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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 11, 2026 has been entered.
Status of Claims
This office action for the 18/241511 application is in response to the communications filed February 11, 2026.
Claims 1, 15 and 18 were amended February 11, 2026.
Claims 2, 16 and 19 were cancelled February 11, 2026.
Claim 21 was added as new February 11, 2026.
Claims 1, 3-15, 17, 18, 20 and 21 are currently pending and 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, 3-15, 17, 18, 20 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for classifying medical patients, the method comprising: receiving respective medical data of a plurality of patients; classifying one or more patients of the plurality of patients into respective categories of a plurality of hierarchical categories based on the received respective medical data, wherein the received medical data comprises all three of: (i) International Classification of Diseases (ICD) codes, (ii) Health and Human Services (HHS)-Hierarchical Condition Categories, and (iii) Centers for Medicare & Medicaid Services (CMS)-HCC data; wherein the classifying comprises: determining if a patient of the plurality of patients meets criteria of a most severe health category of the plurality of hierarchical categories; and if the patient does not meet the criteria of the most severe health category, determining if the patient should be placed in a second most severe health category, predicting based on the respective category classifications of the patients into the respective categories, an organizational or clinical operational improvement. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. For example, but for the additional element(s) of “computer-implemented”, “via one or more processors”, “via the one or more processors” and “displaying, via the one or more processors, the predicted organizational or clinical operational improvement.”, the identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recite a list of rules or instructions that a human person can follow in the course of the personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“computer-implemented”, “via one or more processors”, and “via the one or more processors” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0112] of the as-filed specification describes that the hardware implementing the steps of the claims amounts to a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“displaying, via the one or more processors, the predicted organizational or clinical operational improvement.” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“displaying, via the one or more processors, the predicted organizational or clinical operational improvement.” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the received respective medical data further comprises at least one of: information of a hospice stay; dates of death; information of stays at a skilled nursing facility (SNF); information of stays at a residential boarding house (BH); Chronic Conditions Wearhouse (CCW) data; ages of the patients; numbers of emergency department (ED) visits; indications of lines of business (LOBs) being any of commercial, Medicare, or Medicare; information of a diagnosis related group (DRG); or information of a behavioral health inpatient stay.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the received respective medical data further comprises at least one of: a disease classification code, or information of a stay in a healthcare facility.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the classifying comprises: classifying…the patients of the plurality of patients into the respective categories of the plurality of hierarchical categories.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“via the one or more processors, with a trained machine learning algorithm” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: adding…the categories that the patients have been classified into to electronic medical records (EMRs) of the patients.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“via the one or more processors” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the plurality of patients are pediatric patients, and the plurality of hierarchical categories are a plurality of pediatric hierarchical categories.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein at least one category of the plurality of hierarchical categories includes a plurality of hierarchical subcategories.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the organizational improvement comprises predicting at least one of: an optimal overall staffing level of a healthcare facility; an optimal ratio of a first type of caregiver to a second type of caregiver; or an amount of financing that will be required for a hierarchical category of the plurality of hierarchical categories.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the organizational improvement comprises predicting an optimal staffing level of a particular type of caregiver, the particular type of caregiver being one of: a primary care physician (PCP); an advanced practice provider (APP); a registered nurse (RN); a medical assistant (MA); or a care manager (CM).” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 11 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the organizational improvement comprises predicting an amount of financing that will be required for a service section, and the service section comprises at least one of: ambulance and transportation; ancillary services; durable medical equipment (DME) and supplies; emergency department (ED) care; home based care; hospital inpatient care; hospital outpatient care; institutional care; lab and/or pathology; office and/or clinic care; treatment and evaluation of other sites; physical, occupational, and/or speech therapy; radiology; retail prescriptions; or specialty pharma.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 12 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 12 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the clinical operational improvement comprises predicting an optimal bundle of services for a patient of the plurality of patients by assigning the patient a bundle of services corresponding to a category classification of the patient.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 13,
Claim 13 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 13 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the clinical operational improvement comprises predicting bundles of services for patients of the plurality of patients corresponding to category classifications of the patients, and wherein the bundles comprise: (i) types of touchpoints, (ii) time durations for the types of touchpoints, and (iii) numbers of times per bundle for the types of touchpoints.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 14,
Claim 14 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 14 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein predicting the clinical operational improvement comprises modifying a care pathway and/or placing an intervention in the care pathway, the care pathway comprising a schedule of visits with healthcare providers.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reason as claim 1.
As per claim 17,
Claim 17 is substantially similar to claim 4. Accordingly, claim 17 is rejected for the same reason as claim 4.
As per claim 18,
Claim 18 is substantially similar to claim 1. Accordingly, claim 18 is rejected for the same reason as claim 1.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reason as claim 10.
