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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 18 March 2026 has been entered.
Status of Claims
This action is in reply to the RCE filed on 18 March 2026. Claims 1, 5, 13, 23-25, and 35 were amended. Claims 1-35 are currently pending and have been examined.
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 1-35 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition or matter?
Claims 1-35 fall with one or more statutory categories. Claims 1-4 fall within the category of a process. Claims 5-22 fall within the category of a process. Claims 23-34 fall within the category of a process. Claim 35 falls within the category of a machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-35 recite an abstract idea. Representative claim 1 recites:
identifying, for a first resident, a first set of resident attributes corresponding to a defined set of features, comprising: accessing resident data for the first resident… ; and
… generate at least one of the first set of resident attributes … ;
generating, at a first time, a first acuity score for the first resident …, wherein:
[specify] a respective weight for each respective feature in the defined set of features, and
the first acuity score indicates a probability that the first resident will be hospitalized within a defined timeframe;
determining, at a second time, that one or more resident attributes of the first set of attributes have changed; and
in response to determining that one or more of the first set of resident attributes have changed, automatically generating an updated acuity score for the first resident …, wherein the updated acuity score indicates a probability that the changed set of resident attributes will result in the hospitalization of the first resident within a second defined timeframe;
selecting a prophylactic media component based on a profile of the first resident; and
outputting the selected prophylactic media component; and
in response to determining, at a third time subsequent to the second time, that no resident attribute of the changed set of resident attributes has changed since the updated acuity score was generated, refraining from generating a second updated acuity score.
Therefore, the claim as a whole is directed to “treating a patient,” which is an abstract idea because it is a method of organizing human activity. “Treating a patient” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), i.e. the interaction between a patient and a healthcare provider.
Alternatively, the broadest reasonable interpretation of the claims include a mental process, a concept capable of being performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s):
processing the resident data using one or more machine learning models;
processing the first set of resident attributes using an acuity model;
processing the changed set of resident attributes using an acuity model.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional elements, including the broadly recited machine learning elements, amount to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not include additional elements, considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), individually and in combination, merely recite the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
determining that the first acuity score exceeds a defined threshold;
generating an alert identifying the first resident, wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score;
selecting one or more interventions based on the first acuity score; and
initiating the one or more interventions.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 2 is considered to be ineligible.
Dependent claim 3 recites the method of claim 1, wherein:
the respective weights are defined based on prior resident data for a plurality of residents, the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes, and
a respective acuity score.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 3 is considered to be ineligible.
Dependent claim 4 recites the method of claim 1, wherein:
for each respective resident of a plurality of residents: identifying a respective set of resident attributes; and
generating a respective acuity score for the respective resident by processing the respective set of resident attributes using the acuity model; and
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents, wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 4 is considered to be ineligible.
Independent claim 5 recites a method that is substantially similar to the method of claim 1. Accordingly, claim 5 is rejected based on the same analysis.
Dependent claim 6 recites the method of claim 5, wherein:
determining that the first acuity score exceeds a defined threshold; and
generating an alert identifying the first resident, wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 6 is considered to be ineligible.
Dependent claim 7 recites the method of claim 6, wherein:
selecting the one or more interventions based on at least one of the first set of resident attributes.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 7 is considered to be ineligible.
Dependent claim 8 recites the method of claim 5, wherein:
the respective weights are defined based on prior resident data for a plurality of residents, the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes, and
a respective acuity score.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 8 is considered to be ineligible.
Dependent claim 9 recites the method of claim 8, wherein:
the acuity model is a static statistical model with manually-curated weights.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 9 is ineligible.
Dependent claim 10 recites the method of claim 8, wherein:
the acuity model is a trained machine learning model, and
wherein the respective weights were learned during a training phase.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 10 is ineligible.
Dependent claim 11 recites the method of claim 5, wherein:
for each respective resident of a plurality of residents in the healthcare facility: identifying a respective set of resident attributes; and
generating a respective acuity score for the respective resident by processing the respective set of resident attributes using the acuity model.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 11 is considered to be ineligible.
Dependent claim 12 recites the method of claim 11, wherein:
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents, wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 12 is considered to be ineligible.
Dependent claim 13 recites the method of claim 5, wherein:
the first set of resident attributes comprises a predicted fall risk, and
the predicted fall risk is generated using the one or more machine learning model.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 13 is ineligible.
Dependent claim 14 recites the method of claim 5, wherein:
the defined set of features comprises: one or more features relating to resident diagnoses, one or more features relating to needed assistance actions, one or more features relating to clinical assessments, and one or more features relating to therapies.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 14 is considered to be ineligible.
Dependent claim 15 recites the method of claim 14, wherein:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses, and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 15 is considered to be ineligible.
Dependent claim 16 recites the method of claim 15, wherein:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 16 is considered to be ineligible.
Dependent claim 17 recites the method of claim 14, wherein:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents, and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 17 is considered to be ineligible.
Dependent claim 18 recites the method of claim 17, wherein:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 18 is considered to be ineligible.
