DETAILED ACTION
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 .
Status of Claims
This Nonfinal Office Action is in response to the Application filed 02/28/2025. Claims 1-20 are pending and considered herein.
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 they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract idea is not emboldened:
A system comprising: one or more processors; and one or more hardware-based non-transitory computer-readable memory devices storing instructions which, when executed by the one or more processors, cause the system to:
create a timeline from medical histories of patients in a dataset, in which data for past events of interest for patients are included on the timeline; implement a rolling timebound window into which a portion of the data for the past events is selectively captured as a snapshot of the medical histories of the plurality of patients; transform the snapshot by rolling the window along the timeline to selectively capture data at different points along the timeline to thereby generate multiple snapshots of the patient medical histories, wherein each snapshot comprises a prediction window and a lookback window preceding the prediction window; create a training set using data from the lookback window and the prediction window of a first snapshot of the multiple snapshots of the patient medical histories; train a machine learning model using the training set; and validate the machine learning model utilizing data from the prediction window of a second snapshot of the multiple snapshots of the patient medical histories.
Independent claims 8 and 15 recite substantially similar limitations. The claimed invention is broadly directed to the abstract idea of collecting patient information, analyzing the information, and determining a timeline and predictions related to the patient based on the analyses.
The limitations to “create a timeline from medical histories of patients in a dataset, in which data for past events of interest for patients are included on the timeline; implement a rolling timebound window into which a portion of the data for the past events is selectively captured as a snapshot of the medical histories of the plurality of patients; transform the snapshot by rolling the window along the timeline to selectively capture data at different points along the timeline to thereby generate multiple snapshots of the patient medical histories, wherein each snapshot comprises a prediction window and a lookback window preceding the prediction window; create a training set using data from the lookback window and the prediction window of a first snapshot of the multiple snapshots of the patient medical histories; and validate the model utilizing data from the prediction window of a second snapshot of the multiple snapshots of the patient medical histories,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic computer system, processor, memory and machine learning model, in the context of this claim, the judicial exception is found that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing users to access patient data, analyze the data, and generate predictions based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a patient and caregiver. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic computer system, processors, memory, IoT devices and computer devices does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The claims also recited an abstract idea including mental processes. But for the generic reciting of a computer system, processor, memory and training a machine learning model, nothing in the claims is precluded from being performed in the mind. For example, a physician can collect the patient data and analyze the medical history and events and determine/predict if there is some risk or need or not for the patient based on the analyses. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic computer system, processor, memory and machine learning model. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient analysis and general diagnostic techniques between a clinician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of being implemented by a generic computer system, processor, memory and machine learning model amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claim 2 describes events and further narrows the abstract idea. Claims 3-5, 7 and 20 further detail the machine learning model, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning model does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 6 describes a user interface at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the user interface does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 9-14 and 16-18 describe the timeline windows and sampling bias and further limit the abstract idea. Claim 19 describes events of interest and limits the abstract idea.
Therefore, the claims are not patent eligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2016/0328526 A1 to Park et al., hereinafter “Park,” in view of U.S. 2014/0278547 A1 to Wickert et al., hereinafter “Wickert” and further in view of U.S. 11,043,289 B2 to Thomas et al., hereinafter “Thomas.”
Regarding claim 1, Park discloses A system comprising: one or more processors; and one or more hardware-based non-transitory computer-readable memory devices storing instructions which, when executed by the one or more processors (See Park at least at Paras. [0038]-[0040], [0074], [0087]; Claim 17; Figs. 1, 2) , cause the system to: create a timeline from medical histories of patients in a dataset, in which data for past events of interest for patients are included on the timeline (See id. at least at Paras. [0039]-[0042] (medical histories, events and timeline); Figs. 6, 13-23); implement a rolling timebound window into which a portion of the data for the past events is selectively captured as a snapshot of the medical histories of the plurality of patients (See id. at least at Paras. [0005]-[0008] (“an aggregated snapshot of data, e.g., claims from a certain time period of time”), [0047]-[0050], [0057]-[0059] (rolling window); Figs. 6, 13, 15, 18-23); transform the snapshot by rolling the window along the timeline to selectively capture data at different points along the timeline to thereby generate multiple snapshots of the patient medical histories, wherein each snapshot comprises a prediction window and a lookback window preceding the prediction window (See id. at least at Paras. [0005]-[0008] (medical events, future predictions), [0040]-[0042] (“To address this issues, the data transformer 212 merges such multiple sources to construct a member-level view of the data, preferably by using a flexible data representation (such as a JSON format), together with the notion of an “episode” that combines multiple claim items into one.”), [0047]-[0050], [0057]-[0069] (rolling windows, future and previous history/data); Figs. 6-8, 13-23).