As per claim 21,
Claim 21 depends from claim 18 and inherits all the limitations of the claim from which it depends. Claim 21 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the plurality of hierarchical categories comprises an end of life (EOL) category, an institutionalized category, a longitudinal needs category, a complex polychronic category, a specialty treatable category, a primary care treatable category, and a wellbeing category” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-15, 17, 18, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al. (US 2022/0336088; herein referred to as Thomas) in view of Derrick, Jr. et al. (US 2021/0319887; herein referred to as Derrick).
As per claim 1,
Thomas teaches computer-implemented method for classifying medical patients, the method comprising: receiving, via one or more processors, respective medical data of a plurality of patients; classifying, via the one or more processors, one or more patients of the plurality of patients into respective categories of a plurality of hierarchical categories based on the received respective medical data:
(Paragraphs [0006] and [0100]-[0102] of Thomas. The teaching describes a complex patient identification component which can filter the currently admitted patients based on their forecasted discharge destination, LOS, readmission risk and/or safety risks, or another defined criterion (e.g., medical complexity, diagnosis, comorbidity, etc.) to generate a smaller more refined subgroup of patients to evaluate for classification as complex needs or not. The complex patient identification component 402 can be configured to identify complex needs patients using machine learning, qualitative and/or hybrid techniques based on defined clinical and non-clinical factors extracted from the input data 104 (e.g., including parameters regarding medical complexity, socio-economic conditions, insurance, medications, behavioral characteristics, mental health characteristics, etc.). The complex patient identification component 402 can also employ machine learning methods, qualitative methods, or hybrid techniques to identify relevant clinical factors collected for a currently admitted patient (e.g., via the data collection component 112) that strongly influence the patient being classified as complex needs and/or that require clinical attention (e.g., during the patient stay and/or post-discharge). For example, the complex patient identification component 402 can identify specific care needs of the complex patient that significantly contribute (e.g., relative to other factors) to them being classified as complex (e.g., needing hemodialysis, chemotherapy, radiation therapy, wound vacuums, and mental health care needs, having a physical disability, etc.). In another example, the complex patient identification component 402 can identify clinical factors that require coordination and scheduling for the complex needs patient during and/or post-discharge (e.g., the patient needs dialysis before and after discharge along with nursing support, the patient needs oxygen before and after discharge along with equipment support etc.). These relevant factors can be predefined and/or learned using one or more machine learning techniques. In some implementations, these strong contributing factors can be given higher weight in the complex needs classification model. The system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory.)
Thomas further teaches predicting, via the one or more processors, based on the respective category classifications of the patients into the respective categories, an organizational or clinical operational improvement; and displaying, via the one or more processors, the predicted organizational or clinical operational improvement:
(Paragraphs [0104]-[0106] of Thomas. The teaching describes an example visualization 500 reporting identified complex patients and their forecasted discharge dispositions in accordance with one or more embodiments of the disclosed subject matter. Visualization 500 provides an example display that can be generated by the display component 118 based on the output data 120 as generated/determined using the techniques described. The disclosed care outcomes forecasting techniques can employ an ensemble machine learning approach that involves chaining and/or stacking of a plurality of different predictive models and employing the aggregated outputs of all the different models to converge on a final optimized prediction. With these embodiments, the respective forecasting models (e.g., the discharge destination foresting models 126, the LOS forecasting models 132, the readmission risk forecasting models 138 and/or the safety risk forecasting models 144) can include a plurality of different models respectively configured to forecast their corresponding patient care outcomes based on different combinations of clinical and/or non-clinical factors (e.g., different input parameters), using different weighting schemes for one or more of the input parameters, and/or using different types of machine learning algorithms/models. With these embodiments, the optimization component 602 can combine/aggregate and evaluate the outputs of the individual models to generate an optimized care outcome forecast. In accordance with these embodiments, the predicted care outcomes 122 can comprise optimized discharge destination forecasts, optimized discharge time/LOS forecasts 136, optimized readmission risk forecasts 142 and/or optimized safety risk forecasts 148). predictions. Information that controls how the optimization component 602 combines the aggregated care outcome predictions to generate the optimized care outcome forecasts can be defined in an optimization information 604 data store.)
Thomas does not explicitly teach wherein the received respective medical data comprises wherein the received medical data comprises all three of: (i) International Classification of Diseases (ICD) codes, (ii) Health and Human Services (HHS)-Hierarchical Condition Categories, and (iii) Centers for Medicare & Medicaid Services (CMS)-HCC data.
