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 .
Introduction
The following is a final Office action in response to Applicant’s submission filed on 4/7/2026. Currently claims 1, 3-10, 12-22, are pending and claims 1, 10, 19 are independent. Claims 1, 3, 5-10, 12-20 have been amended from the previous claim set dated 12/29/2023. Claims 2 and 11 have been cancelled and claims 21 and 22 are new.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action. In light of the amendments (and arguments), the 35 USC § 101 rejections are withdrawn. Specifically, the included amendments reflect a technological improvement to machine learning in line with guidance contained within MPEP 2106.04 (as updated according to Ex Parte Desjardins), particularly applying a correction mechanism in response to detected performance drift.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (US 20240013928 A1) in view of Meaker et al. (US 20190303758 A1) further in view of Hordan et al. (US 20250259749 A1)
Regarding claims 1, 10, and 19 (Amended), Khan discloses a computer-implemented method (Khan ABS - Systems and method for determining patient prioritization scores for use by healthcare professionals in administering care to patients), the method comprising: receiving, by one or more processors, a first data object (Khan ¶111 - receiving the healthcare data at act 302 includes receiving the healthcare data from a healthcare data store (e.g., healthcare data store 270). For example, the healthcare data may have previously been uploaded, entered, or otherwise provided, and stored in the healthcare data store), the first data object including: a member data set containing a plurality of members (Khan ¶3 - receiving healthcare data for each particular patient of the plurality of patients); a first classification data set; a second classification data set; and a plurality of data sets associated with one or more metrics (68 - As nonlimiting examples, the healthcare data 104 may include demographic data (e.g., age, sex race, ethnicity, nationality, nativity/time in a country, language, etc.) {i.e. classifications}, socioeconomic data (e.g., household income, education, employment, food insecurity, housing insecurity, crime prevalence, transportation issues, household composition (e.g., whether a patient lives alone), etc.) {i.e. second classifications}, attitudes and values (e.g., risk tolerance, attitudes about insurance and doctors, past experiences with healthcare providers, etc.), community data (e.g., urban-rural location of residence, point of care, etc.), access to care (e.g., public and private insurance coverage eligibility, usual source of care, unmet need, etc.), diagnoses, social history, family history, medical visit data, radiology, procedures, lab values, lab trends, lab deltas, medications, follow up, genetic markers, exercise habits, shopping habits, patient survey answers, diet, disabilities and impairments, mental health history, addiction treatment history, medical expense data, and other clinical and lifestyle information. In some embodiments, healthcare data 104 may vary depending on patient 102); generating, by the one or more processors and for each member of the plurality of members: a usage indicator for a pre-determined time period based on at least one of the member data set or one or more of the plurality of data sets associated with the one or more Khan ¶191 - Pre-processing the cost data included determining a 12-month cost for each patient. The 12-month cost is determined based on costs accrued during a length of stay. Because the patients' length of stay varied, so did the costs available for determining the 12-month costs. For example, sometimes only 1 month's cost is available for a patient, while other times, 12 months' cost is available. To account for this variability, the 12-month cost for each patient was determined by determining 1 month's cost multiplied by 12); and a usage rate for the pre-determined time period based on the usage indicator and at least one of the member data set or one or more of the plurality of data sets associated with the one or more metrics (Khan ¶191 - Pre-processing the cost data included determining a 12-month cost for each patient. The 12-month cost is determined based on costs accrued during a length of stay. Because the patients' length of stay varied, so did the costs available for determining the 12-month costs. For example, sometimes only 1 month's cost is available for a patient, while other times, 12 months' cost is available. To account for this variability, the 12-month cost for each patient was determined by determining 1 month's cost multiplied by 12); generating, by the one or more processors and for each member of the plurality of members, based at least on the first classification data set, the second classification data set, the usage indicator, and the usage rate, a member optimization parameter metrics (Khan ¶57 - in some embodiments, the techniques include determining a Patient Prioritization Score (PPS) for each of one or more patients. In some embodiments, the PPS is indicative of the degree to which a patient should be prioritized with respect to receiving healthcare services...When administering care, Providers may prioritize administration of care to those patients with a high PPS, as opposed to those patients with a low PPS. Accordingly, in some embodiments, it may be helpful to determine a PPS for each of multiple prospective patients, and then to rank those patients according to the determined PPS s); generating, by the one or more processors, based at least on the usage indicator, the usage rate, and the member optimization parameter for each member of the plurality of members, a plurality of cluster data objects, wherein members of each cluster data object are unique from members of any other cluster data object (Khan ¶150 - In some embodiments, care gaps are grouped to form a care gap group. In some embodiments, a care gap group covers a specific set of patients or