress-relieving activities (Ahmadi: Paragraphs 13 and 25 and Wiggermann: Paragraph 80; time off).
Claim 7: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 6, further comprising: analyzing, by the one or more processors, at least one of the lateral score or the longitudinal score to measure efficacy of the one or more mitigation actions or adherence to the one or more mitigation actions(Ahmadi: Paragraph 63; check results and make adjustment and Wiggermann: Paragraphs 17-19 and 42-44; overall burnout risk score).
Claim 8: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, further comprising: updating, by the one or more processors, at least one of the system score or the non-system score in real-time, near real-time, or on a scheduled basis to dynamically determine the likelihood of burnout of the first entity (Ahmadi: Paragraph 13 and 62; real-time analysis and Wiggermann: Paragraph 34; real-time).
Claim 9: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein the lateral score includes a comparison between one or more scores associated with the first entity and one or more scores associated with other entities within a pre-determined time threshold (Ahmadi: abstract and Paragraph 68 and Wiggermann: Paragraphs 17-19 and 42-44; overall burnout risk score).
Claim 10: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein the longitudinal score includes a comparison between multiple scores associated with the first entity (Ahmadi: Paragraph 58; sub-core provides multiple scores for comparison and Wiggermann: Paragraphs 17-19 and 42-44; overall burnout risk score).
Claim 11: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein the control dataset includes at least one of patient panel data, provider registry data, patient complexity data, staffing ratio data, specialty data, patient volume data, patient social determinants of health (SDOH) data, or patient panel race, language, and ethnicity (RLE) data (Fogarty: Column 26, Lines 14-25; race and Wiggermann: Paragraph 57; staffing issue).
Claim 12: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein the system dataset includes at least one of electronic health record (EHR) data, clinical data, national provider identifier (NPI) data, intervention adherence data, net prompter score (NPS) data, historical training data, or task data (Fogarty: Column 48, Lines 44-55; medical history and Wiggermann: Paragraph 57; staffing experience knowledge).
Claim 13: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein the non-system dataset includes at least one of environmental data, social determinants of health (SDOH) data, financial data, health condition data, or activity data (Fogarty: Column 48, Lines 44-55; medical history and Wiggermann: Paragraph 56; workload).
Claim 14: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, wherein at least one of the first, the second, or the third machine learning model is at least one of Gradient Boosting Machine (GBM), LightGBM (LGBM), or Extra Trees Classifier (Fogarty: Column 42, Lines 1-5).
Claims 15 and 20 are similar in scope to claim 1 and therefore rejected under the same rationale.
Processor (Ahmadi: Paragraph 13)
Non-transitory (Ahmadi: Paragraph 13)
Claim 16 is similar in scope to claim 2 and therefore rejected under the same rationale.
Claims 3-5 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fogarty et al. (“Fogarty” 12125067 B1), Ahmadi et al. (“Ahmadi” 20240096466 A1) and Wiggermann et al. (“Wiggermann” 20230114135 A1) in further view of MacManus et al. (“MacManus” 12230372 B2).
Claim 3: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, further comprising training the second machine learning model by: pre-processing a training system dataset to generate a first master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the first master training dataset into a first training dataset and a first validation dataset; and inputting the first training dataset into the second machine learning model to determine a training system score, wherein one or more performance parameters of the second machine learning model are measured using the first validation dataset (Fogarty: Column 23, Line 20-Column 24, Line 7 and Ahmadi: Paragraph 56-57; provides analysis where the one or more machine learning functions takes feature).
MacManus is further provided to disclose a correlation functionality of data which also utilizes a validation dataset for a plurality of machine learning models (Claim 1).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide validation datasets for training of models in the modified Fogarty.
One would have been motivated to provide the functionality as a way to better analyze data for a more optimized model.
Claim 4: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, further comprising training the third machine learning model by: pre-processing a training non-system dataset to generate a second master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the second master training dataset into a second training dataset and a second validation dataset; and inputting the second training dataset into the third machine learning model to determine a training non-system score, wherein one or more performance parameters of the third machine learning model are measured using the second validation dataset(Fogarty: Column 23, Line 20-Column 24, Line 7 and Ahmadi: Paragraph 56-57; provides analysis where the one or more machine learning functions takes feature).
MacManus is further provided to disclose a correlation functionality of data which also utilizes a validation dataset for a plurality of machine learning models (Claim 1).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide validation datasets for training of models in the modified Fogarty.
One would have been motivated to provide the functionality as a way to better analyze data for a more optimized model.
Claim 5: Fogarty, Ahmadi and Wiggermann disclose a computer-implemented method of claim 1, further comprising training the fourth machine learning model by: pre-processing at least one of a training system score, a training non-system score, or a subset of a training control dataset to generate a third master training dataset, wherein the pre-processing includes at least one of correlation detection, missing value treatment, outlier treatment, new features creation, scaling, or multivariate analysis; splitting the third master training dataset into a third training dataset and a third validation dataset; and inputting the third training dataset into the fourth machine learning model to determine a training composite score, wherein one or more performance parameters of the fourth machine learning model are measured using the third validation dataset(Fogarty: Column 23, Line 20-Column 24, Line 7 and Ahmadi: Paragraph 56-57; provides analysis where the one or more machine learning functions takes feature).
MacManus is further provided to disclose a correlation functionality of data which also utilizes a validation dataset for a plurality of machine learning models (Claim 1).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide validation datasets for training of models in the modified Fogarty.
One would have been motivated to provide the functionality as a way to better analyze data for a more optimized model.
Claim 17-19 are similar in scope to claims 3-5 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20160364822 A1 ABSTRACT
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148 6-20-2026
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