However, Derrick teaches wherein the received medical data comprises all three of: (i) International Classification of Diseases (ICD) codes, (ii) Health and Human Services (HHS)-Hierarchical Condition Categories, and (iii) Centers for Medicare & Medicaid Services (CMS)-HCC data:
(Paragraphs [0030], [0031], [0038], [0067] and [0234] of Derrick. The teaching describes that the presence of additional coexisting chronic conditions has a significant impact on the diagnosis, prevention, delay of the onset, treatment and management of diabetes and premature death. CVD is the leading cause of death for all people with diabetes. The Centers for Medicare and Medicaid (CMS) reports [CMS-HCC data] that its Medicare beneficiaries account for 80+ commonly coexisting triads of diabetes and other chronic diseases, of which the top 20 triads, their prevalence among all Medicare beneficiaries and the per capita Medicare spending, respectively, were: (1) diabetes & hyperlipidemia & hypertension, 29.2%, $20,071; (2) diabetes & chronic kidney disease & hypertension, 24.2%, $24,494; (3) diabetes & ischemic heart disease & hypertension, 19.9%, $25,680; (4) diabetes & chronic kidney disease & hyperlipidemia, 18.7%, $25,787; (5) diabetes & arthritis & hypertension, 18.4%, $22,920; (6) diabetes & ischemic heart disease & hyperlipidemia, 16.0%, $26,992; (7) diabetes & arthritis & hyperlipidemia, 14.13%, $24,470; (8) diabetes & chronic kidney disease & ischemic heart disease, 14.1%, $30,923; (9) diabetes & chronic kidney disease & arthritis, 12.1%, $28,308; (10) diabetes & heart failure & hypertension, 11.9%, $34,995; (11) diabetes & depression & hypertension, 10.4%, $30,710; (12) diabetes & ischemic heart disease & arthritis, 10.3%, $29,242; (13) diabetes & heart failure & chronic kidney disease, 9.7%, $38,880; (14) diabetes & heart failure & ischemic heart disease, 9.6%, $36,620; (15) diabetes & heart failure & hyperlipidemia, 9.1%, $37,502; (16) diabetes & depression & hyperlipidemia, 8.1%, $32,439; (17) COPD & diabetes & hypertension, 8.0%, $34,186; (18) diabetes & chronic kidney disease & depression, 7.3%, $36,847; (19) Alzheimer's disease/dementia & diabetes & hypertension, 6.8%, $34,489; and (20) diabetes & heart failure & arthritis, 6.5%, $37,923. Comorbidity deals with people having two or more multiple chronic diseases, multiple chronic illnesses or multiple chronic conditions (interchangeably referred to as “MCC” as the context may require). Examples of MCC include, but are not limited to, the simultaneous presence of one or more of: arthritis, asthma, chronic respiratory conditions, diabetes, heart disease, human immunodeficiency virus infection, obesity/overweight, and hypertension HHS defines chronic illnesses as “conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living”. Although presently there is no standard definition of comorbidity, the term “comorbid” generally is understood: (a) to indicate a medical condition existing simultaneously but independently with another condition in a patient and (b) to indicate a medical condition in a patient that causes, is caused by, or is otherwise related to another condition in the same patient. As a result, defining a chronic condition and MCC requires careful consideration. In the HHS MCC [HHS-HCC data] Strategic Framework, a chronic condition is defined as a condition lasting 12 or more months and requiring ongoing medical care. An embodiment may be directed to accessing, cleaning, storing, extracting, retrieving, and converting a type or plurality of types of data including by way of example, but not limited to, the following: an Indicator Of Health, a Component Of Health, and/or a Social Determinant Of Health, or a plurality of Indicators Of Health, Components Of Health, and/or Social Determinants Of Health, and/or other pattern-of-life or every-day life data, familiar to one of ordinary experience in the art, such as including, but not limited to: social determinants and/or indicators of health; the social determinants of obesity; diagnosis codes found in Medicare and Medicaid inpatient, outpatient, and physician claims files; Condition Categories found in the Hierarchical Condition Category grouper. Managing MCC is quite complex. How does one deal with complex clinical manifestations of conditions, such as signs (visually observable patient abnormalities), symptoms (abnormal perceptions of illness that only the patients can report, such as pain, itching, fatigue, depressive feelings), and syndromes (clusters of signs, symptoms, and other clinical phenomena that may or may not be indicative of a specific underlying disease)? Do these signs, symptoms, and syndromes belong in the study of MCC? These signs, symptoms, and syndromes must be carefully and systematically addressed, since many never reach the level of a specific diagnosable “disease” with an ICD code, therapy cost reimbursement codes known by one of ordinary skill in the art. Although specific ICD codes may not have been assigned to such signs symptoms and syndromes, they nevertheless can cause considerable suffering and require health care.)