patient profiles {i.e. cluster}. For example, a care gap group may include care gaps associated with a particular diagnosis such as diabetes. In some embodiments, each care gap in the care gap group is associated with a rule for determining whether the patient has that particular care cap. For example, a care gap group for diabetic patients may include care gaps associated with the following rules to be applied to a healthcare data for a diabetic patient: (a) at least 2 outpatient encounters with a diagnosis of diabetes over two years; (b) at least 1 inpatient encounter over 2 years; (c) prescription for diabetes medications other than Metformin, and (d) HbA1c is available and >6.5%... At act 412, a care gap score is determined based on the individual score(s) provided to the care gap(s) at act 406 and the weights applied at act 408); and causing, by the one or more processors, at least one of the plurality of cluster data objects to be displayed on a Graphical User Interface (GUI) (Khan ¶24 - The method comprises generating a graphical user interface (GUI) including a prompt to a healthcare professional to administer a level of care to a patient and providing the GUI to a display. The prompt, included in the GUI, to the healthcare professional to administer the level of care to the patient is determined based on a patient prioritization score of the patient).
Khan lacks generating the usage indicator is performed using a first machine-learning model trained to identify associations between prior usage of a resource and future usage of the resource and monitoring, by the one or more processors, a performance of the first machine- learning model as a new data object is received; detecting, by the one or more processors and based on the monitoring, parameters associated with the new data object having a threshold difference from parameters associated with the training data that indicates a drift, the drift affecting the performance of the first machine-learning model; and in response to detecting the drift, automatically applying, by the one or more processors, a correction mechanism to optimize the performance of the first machine- learning model, the correction mechanism including a retraining of the first machine- learning model based on the parameters associated with the new data object.
Meaker, from the same field of endeavor, teaches generating the usage indicator is performed using a first machine-learning model trained to identify associations between prior usage of a resource and future usage of the resource (Meaker ¶66 - Referring to the DTOC group 144, this comprises an ETL module 66 for passing the received, and classified, real-time data into a DTOC database. A first learning model 68 then generates the predicted resources and predicted length of stay data sets 72, 74. These data sets 72, 74 are then passed to another, second learning model 76 which generates or designs a combined care package 78 based on the combination of data sets 72, 74. Another ETL module 80 then passes the predicted care package to the procurement module 50 for allocation. The first model 68 may also be fed historical (not real-time) data from an ETL historical data module 70, which may comprise one or more sets of training data for training said first model. The way in which the historical data is captured may use modules similar to those shown in the real-time data capture group 142, albeit the data being stored for later use rather than provided in real-time) and monitoring, by the one or more processors, a performance of the first machine- learning model as a new data object is received; detecting, by the one or more processors and based on the monitoring, parameters associated with the new data object having a threshold difference from parameters associated with the training data that indicates a drift, the drift affecting the performance of the first machine-learning model (Meaker ¶47 - Training may involve supervised or unsupervised learning. Supervised learning involves providing both input and desired output data, and the neural network then processes the inputs, compares the resulting outputs against the desired outputs, and propagates the resulting errors back through the neural network causing the weights to be adjusted with a view to minimizing the errors iteratively. When an appropriate set of weights are determined, the neural network is considered trained); and in response to detecting the drift, automatically applying, by the one or more processors, a correction mechanism to optimize the performance of the first machine- learning model, the correction mechanism including a retraining of the first machine- learning model based on the parameters associated with the new data object (Meaker ¶49 - For this purpose, one or more sets of training data maybe inputted to the neural network, the training data being historical data relating to the same or similar real-world events. The actual outcomes, i.e. durations and one or more needed resources, resulting from the event, may be fed back to the neural network in order to 30 improve its accuracy, which feedback may be iteratively performed over time to further improve accuracy. The feedback may be provided one or more times before the duration end to update the model and to modify allocations, if needed).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the resource allocation techniques of Meaker because Meaker discloses “Embodiments herein, if employed in healthcare, enable reduction of delayed transfers of care (DTOC) from hospitals, currently an issue of escalating concern. The resultant effect of these delays on patients is poorer outcomes, and for older patients in particular, increases the risk of readmission. (Meaker ¶82)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional resource allocation techniques that Meaker discloses because it would help reduce negative patient outcomes within the patient prioritization system of Khan by additionally considering/reducing delays in care.