Park may not specifically describe but Wickert teaches to create a training set using data from the lookback window and the prediction window of a first snapshot of the multiple snapshots of the patient medical histories (See Wickert at least at Abstract; Paras. [0016], [0022]-[0025], [0030]-[0031] (“[A] fixed time window extending from the date of a given type of medical event until 14 days after it. For each instance of that type of medical event in the training data, all of the ICD9 diagnostic codes (and/or all categorical data, such as all relevant patient categorical data in the relevant high-level construct) in the data that fall within the fixed-length window (e.g., defined, fixed variable, etc.) would be included in the WoE table.”); Claims 4, 5; Figs. 1-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park to incorporate the teachings of Wickert and provide certain training data and timelines. Wickert is directed to a system for healthcare predictions using medical history. Incorporating the healthcare prediction techniques of Wickert with the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction
The references may not specifically describe but Thomas teaches to train a machine learning model using the training set (See Thomas at least at Col. 9, ln. 10 – Col. 10, ln. 31; Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 8, ln. 57; Figs. 1-10); and validate the machine learning model utilizing data from the prediction window of a second snapshot of the multiple snapshots of the patient medical histories (See Thomas at least at Col. 9, ln. 10 – Col. 10, ln. 31; Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 8, ln. 57; Col. 18, ln. 8-56 (“[T]he feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain training data and timelines and machine learning and validation. Thomas is directed to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 2, Park as modified by Wickert and Thomas teaches the limitations of claim 1, and Park further discloses the past events of interest indicate disease progression or change of therapy and the dataset of selectively captured data comprises positive past events of interest (See Park at least at Paras. [0007]-[0008], [0044], [0048]-[0050] (episodic progression), [0057]-[0059]; Figs. 12-16).
Regarding claim 3, Park as modified by Wickert and Thomas teaches the limitations of claim 1, and Park further discloses wherein: the multiple snapshots of patient medical histories enable analysis of a particular patient at multiple different points along the timeline (See Park at least at Paras. [0005]-[0008] (medical events, future predictions), [0040]-[0050], [0057]-[0069] (rolling windows, future and previous history/data); Figs. 6, 13-23), the particular patient diagnosed as having a rare disease (See id. at least at Paras. [0040], [0057]-[0059], [0068]-[0069]); and the instructions further cause the system to use the machine learning model (See Thomas supra) to identify other patients with the rare disease (See id. at least at Paras. [0040]-[0041], [0045], [0052], [0062]-[0064], [0069]; Figs. 6-8, 17).
Regarding claim 4, Park as modified by Wickert and Thomas teaches the limitations of claim 3, and Park further discloses wherein the transformation comprises utilizing the generated multiple snapshots to increase sample size due to scarcity of other patients diagnosed as having the rare disease (See id. at least at Paras. Paras. [0005]-[0008] (“an aggregated snapshot of data, e.g., claims from a certain time period of time”) (medical events, future predictions), [0040]-[0050], [0057]-[0069] (rolling windows, multiple snapshots); Figs. 6-8, 13-23).
Regarding claim 5, Park as modified by Wickert and Thomas teaches the limitations of claim 1, and Thomas further teaches using the multiple snapshots of the patient medical histories in the machine learning model to predict one or more future events of interest (See Thomas at least at Col. 17, ln. 26 – Col. 20, ln. 34 (Prediction phase. “The combined machine learning model 806 can provide one or more weighted predictions 808. For instance, the one or more weighted predictions 808 can be one or more weighted predictions for the patient census data associated with a prediction for a total number of patient identities.”); Figs. 5-10; See also Wickert at least at Paras. [0016] (predictions using medical history data), [0019]-[0026], [0033]-[0039]; Claims 1, 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain training data and timelines (snapshots) and machine learning and validation. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 6, Park as modified by Wickert and Thomas teaches the limitations of claim 5, and Park further discloses storing the predictions in a destination system that includes a user interface configured to enable users to interact with the stored predictions (See Park at least at Paras. [0038], [0043]-[0047], [0066]-[0069]; Figs. 1-2, 18-23).