Thomas further teaches wherein the classifying comprises: determining, via the one or more processors, if a patient of the plurality of patients meets criteria of a most severe health category of the plurality of hierarchical categories; and if the patient does not meet the criteria of the most severe health category, determining, via the one or more processors, if the patient should be placed in a second most severe health category:
(Paragraphs [0100]-[0102] and [0157] of Thomas. The teaching describes a complex patient identification component which can filter the currently admitted patients based on their forecasted discharge destination, LOS, readmission risk and/or safety risks, or another defined criterion (e.g., medical complexity, diagnosis, comorbidity, etc.) to generate a smaller more refined subgroup of patients to evaluate for classification as complex needs or not. The complex patient identification component 402 can be configured to identify complex needs patients using machine learning, qualitative and/or hybrid techniques based on defined clinical and non-clinical factors extracted from the input data 104 (e.g., including parameters regarding medical complexity, socio-economic conditions, insurance, medications, behavioral characteristics, mental health characteristics, etc.). The complex patient identification component 402 can also employ machine learning methods, qualitative methods, or hybrid techniques to identify relevant clinical factors collected for a currently admitted patient (e.g., via the data collection component 112) that strongly influence the patient being classified as complex needs and/or that require clinical attention (e.g., during the patient stay and/or post-discharge). For example, the complex patient identification component 402 can identify specific care needs of the complex patient that significantly contribute (e.g., relative to other factors) to them being classified as complex (e.g., needing hemodialysis, chemotherapy, radiation therapy, wound vacuums, and mental health care needs, having a physical disability, etc.). In another example, the complex patient identification component 402 can identify clinical factors that require coordination and scheduling for the complex needs patient during and/or post-discharge (e.g., the patient needs dialysis before and after discharge along with nursing support, the patient needs oxygen before and after discharge along with equipment support etc.). These relevant factors can be predefined and/or learned using one or more machine learning techniques. In some implementations, these strong contributing factors can be given higher weight in the complex needs classification model. Classification is defined as the process of recognition, understanding, and grouping of objects and ideas into preset categories a.k.a “sub-populations.” Classification models (or algorithms) used in machine learning utilize input training data for the purpose of predicting the likelihood or probability that the data that follows will fall into one of the predetermined categories. With the help of these pre-categorized training datasets, classification in machine learning programs leverage a wide range of algorithms to classify future datasets into respective and relevant categories using a supervised machine learning process. This means that higher weights correspond to higher severity categories and if a patient does not meet a threshold for a particular category, a less severe category is given as that patient’s classification.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the teaching of Thomas, the coding categories of Derrick. Paragraph [0039] of Derrick describes that HHS MCC Strategic Framework for managing has four overarching goals: foster health care and public health system changes to improve the health of individuals with multiple chronic conditions; maximize the use of proven self-care management and other services by individuals with multiple chronic conditions; provide better tools and information to health care, public health, and social services workers who deliver care to individuals with multiple chronic conditions; facilitate research to fill knowledge gaps about, and interventions and systems to benefit, individuals with multiple chronic conditions. Strategies of the framework include the stimulation of epidemiologic research to determine the most common MCC dyads and triads (terms known by one of ordinary skill in the art) and to explain more clearly the differences in MCC and the opportunities for prevention and treatment among various sociodemographic groups. One of ordinary skill in the art in possession of Thomas would have looked to the coding described in Derrick to achieve these advantages. One of ordinary skill in the art would have added to the teaching of Thomas, the teaching of Derrick based on these incentives without yielding unexpected results.
As per claim 3,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein the received respective medical data further comprises at least one of: information of a hospice stay; dates of death; information of stays at a skilled nursing facility (SNF); information of stays at a residential boarding house (BH); Chronic Conditions Wearhouse (CCW) data; ages of the patients; numbers of emergency department (ED) visits; indications of lines of business (LOBs) being any of commercial, Medicare, or Medicare; information of a diagnosis related group (DRG); or information of a behavioral health inpatient stay:
(Paragraphs [0047] and [0048] of Thomas. The teaching describes that clinical factors can include information regarding the patient's medical history prior to admission, as well as admission data regarding the reason for admission, patient status and diagnosis upon admission, initial clinical orders for the patient, an initial care plan for the patient, and the like. Non-clinical factors can include demographic factors, such patient age, gender, ethnicity, religion, language, marital status, occupation, etc.)
As per claim 4,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein the received respective medical data comprises at least one of: a disease classification code, or information of a stay in a healthcare facility:
(Paragraphs [0047] and [0048] of Thomas. The teaching describes that clinical factors can include information regarding the patient's medical history prior to admission, as well as admission data regarding the reason for admission, patient status and diagnosis upon admission, initial clinical orders for the patient, an initial care plan for the patient, and the like. Non-clinical factors can include demographic factors, such patient age, gender, ethnicity, religion, language, marital status, occupation, etc.)