Dependent claim 19 recites the method of claim 20, wherein:
the one or more features relating to clinical assessments comprise a defined set of conditions, recorded by one or more caregivers, relating to functional states of residents, and
the first set of resident attributes indicate, for each respective condition of the defined set of conditions, whether the first resident has the respective condition.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 19 is considered to be ineligible.
Dependent claim 20 recites the method of claim 19, wherein:
the defined set of conditions comprises at least one of: (i) weight loss, (ii) use of intravenous (IV) feeding, (iii) congestive heart failure, (iv) pain, (v) incontinence, (vi) a catheter or ostomy, (vii) a deep tissue injury, (viii) a skin tear, (ix) an ulcer, (x) use of supplemental oxygen, (xi) use of a bilevel positive airway pressure (BIPAP) device, (xii) required isolation, (xiii) one or more mood or behavioral issues, or (xiv) one or more hallucinations or delusions.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 20 is considered to be ineligible.
Dependent claim 21 recites the method of claim 14, wherein:
the one or more features relating to therapies comprise a defined set of therapies, and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy.
This merely further limits the abstract idea of claim 5 discussed above and does not provide further additional elements. Therefore, claim 21 is considered to be ineligible.
Dependent claim 22 recites the method of claim 21, wherein:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 22 is considered to be ineligible.
Independent claim 23 recites a method that is substantially similar to the method of claim 1. In addition, claim 23 recites the following additional elements:
training an acuity model; and
deploying the acuity model.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, based on this analysis and the analysis of claim 1, claim 23 is ineligible.
Dependent claim 24 recites the method of claim 23, wherein:
training the acuity model comprises generating the respective weight for each respective feature in the defined set of features based on the resident data and based further on prior resident data for a plurality of residents, the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes, and
a respective acuity score.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 24 is considered to be ineligible.
Dependent claim 25 recites the method of claim 23, wherein:
the first set of resident attributes comprises a predicted fall risk, and
the predicted fall risk is generated by the one or more machine learning model.
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, claim 25 is ineligible.
Dependent claim 26 recites the method of claim 23, wherein:
the defined set of features comprises: one or more features relating to resident diagnoses, one or more features relating to needed assistance actions, one or more features relating to clinical assessments, and one or more features relating to therapies.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 26 is considered to be ineligible.
Dependent claim 27 recites the method of claim 26, wherein:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses, and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 27 is considered to be ineligible.
Dependent claim 28 recites the method of claim 27, wherein:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 28 is considered to be ineligible.
Dependent claim 29 recites the method of claim 26, wherein:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents, and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 29 is considered to be ineligible.
Dependent claim 30 recites the method of claim 29, wherein:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 30 is considered to be ineligible.
Dependent claim 31 recites the method of claim 26, wherein:
the one or more features relating to clinical assessments comprise a defined set of conditions, recorded by one or more caregivers, relating to functional states of residents, and
the first set of resident attributes indicate, for each respective condition of the defined set of conditions, whether the first resident has the respective condition.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 31 is considered to be ineligible.
Dependent claim 32 recites the method of claim 31, wherein:
the defined set of conditions comprises at least one of: (i) weight loss, (ii) use of intravenous (IV) feeding, (iii) congestive heart failure, (iv) pain, (v) incontinence, (vi) a catheter or ostomy, (vii) a deep tissue injury, (viii) a skin tear, (ix) an ulcer, (x) use of supplemental oxygen, (xi) use of a bilevel positive airway pressure (BIPAP) device, (xii) required isolation, (xiii) one or more mood or behavioral issues, or (xiv) one or more hallucinations or delusions.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 32 is considered to be ineligible.
Dependent claim 33 recites the method of claim 26, wherein:
the one or more features relating to therapies comprise a defined set of therapies, and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 33 is considered to be ineligible.
Dependent claim 34 recites the method of claim 33, wherein:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring.
This merely further limits the abstract idea of claim 23 discussed above and does not provide further additional elements. Therefore, claim 34 is considered to be ineligible.
Independent claim 35 recites a system that performs a method that is substantially similar to the method of claim 1. In addition, claim 35 recites the following additional elements:
one or more computer processors; and
logic encoded in a non-transitory medium, the logic executable by operation of the one or more computer processors to perform an operation [that is substantially similar to the method of claim 1].
The additional elements present in this claim merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). These types of additional elements are not enough to integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception. Accordingly, based on this analysis and the analysis of claim 1, claim 35 is ineligible.
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 1-3, 5-11, 13-14, 19-20, 23-26, 31-32, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Yudkovicz et al. (U.S. 2022/0351857), hereinafter “Yudkovicz,” in view of Soenksen et al. (U.S. 2015/0154372), hereinafter “Soenksen.”