Regarding claim 7, Park as modified by Wickert and Thomas teaches the limitations of claim 1, and Thomas further teaches wherein past events of interest occurring within the lookback and prediction windows of one or more of the multiple snapshots are utilized for training of the machine learning model (See Thomas at least at Col. 3, ln. 31-65; Col. 5, ln. 41-64 (forecasting component and historical data); Col. 9, ln. 10 – Col. 10, ln. 31; Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 18, ln. 57 (“[T]he feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claims 8 and 15, claims 8 and 15 recite substantially the same limitations as recited in independent claim 1. Therefore, the claims are rejected under the same grounds of rejection and for the same reasoning as applied to claim 1, above.
Regarding claim 9, Park as modified by Wickert and Thomas teaches the limitations of claim 8, and Thomas further teaches the future prediction window is an element of a cross-section further comprising a lookback window, the lookback window preceding the future prediction window on the timeline, in which past clinical events occurring within the lookback window preceding the future prediction window are extracted and applied to the machine learning model to predict the occurrence of a future clinical event of interest within the future prediction window (See id. at least at Col. 3, ln. 31-65; Col. 5, ln. 41-64 (forecasting component and historical data); Col. 9, ln. 10 – Col. 10, ln. 31 (“the monitoring engine component 106 can include a prediction component that employs data (e.g., real time data and/or historical data) to monitor a current state of the set of medical inpatient units. Additionally, the monitoring engine component 106 can facilitate prediction of emerging census patterns […] the monitoring engine component 106 can employ other classification techniques associated with Bayesian networks, decision trees and/or probabilistic classification models.”); Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 18, ln. 57 (“The real-time data 526 can include medical data, sensor data, process data (e.g., process log data), monitoring data, maintenance data, parameter data, measurement data, performance data, textual data, audio data, image data, video data, machine data, asset data, equipment data, medical device data, meter data, real-time data, historical data and/or other data. […] the feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 10, Park as modified by Wickert and Thomas teaches the limitations of claim 8, and Thomas further teaches each cross-section further comprises an offset window immediately preceding the prediction window on the timeline, each cross-section comprising a section of the timeline having a predetermined length, in which the offset window is chosen to accommodate time lags in data collection from the data sources (See id. at least at Col. 3, ln. 31-65; Col. 5, ln. 41-64 (forecasting component and historical data); Col. 9, ln. 10 – Col. 10, ln. 31 (“the monitoring engine component 106 can include a prediction component that employs data (e.g., real time data and/or historical data) to monitor a current state of the set of medical inpatient units. Additionally, the monitoring engine component 106 can facilitate prediction of emerging census patterns […] the monitoring engine component 106 can employ other classification techniques associated with Bayesian networks, decision trees and/or probabilistic classification models.”); Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 18, ln. 57; Col. 18, ln. 8-56 (“[T]he feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 13, Park as modified by Wickert and Thomas teaches the limitations of claim 8, and Thomas further teaches in which the executed instructions further cause the computing device to utilize the final dataset to validate the machine learning model or test the machine learning model (See Thomas at least at Col. 9, ln. 10 – Col. 10, ln. 31; Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 18, ln. 57; Col. 18, ln. 8-56 (“[T]he feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 14, Park as modified by Wickert and Thomas teaches the limitations of claim 8, and Thomas further teaches the executed instructions further cause the computing device to use different portions of the final dataset for training to thereby compensate for drift of the machine learning model or bias in the machine learning model (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 16, Park as modified by Wickert and Thomas teaches the limitations of claim 15, and Park further discloses the different windows on the timeline have offset start times and end times on the timeline and the events of interest relate to one of disease progression or change of therapy (See Park at least at Paras. [0005]-[0008] (“an aggregated snapshot of data, e.g., claims from a certain time period of time”), [0047]-[0050], [0057]-[0059] (rolling window); Figs. 6-23).