As per claim 5,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein the classifying comprises: classifying, via the one or more processors, with a trained machine learning algorithm, the patients of the plurality of patients into the respective categories of the plurality of hierarchical categories:
(Paragraphs [0100]-[0102] and [0157] of Thomas. The teaching describes a complex patient identification component which can filter the currently admitted patients based on their forecasted discharge destination, LOS, readmission risk and/or safety risks, or another defined criterion (e.g., medical complexity, diagnosis, comorbidity, etc.) to generate a smaller more refined subgroup of patients to evaluate for classification as complex needs or not. The complex patient identification component 402 can be configured to identify complex needs patients using machine learning, qualitative and/or hybrid techniques based on defined clinical and non-clinical factors extracted from the input data 104 (e.g., including parameters regarding medical complexity, socio-economic conditions, insurance, medications, behavioral characteristics, mental health characteristics, etc.). The complex patient identification component 402 can also employ machine learning methods, qualitative methods, or hybrid techniques to identify relevant clinical factors collected for a currently admitted patient (e.g., via the data collection component 112) that strongly influence the patient being classified as complex needs and/or that require clinical attention (e.g., during the patient stay and/or post-discharge). For example, the complex patient identification component 402 can identify specific care needs of the complex patient that significantly contribute (e.g., relative to other factors) to them being classified as complex (e.g., needing hemodialysis, chemotherapy, radiation therapy, wound vacuums, and mental health care needs, having a physical disability, etc.). In another example, the complex patient identification component 402 can identify clinical factors that require coordination and scheduling for the complex needs patient during and/or post-discharge (e.g., the patient needs dialysis before and after discharge along with nursing support, the patient needs oxygen before and after discharge along with equipment support etc.). These relevant factors can be predefined and/or learned using one or more machine learning techniques. In some implementations, these strong contributing factors can be given higher weight in the complex needs classification model. Classification is defined as the process of recognition, understanding, and grouping of objects and ideas into preset categories a.k.a “sub-populations.” Classification models (or algorithms) used in machine learning utilize input training data for the purpose of predicting the likelihood or probability that the data that follows will fall into one of the predetermined categories. With the help of these pre-categorized training datasets, classification in machine learning programs leverage a wide range of algorithms to classify future datasets into respective and relevant categories using a supervised machine learning process. This means that higher weights correspond to higher severity categories and if a patient does not meet a threshold for a particular category, a less severe category is given as that patient’s classification.)
As per claim 6,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches further comprising: adding, via the one or more processors, the categories that the patients have been classified into to electronic medical records (EMRs) of the patients:
(Paragraph [0106] of Thomas. The teaching describes the predicted care outcomes 122 can comprise optimized discharge destination forecasts, optimized discharge time/LOS forecasts 136, optimized readmission risk forecasts 142 and/or optimized safety risk forecasts 148). predictions. Information that controls how the optimization component 602 combines the aggregated care outcome predictions to generate the optimized care outcome forecasts can be defined in an optimization information 604 data store.)
As per claim 7,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches that wherein the plurality of patients are pediatric patients, and the plurality of hierarchical categories are a plurality of pediatric hierarchical categories:
(Paragraph [0089] of Thomas. The teaching describes that patient factors include education level, occupation, income level per capita, median household income, debt, net worth, credit score, assets, home zip code, rural-urban community area (RUCA) code associated with the patient's current home location, criminal background (e.g., arrests, convictions, etc.), living family members (e.g., spouse, parents, grandparents, siblings, children, grandchildren), family member ethnicities, number of siblings, number of children, number of grandchildren, next of kin, emergency contact type, emergency contact person, and the like. These descriptors are not exclusive to adults and are construed as inclusive of child patients.)
As per claim 8,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein at least one category of the plurality of hierarchical categories includes a plurality of hierarchical subcategories:
(Paragraphs [0100]-[0102] and [0157] of Thomas. The teaching describes a complex patient identification component which can filter the currently admitted patients based on their forecasted discharge destination, LOS, readmission risk and/or safety risks, or another defined criterion (e.g., medical complexity, diagnosis, comorbidity, etc.) to generate a smaller more refined subgroup of patients to evaluate for classification as complex needs or not. The complex patient identification component 402 can be configured to identify complex needs patients using machine learning, qualitative and/or hybrid techniques based on defined clinical and non-clinical factors extracted from the input data 104 (e.g., including parameters regarding medical complexity, socio-economic conditions, insurance, medications, behavioral characteristics, mental health characteristics, etc.). The complex patient identification component 402 can also employ machine learning methods, qualitative methods, or hybrid techniques to identify relevant clinical factors collected for a currently admitted patient (e.g., via the data collection component 112) that strongly influence the patient being classified as complex needs and/or that require clinical attention (e.g., during the patient stay and/or post-discharge). For example, the complex patient identification component 402 can identify specific care needs of the complex patient that significantly contribute (e.g., relative to other factors) to them being classified as complex (e.g., needing hemodialysis, chemotherapy, radiation therapy, wound vacuums, and mental health care needs, having a physical disability, etc.). In another example, the complex patient identification component 402 can identify clinical factors that require coordination and scheduling for the complex needs patient during and/or post-discharge (e.g., the patient needs dialysis before and after discharge along with nursing support, the patient needs oxygen before and after discharge along with equipment support etc.). These relevant factors can be predefined and/or learned using one or more machine learning techniques. In some implementations, these strong contributing factors can be given higher weight in the complex needs classification model. Classification is defined as the process of recognition, understanding, and grouping of objects and ideas into preset categories a.k.a “sub-populations.” Classification models (or algorithms) used in machine learning utilize input training data for the purpose of predicting the likelihood or probability that the data that follows will fall into one of the predetermined categories. With the help of these pre-categorized training datasets, classification in machine learning programs leverage a wide range of algorithms to classify future datasets into respective and relevant categories using a supervised machine learning process. This means that higher weights correspond to higher severity categories and if a patient does not meet a threshold for a particular category, a less severe category is given as that patient’s classification.)