Khan further lacks member optimization parameter generated in part by identifying one or more events likely to occur for the respective member that are preventable or at least modifiable to utilize less resources; the plurality of cluster data objects are associated with a similar likelihood of reducing resource utilization via one or more interventions that prevent or at least modify the one or more events to utilize less resources, and the subset of the plurality of members of each cluster data object are unique from one another.
Hordan, from the same field of endeavor, teaches member optimization parameter generated in part by identifying one or more events likely to occur for the respective member that are preventable or at least modifiable to utilize less resources (Hordan ¶7 - The presently disclosed subject matter further includes computerized methods and systems dedicated to automatic health risks stratification of large population of patients for determining a respective health risk of the patients in the population, which enables to identify patients that exhibit high risk of a medical-care event (e.g., hospitalization risk) and provide urgent interventions to avoid or reduce the likelihood of the occurrence of such an event); the plurality of cluster data objects are associated with a similar likelihood of reducing resource utilization via one or more interventions that prevent or at least modify the one or more events to utilize less resources, and the subset of the plurality of members of each cluster data object are unique from one another (Hordan ¶231 - For example, patients' health-risks stratification module 123, can be configured to manage the process where personal medical data of multiple patients of a certain population (cohort) is processed to determine a respective patient's risk score for each patient and provide the classification of the patients according to their assigned risk-score and other risk related attributes (e.g., affinity groups based on location, age groups, health quality metrics, etc.))
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the patient health risk techniques of Hordan because Hordan discloses “identify patients that exhibit high risk of a medical-care event (e.g., hospitalization risk) and provide urgent interventions to avoid or reduce the likelihood of the occurrence of such an event (Hordan ABS)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional patient health risk techniques that Hordan discloses because it would improve the system of Khan by identifying patients that would benefit from particular preventative care.
Regarding claim 20, Khan in view of Meaker further in view of Hordan discloses a computer-implemented method (Khan ABS - Systems and method for determining patient prioritization scores for use by healthcare professionals in administering care to patients).
Meaker further teaches adjusting, by the one or more processors, the first machine-learning model based on one or more data objects received by the one or more processors, the adjustment resulting in the first machine-learning model being more optimally configured to identify associations between prior usage of a resource and future usage of the resource (Meaker ¶49 - For this purpose, one or more sets of training data maybe inputted to the neural network, the training data being historical data relating to the same or similar real-world events. The actual outcomes, i.e. durations and one or more needed resources, resulting from the event, may be fed back to the neural network in order to 30 improve its accuracy, which feedback may be iteratively performed over time to further improve accuracy. The feedback may be provided one or more times before the duration end to update the model and to modify allocations, if needed).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the resource allocation techniques of Meaker because Meaker discloses “Embodiments herein, if employed in healthcare, enable reduction of delayed transfers of care (DTOC) from hospitals, currently an issue of escalating concern. The resultant effect of these delays on patients is poorer outcomes, and for older patients in particular, increases the risk of readmission. (Meaker ¶82)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional resource allocation techniques that Meaker discloses because it would help reduce negative patient outcomes within the patient prioritization system of Khan by additionally considering/reducing delays in care.
Regarding claims 3 and 12, Khan in view of Meaker further in view of Hordan discloses generating the usage rate for the pre-determined time period is performed using a second machine-learning model trained to identify associations between prior usage of a resource and a rate of future usage (Khan ¶93 - In some embodiments, the future cost determination module 228 is configured to predict cost(s) associated with future care of the patients (e.g., future cost(s)). For example, the future cost determination module 228 may be configured to process at least some of the healthcare data to determine the prediction of future cost(s) associated with care of the patient. As nonlimiting examples, the future cost determination module 228 may be configured to process claims data (e.g., data used to process insurance claims) such as medical visit data, diagnoses, treatments, procedures, medications, and/or any other suitable healthcare data {i.e. past usage}. In some embodiments, the future cost determination module 228 is configured to process the healthcare data using any suitable cost risk prediction model).