Regarding claim 17, Park as modified by Wickert and Thomas teaches the limitations of claim 15, and Park further discloses each of the different windows comprise a lookback window and a prediction window, in which the lookback windows precede the respective prediction windows on the timeline (See id.). While Thomas teaches wherein events of interest occurring in the lookback and prediction windows are utilized by the machine learning model for training (See Thomas at least at Col. 9, ln. 10 – Col. 10, ln. 31; Col. 13, ln. 9-62; Col. 16, ln. 1 – Col. 8, ln. 57; Col. 18, ln. 8-56 (“[T]he feature engineering process 708 can employ a training phase of a machine learning process to generate the training data 704. Additionally or alternatively, the feature engineering process 708 can employ a prediction phase of a machine learning process to generate the validation data set 706. In certain embodiments, transformed data 710 (e.g., transformed data 710 stored in a machine learning database) can be employed by a feature selection process 712. The transformed data 710 can be, for example, a transformed version of real-time data and/or a transformed version of patient flow data.”); Figs. 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Regarding claim 18, Park as modified by Wickert and Thomas teaches the limitations of claim 17, and Wickert further teaches each of the different windows further comprises an offset window between the lookback window and the prediction window on the timeline, in which the offset window provides a predetermined time lag to the prediction window such that the machine learning model is enabled to predict an event of interest by an amount of time equal to the size of the offset window (See Wickert at least at Paras. [0016]-[0019], [0022]-[0025]; Figs. 1-7) .
Regarding claim 19, Park as modified by Wickert and Thomas teaches the limitations of claim 15, and Wickert further teaches the events of interest are expressed using positive and negative indicators, and in which a positive indicator comprises one of an initiation or escalation of a therapy, a clinical procedure, a diagnosis, or any medical event captured by the available data, or a combination thereof (See Wickert at least at Paras. [0016]-[0025], [0028], [0030], [0034]; Claim 2).
Regarding claim 20, Park as modified by Wickert and Thomas teaches the limitations of claim 15, and Thomas further teaches storing predictions from operations of the machine learning model in a destination system that is configured to interface with one or more computing device users to enable review and analysis of the predictions (See Thomas at least at Col. 9, ln. 60 – Col. 11, ln. 31; Col. 16, ln. 1 – Col. 18, ln. 57; Figs. 1-11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park and Wickert to incorporate the teachings of Thomas and provide certain prediction windows and certain times and determinations. Thomas relates to monitoring and alerting census periods for medical inpatient units. Incorporating the healthcare prediction techniques of Wickert with the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over Park, in view of Wickert, in view of Thomas and further in view of U.S. 2019/0311807 A1 to Kannan et al., hereinafter “Kannan.”
Regarding claim 11, Park as modified by Wickert and Thomas teaches the limitations of claim 8. The references may not specifically describe but Kannan teaches the final dataset having data captured at multiple different points along the timeline has reduced sampling bias relative to the initial dataset (See Kannan at least at Paras. [0101]-[0104] (“Sources of bias in the samples that drive the models may be quantified, and the models may be corrected for known and estimated bias. The models may incorporate disease and symptom prevalence in the general population, and may include how those statistics vary in specific subpopulations by demographic, geography, calendar, social connections, etc.”); Figs. 1, 12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park, Wickert and Thomas to incorporate the teachings of Kannan and provide reducing sampling bias. Kannan is directed to systems for responding to healthcare inquiries. Incorporating the responses and sampling bias of Kannan with the healthcare prediction techniques of Wickert, the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Park, in view of Wickert, in view of Thomas, in view of Kannan and further in view of U.S. 2021/0064929 A1 to Trim et al., hereinafter “Trim.”
Regarding claim 12, Park as modified by Wickert, Thomas and Kannan teaches the limitations of claim 11, and Trim further teaches the sampling bias results from one of seasonality, changes in data coverage, or changes in market conditions (See Trim at least at Paras. [0023]-[0025], [0043]-[0045]; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Park, Wickert, Thomas and Kannan to incorporate the teachings of Trim and provide sampling bias from different sources. Trim is directed to preventing unwanted model training data. Incorporating the techniques for removing unwanted model training data as in Trim with the responses and sampling bias of Kannan, the healthcare prediction techniques of Wickert, the monitoring and alerting events of Thomas and the case management system using a medical event forecasting engine as in Park would thereby improve the functionality and applicability of the claimed system for timed medical event prediction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682
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