As per claim 9,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the organizational improvement comprises predicting at least one of: an optimal overall staffing level of a healthcare facility; an optimal ratio of a first type of caregiver to a second type of caregiver; or an amount of financing that will be required for a hierarchical category of the plurality of hierarchical categories:
(Paragraph [0155] of Thomas. The teaching describes a continuous process that can be repeatedly performed for each of the respective patients over time to generate updated forecasts as conditions change for over time that impact or control the timing of the defined clinical event being forecasted. For example, as applied to expected discharge time, many conditions related to the patient's health condition/status may change over their course of care that can influence when the patient will get discharged, as well as conditions related to the state of operations of the inpatient facility (e.g., availability of resources such as beds, staff and medical equipment/supplies, early/late discharge of other patients, admission of new higher priority patients, etc.), and availability of beds at post-discharge facilities, changes in barriers to discharge, etc. In this regard, as applied to predicting patient discharge timing and other clinical events, the care outcomes forecasting component 1700 can be configured to perform process 1800 for the respective patients once a day (i.e., once every 24 hours) or multiple times a day (e.g., a defined time points throughout the day) to generate updated forecasts for the respective patients as new longitudinal data is collected and aggregated for the respective patients over their course of stay at the inpatient facility.)
As per claim 10,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the organizational improvement comprises predicting an optimal staffing level of a particular type of caregiver, the particular type of caregiver being one of: a primary care physician (PCP); an advanced practice provider (APP); a registered nurse (RN); a medical assistant (MA); or a care manager (CM):
(Paragraph [0155] of Thomas. The teaching describes a continuous process that can be repeatedly performed for each of the respective patients over time to generate updated forecasts as conditions change for over time that impact or control the timing of the defined clinical event being forecasted. For example, as applied to expected discharge time, many conditions related to the patient's health condition/status may change over their course of care that can influence when the patient will get discharged, as well as conditions related to the state of operations of the inpatient facility (e.g., availability of resources such as beds, staff and medical equipment/supplies, early/late discharge of other patients, admission of new higher priority patients, etc.), and availability of beds at post-discharge facilities, changes in barriers to discharge, etc. In this regard, as applied to predicting patient discharge timing and other clinical events, the care outcomes forecasting component 1700 can be configured to perform process 1800 for the respective patients once a day (i.e., once every 24 hours) or multiple times a day (e.g., a defined time points throughout the day) to generate updated forecasts for the respective patients as new longitudinal data is collected and aggregated for the respective patients over their course of stay at the inpatient facility.)
As per claim 11,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the organizational improvement comprises predicting an amount of financing that will be required for a service section, and the service section comprises at least one of: ambulance and transportation; ancillary services; durable medical equipment (DME) and supplies; emergency department (ED) care; home based care; hospital inpatient care; hospital outpatient care; institutional care; lab and/or pathology; office and/or clinic care; treatment and evaluation of other sites; physical, occupational, and/or speech therapy; radiology; retail prescriptions; or specialty pharma:
(Paragraph [0122] of Thomas. The teaching describes that the optimization component 602 can also employ defined constraints regarding where and when a patient can be discharged to facilitate determining the optimized discharge time forecast 808 for a currently admitted patient. For example, the defined discharge constraints can include constraints regarding required care needs of the patient, financial constraints, insurance constraints, regulatory requirement constraints (e.g., defined service level agreements (SLAs)), family support constraints, resource constraints, known barriers to discharge, and the like. For example, a patient's insurance policy can require a patient stay a minimum of N days before discharge to a SNF to be covered. According to this example, the optimization component 602 can eliminate any LOS predictions that are less than N days if the patient has a predicted discharge destination to a SNF with a high probability (e.g., relative to a defined threshold, such as 90% or greater) and the patient is unable or unwilling to pay for the SNF services out of pocket. In various embodiments, the defined constraints can include constraints that are unique/personalized for the particular patient being evaluated.)