Regarding claims 4 and 13, Khan in view of Meaker further in view of Hordan discloses a computer-implemented method (Khan ABS - Systems and method for determining patient prioritization scores for use by healthcare professionals in administering care to patients).
Meaker further teaches adjusting, by the one or more processors, the second machine-learning model based on one or more data objects received by the one or more processors, the adjustment resulting in the second machine-learning model being more optimally configured to identify associations between prior usage of a resource and a rate of future usage (Meaker ¶49 - For this purpose, one or more sets of training data maybe inputted to the neural network, the training data being historical data relating to the same or similar real-world events. The actual outcomes, i.e. durations and one or more needed resources, resulting from the event, may be fed back to the neural network in order to 30 improve its accuracy, which feedback may be iteratively performed over time to further improve accuracy. The feedback may be provided one or more times before the duration end to update the model and to modify allocations, if needed).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the resource allocation techniques of Meaker because Meaker discloses “Embodiments herein, if employed in healthcare, enable reduction of delayed transfers of care (DTOC) from hospitals, currently an issue of escalating concern. The resultant effect of these delays on patients is poorer outcomes, and for older patients in particular, increases the risk of readmission. (Meaker ¶82)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional resource allocation techniques that Meaker discloses because it would help reduce negative patient outcomes within the patient prioritization system of Khan by additionally considering/reducing delays in care.
Regarding claims 5 and 14, Khan in view of Meaker further in view of Hordan discloses generating, by the one or more processors, one or more labels for each cluster data object based on common member data for the members associated with the cluster data object (Khan ¶150 - In some embodiments, care gaps are grouped to form a care gap group. In some embodiments, a care gap group covers a specific set of patients or patient profiles. For example, a care gap group may include care gaps associated with a particular diagnosis such as diabetes. In some embodiments, each care gap in the care gap group is associated with a rule for determining whether the patient has that particular care cap).
Regarding claims 6 and 15, Khan in view of Meaker further in view of Hordan discloses assigning a recommended intervention for each member of the plurality of members based on at least one of the label for a cluster data object associated with the member or the usage rate for the member (Khan ¶104 - In some embodiments, the user interface module 234 may further identify actions to be taken or recommended by the healthcare provider(s) (e.g., user(s) 260) based on the PPS and level of care. For example, the generated GUI may identify treatments, procedures, medications, change of lifestyle, change in frequency of medical visits, and/or any other suitable action to be taken or recommended by the healthcare provider(s)).
Regarding claims 7 and 16, Khan in view of Meaker further in view of Hordan discloses assigning the recommended intervention for each member is further based at least in part on one or more scenario models (Khan ¶104 - In some embodiments, the user interface module 234 may further identify actions to be taken or recommended by the healthcare provider(s) (e.g., user(s) 260) based on the PPS and level of care. For example, the generated GUI may identify treatments, procedures, medications, change of lifestyle, change in frequency of medical visits, and/or any other suitable action to be taken or recommended by the healthcare provider(s)).
Regarding claims 8 and 17, Khan in view of Meaker further in view of Hordan discloses the first classification data set includes one or more indicators that one or more utilizations are avoidable (Khan ¶54 - Second, there is a wide spectrum of patients ranging from very healthy to very sick. Using resources on those who will not benefit from them (e.g., very healthy patients, terminally ill patients, uncooperative patients, etc.) is wasteful and detrimental, given the shortage of healthcare staff and significant resource constraints. For example, some patients may be at low risk for certain health conditions, less willing to follow medical guidance, and/or less likely to responsive to available care. Healthcare services are less likely to significantly impact the health outcomes of such patients {i.e. avoidable services}. Accordingly, the inventors have developed systems and methods that enable Providers to quickly identify patients most likely to be impacted by the available healthcare services, and who should be prioritized in receiving those services. For example, the techniques developed by the inventors may enable Providers to efficiently provide better or best care by prioritizing those patients who need more or most attention and are likely to be responsive to advice on how to better take care of their health).