As per claim 12,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the clinical operational improvement comprises predicting an optimal bundle of services for a patient of the plurality of patients by assigning the patient a bundle of services corresponding to a category classification of the patient:
(Paragraph [0054] of Thomas. The teaching describes a centralized command center, or another suitable operating environment. The visualization can allow a care provider to view the predicted discharge destinations and discharge times of the currently admitted patients to facilitate discharge planning. In some implementations, the visualization can specifically identify and/or otherwise call-out patients determined to be complex needs patients, such as patients expected to be discharged to post-acute care facilities. The real-time display can also include additional information that is relevant to discharge planning, such as pending consults, barriers to discharge, insurance details, current unit location, nurse notes, care team activities, disposition trajectory, discharge barriers, and the like. In this regard, the discharge planning tile can provide a concerted view of all the information pertinent to the discharge of the patient. In some embodiments, the discharge planning application can also provide for receiving user feedback regarding discharge barriers, patient needs, scheduled consults, and other relevant information that can affect where and when a patient is discharged. This user feedback can be fed back into the discharge destination and LOS forecasting models to further update and improve the accuracy of these models over time.)
As per claim 13,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the clinical operational improvement comprises predicting bundles of services for patients of the plurality of patients corresponding to category classifications of the patients, and wherein the bundles comprise: (i) types of touchpoints, (ii) time durations for the types of touchpoints, and (iii) numbers of times per bundle for the types of touchpoints:
(Paragraph [0054] of Thomas. The teaching describes a centralized command center, or another suitable operating environment. The visualization can allow a care provider to view the predicted discharge destinations and discharge times of the currently admitted patients to facilitate discharge planning. In some implementations, the visualization can specifically identify and/or otherwise call-out patients determined to be complex needs patients, such as patients expected to be discharged to post-acute care facilities. The real-time display can also include additional information that is relevant to discharge planning, such as pending consults, barriers to discharge, insurance details, current unit location, nurse notes, care team activities, disposition trajectory, discharge barriers, and the like. In this regard, the discharge planning tile can provide a concerted view of all the information pertinent to the discharge of the patient. In some embodiments, the discharge planning application can also provide for receiving user feedback regarding discharge barriers, patient needs, scheduled consults, and other relevant information that can affect where and when a patient is discharged. This user feedback can be fed back into the discharge destination and LOS forecasting models to further update and improve the accuracy of these models over time.)
As per claim 14,
The combined teaching of Thomas and Derrick teaches the limitations of claim 1.
Thomas further teaches wherein predicting the clinical operational improvement comprises modifying a care pathway and/or placing an intervention in the care pathway, the care pathway comprising a schedule of visits with healthcare providers:
(Paragraph [0054] of Thomas. The teaching describes a centralized command center, or another suitable operating environment. The visualization can allow a care provider to view the predicted discharge destinations and discharge times of the currently admitted patients to facilitate discharge planning. In some implementations, the visualization can specifically identify and/or otherwise call-out patients determined to be complex needs patients, such as patients expected to be discharged to post-acute care facilities. The real-time display can also include additional information that is relevant to discharge planning, such as pending consults, barriers to discharge, insurance details, current unit location, nurse notes, care team activities, disposition trajectory, discharge barriers, and the like. In this regard, the discharge planning tile can provide a concerted view of all the information pertinent to the discharge of the patient. In some embodiments, the discharge planning application can also provide for receiving user feedback regarding discharge barriers, patient needs, scheduled consults, and other relevant information that can affect where and when a patient is discharged. This user feedback can be fed back into the discharge destination and LOS forecasting models to further update and improve the accuracy of these models over time.)
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reason as claim 1.
As per claim 17,
Claim 17 is substantially similar to claim 4. Accordingly, claim 17 is rejected for the same reason as claim 4.
As per claim 18,
Claim 18 is substantially similar to claim 1. Accordingly, claim 18 is rejected for the same reason as claim 1.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reason as claim 10.
As per claim 21,
The combined teaching of Thomas and Derrick teaches the limitations of claim 18.
Thomas further teaches wherein the plurality of hierarchical categories comprises an end of life (EOL) category, an institutionalized category, a longitudinal needs category, a complex polychronic category, a specialty treatable category, a primary care treatable category, and a wellbeing category:
(Paragraphs [0006] and [0100]-[0102] of Thomas. The teaching describes a complex patient identification component which can filter the currently admitted patients based on their forecasted discharge destination, LOS, readmission risk and/or safety risks, or another defined criterion (e.g., medical complexity, diagnosis, comorbidity, etc.) to generate a smaller more refined subgroup of patients to evaluate for classification as complex needs or not. The complex patient identification component 402 can be configured to identify complex needs patients using machine learning, qualitative and/or hybrid techniques based on defined clinical and non-clinical factors extracted from the input data 104 (e.g., including parameters regarding medical complexity, socio-economic conditions, insurance, medications, behavioral characteristics, mental health characteristics, etc.). The complex patient identification component 402 can also employ machine learning methods, qualitative methods, or hybrid techniques to identify relevant clinical factors collected for a currently admitted patient (e.g., via the data collection component 112) that strongly influence the patient being classified as complex needs and/or that require clinical attention (e.g., during the patient stay and/or post-discharge). For example, the complex patient identification component 402 can identify specific care needs of the complex patient that significantly contribute (e.g., relative to other factors) to them being classified as complex (e.g., needing hemodialysis, chemotherapy, radiation therapy, wound vacuums, and mental health care needs, having a physical disability, etc.). In another example, the complex patient identification component 402 can identify clinical factors that require coordination and scheduling for the complex needs patient during and/or post-discharge (e.g., the patient needs dialysis before and after discharge along with nursing support, the patient needs oxygen before and after discharge along with equipment support etc.). These relevant factors can be predefined and/or learned using one or more machine learning techniques. In some implementations, these strong contributing factors can be given higher weight in the complex needs classification model. The system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory.)
Response to Arguments
Applicant's arguments filed February 11, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the process of categorizing medical data in the manner claimed improves technology because this process “saves computational resources, and improves system run time” that is “even more pronounced if the criteria involves all of ICD codes, HHS-HCC, and CMS-HCC data” in the context of paragraph [0075] of the as-filed specification.
The Examiner respectfully disagrees with this argument. The citations from paragraph [0075] demonstrate that a system can avoid data analysis if certain data is prioritized ahead of other data analysis. There is no evidence that the processing of data is improved, but rather evidence that some data is serving as a threshold in determining when enough data has been examined. This is akin to merely looking at paper files for specific markings on a document for placing the file in a particular category. The analysis of the file itself is merely being abbreviated as opposed to being improved. While this might save computational resources and system run time, the same would be true with merely classifying all documents as one category or another at random. The system would not even have to analyze codes and such a feature would save computational resources and system run time. The point here is that merely deciding what information is relevant to prioritize in analysis to skip other data in the processing does not improve the processing itself and does not inherently improve technology itself. There is no evidence about how the claimed invention improves technology and this argument amounts to little more than a bare assertion of improvement.
The Applicant further argues that the claimed invention does not merely decide what information is relevant to prioritize. As explained in paragraph [0075] of the as-filed specification, the hierarchical classification process allows the system to avoid evaluating criteria for every category for every patient. This is not analogous to looking at paper files for specific markings.
The Examiner respectfully disagrees. The point of the argument above made by the Examiner was to illustrate that the system is only looking for particular information and prioritizing that information in processing, thereby disregarding the other evaluation criteria. The reason that this is similar to looking at paper files for specific markings is that these specific markings are particular information that stand in for a prioritized information marking where the other evaluation criteria is disregarded so long as this specific marking is present. In the instant case, the invention only cares about specific codes and data to process. It disregards the rest of the received medical data for predicting operational improvement so long as the ICD codes, HHS-HCC data and CMS-HCC data is present. These three data categories function as these specific markings in the analogy. The reason that the Examiner is not convinced that data processing is being improved is that the goal post of what data is being processed is not comparable. For the sake of argument lets assume that the received medical data is 1000 MB of data and the processor has the ability to process information at 10 MB/s. If these 3 pieces of data are present, the system might only need to process 30% of the whole data set. This would take only 30 seconds to process as opposed to 100 seconds if the whole data set was processed. But herein lies the problem. The processing speed did not change from the 10 MB/s. The system is merely processing less data without changing the processing power at all. Given that the function of the processor did not change, there is no change in technology as it relates to computational resources or system run time. Any improvement here is merely due to the decreased data throughput as opposed to any change in technology.
The Applicant further argues that the Office Action’s analogy of “classifying all documents as one category or another at random” misses the point of the claimed invention.
The Examiner respectfully disagrees. The Applicant misses the point of the Examiner’s argument. The Applicant was arguing that by processing less data, the system is improved by saving computational resources and improving system run time. The Examiner set forth the random classification argument as a criticism of the Applicant’s position. For a concrete example, we can refer back to the 1000 MB data set above. The point of the Examiner’s criticism was that the analysis of the 1000 MB data set could have been reduced significantly below 30 seconds, close to 0, if the system just decided the classification at random. The result would have been improved processing and system run time just as argued but even less of the data was analyzed. This is an argument of reductio ad absurdum to illustrate that the initial argument made by the Applicant was untenable. The point the Examiner was trying to make was that a technical improvement is not present per se by merely reducing the amount of data being analyzed by a processor when the rate of processing does not change.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are not persuasive.
The Applicant argues that Derrick does not teach the use of both HHS-HCC data and CMS-HCC data as recited in claim 1. This is because these data types are not interchangeable and are used for different ends.
The Examiner respectfully disagrees. The cited portions of Derrick do teach these limitations. The Examiner has marked in the citations above where Thomas teaches these data types. Please refer to the updated rejection above.
The Applicant further argues that the Office Action has not provided a rationale for why one of ordinary skill in the art would have found it obvious to combine Thomas and Derrick.
The Examiner respectfully disagrees. The rationale to combine is clear and explicit in the rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHAD A NEWTON/Primary Examiner, Art Unit 3681