Regarding claims 9 and 18, Khan in view of Meaker further in view of Hordan discloses the second classification data set includes one or more indicators related to an amount of resource use of one or more utilizations (Khan ¶103 - In some embodiments, the GUI includes a prompt to a healthcare provider (e.g., user(s) 260) to provide a level of care to a patient. In some embodiments, the user interface module 234 may obtain the level of care from the level of care determination module 240 and include an indication of the level of care in the GUI. For example, the user interface module 234 may generate a GUI that includes a list of patients in the order in which they should be prioritized based on PPS. As another example, the GUI may indicate the level of care in terms of a scale (e.g., a scale of low to high, a numeric scale)).
Regarding claim 21, Khan in view of Meaker further in view of Hordan discloses a computer-implemented method (Khan ABS - Systems and method for determining patient prioritization scores for use by healthcare professionals in administering care to patients).
Meaker further teaches the correction mechanism including the retraining of the first machine-learning model is a first correction mechanism applied when the detected drift is below a threshold, the retraining including one or more of model parameter modifications, weight adjustments, or feature recalibrations (Meaker ¶47 - Training may involve supervised or unsupervised learning. Supervised learning involves providing both input and desired output data, and the neural network then processes the inputs, compares the resulting outputs against the desired outputs, and propagates the resulting errors back through the neural network causing the weights to be adjusted with a view to minimizing the errors iteratively. When an appropriate set of weights are determined, the neural network is considered trained).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the resource allocation techniques of Meaker because Meaker discloses “Embodiments herein, if employed in healthcare, enable reduction of delayed transfers of care (DTOC) from hospitals, currently an issue of escalating concern. The resultant effect of these delays on patients is poorer outcomes, and for older patients in particular, increases the risk of readmission. (Meaker ¶82)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional resource allocation techniques that Meaker discloses because it would help reduce negative patient outcomes within the patient prioritization system of Khan by additionally considering/reducing delays in care.
Regarding claim 22, Khan in view of Meaker further in view of Hordan discloses a computer-implemented method (Khan ABS - Systems and method for determining patient prioritization scores for use by healthcare professionals in administering care to patients).
Meaker further teaches the detected drift is above the threshold, applying the correction mechanism further comprises: applying a second correction mechanism including one or more of incorporation of novel features or adjustment of hyperparameters of the first machine-learning model (Meaker ¶47 - Training may involve supervised or unsupervised learning. Supervised learning involves providing both input and desired output data, and the neural network then processes the inputs, compares the resulting outputs against the desired outputs, and propagates the resulting errors back through the neural network causing the weights to be adjusted with a view to minimizing the errors iteratively. When an appropriate set of weights are determined, the neural network is considered trained).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the patient prioritization methodology/system of Khan by including the resource allocation techniques of Meaker because Meaker discloses “Embodiments herein, if employed in healthcare, enable reduction of delayed transfers of care (DTOC) from hospitals, currently an issue of escalating concern. The resultant effect of these delays on patients is poorer outcomes, and for older patients in particular, increases the risk of readmission. (Meaker ¶82)”. Additionally, Khan further details that “In some embodiments, patient prioritization scores determined according to the techniques developed by the inventors can be used to identify those patients who should be prioritized in terms of administering healthcare and allocating healthcare resources (Khan ¶59)” so it would be obvious to consider including the additional resource allocation techniques that Meaker discloses because it would help reduce negative patient outcomes within the patient prioritization system of Khan by additionally considering/reducing delays in care.
Response to Arguments
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
As identified above and in light of the amendments (and arguments), the 35 USC § 101 rejections are withdrawn. Specifically, the included amendments reflect a technological improvement to machine learning in line with guidance contained within MPEP 2106.04 (as updated according to Ex Parte Desjardins), particularly applying a correction mechanism in response to detected performance drift.
Regarding the 35 USC § 103 rejections on the previous Office action, Applicant amended the independent claims to further limit the claims with respect to the groups of people and avoidable resource use. In light of this amendment, Examiner agrees that the original references did not clearly teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Hordan as discussed above) and is now cited. As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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