Regarding claim 1, Yudkovicz discloses a method, comprising:
identifying, for a first resident, a first set of resident attributes corresponding to a defined set of features, comprising: accessing resident data for the first resident (See Yudkovicz [0008] healthcare setting including an assisted care facility or an assisted living facility. [0010] the system can collect various metrics connected to Activities of Daily Life (ADL), with assistance from caregivers or other personnel. [0012] system uses sensors to collect resident daily data to build a thorough time-series of resident activity that can later be correlated with behaviors, incidents (falls), and monitored activities (e.g., sleep quality, activity levels, etc.); and
processing the resident data using one or more machine learning models to generate at least one of the first set of resident attributes (See Yudkovicz [0009] system can use predictive algorithms and machine learning (ML) technologies to analyze and interpret patient information based on observed data, including to predict changes in resident acuity more accurately. [0011] data collected and reviewed by predictive data models and logistic regression models.);
generating, at a first time, a first acuity score for the first resident by processing the first set of resident attributes using an acuity model (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score, made up of different sub-scores, may be utilized as an alert mechanism for staff.), wherein:
the acuity model specifies a respective weight for each respective feature in the defined set of features (See Yudkovicz [0024] these composites scores may be provided as a weighted estimate of general behavioral well-being. See also [0018] the score can be based on sub-scores. [0020] system is trained to determine which covariates are significantly associated with either a greater or lower odds of experiencing a specific outcome.), and
determining, at a second time, that one or more resident attributes of the first set of attributes have changed (See Yudkovicz [0009] system can be used to predict changes in resident acuity. [0019] predict a health change attributed to a monitored behavior of one of the individuals; a medical event with the one of the individuals; and a change in biometric statistics of the one of the individuals. [0028] behavior patterns of a resident may change in progression as the resident moves between different behavior states. Thus, charts of a resident may be compared with earlier charts of the resident to predict future changes in behavior, an incident, and/or biometric statistics that correlate with the progression of pattern changes for the resident. [0018] The composite score may be utilized as an alert mechanism for staff.);
in response to determining that one or more of the first set of resident attributes have changed: automatically generating an updated acuity score for the first resident by processing the changed set of resident attributes using the acuity model (See Yudkovicz [0009] system can be used to predict changes in resident acuity. [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.);
in response to determining, at a third time subsequent to the second time, that no resident attribute of the changed set of resident attributes has changed since the updated acuity score was generated, refraining from generating a second updated acuity score (See Yudkovicz [0009] system can be used to predict changes in resident acuity. [0019] predict a health change attributed to a monitored behavior of one of the individuals; a medical event with the one of the individuals; and a change in biometric statistics of the one of the individuals. [0028] behavior patterns of a resident may change in progression as the resident moves between different behavior states. Thus, charts of a resident may be compared with earlier charts of the resident to predict future changes in behavior, an incident, and/or biometric statistics that correlate with the progression of pattern changes for the resident. Therefore, the system is understood to search for change, but if none is found no updated scores are added to the patient chart/profile.).
Yudkovicz does not disclose:
the first acuity score indicates a probability that the first resident will be hospitalized within a defined timeframe;
wherein the updated acuity score indicates a probability that the changed set of resident attributes will result in the hospitalization of the first resident within a second defined timeframe;
selecting a prophylactic media component based on a profile of the first resident; and
outputting the selected prophylactic media component.
Soenksen teaches:
the first acuity score indicates a probability that the first resident will be hospitalized within a defined timeframe (See Soenksen [0050] system can determine a patient's acute incident risk. Acute incident risk quantifies the risk of an acute incident such as hospitalization. Examiner notes that the use of “acute” indicates that the risk is imminent. This meets the broadest reasonable interpretation of “a defined timeframe.”);
wherein the updated acuity score indicates a probability that the changed set of resident attributes will result in the hospitalization of the first resident within a second defined timeframe (See Soenksen [0050] system can determine a patient's acute incident risk. Acute incident risk quantifies the risk of an acute incident such as hospitalization. Examiner notes that the use of “acute” indicates that the risk is imminent. This meets the broadest reasonable interpretation of “a defined timeframe.”);
selecting a prophylactic media component based on a profile of the first resident (See Soenksen [0038] therapy module may deliver digital personalized psychosocial therapies (PPT) to the patient via the visual, audio, and tactile user interfaces of patient device. [0050] risk module determines the patient’s acute incident risk, including risk of an acute incident such as hospitalization or admission to an assisted living facility or a nursing home. [0055] risk assessment allows the system to identify and recommend specific risk-reducing actions, including personalized digital therapies. [0078] Activity module is configured to personalize digital therapies and non-digital activities using personalized and personally relevant content, which includes video, images, pictures, music, voice messages that are personal to a specific patient. Such content is stored in memory as part of a patient's profile.); and
outputting the selected prophylactic media component (See Soenksen [0055] risk assessment allows the system to identify and recommend specific risk-reducing actions, including personalized digital therapies. [0078] Activity module is configured to personalize digital therapies and non-digital activities using personalized and personally relevant content, which includes video, images, pictures, music, voice messages that are personal to a specific patient. Such content is stored in memory as part of a patient's profile.).
The system of Soenksen is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to quantifying patient acute risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include selecting and outputting media as taught by Soenksen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to reduce burden and stress for caregivers of dementia patients, increase health-related quality of life (HRQOL) for dementia patients and their caregivers, and reduce costs of healthcare for dementia patients (see Soenksen [0008]).
Regarding claim 2, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Yudkovicz further discloses a method, comprising:
determining that the first acuity score exceeds a defined threshold (See Yudkovicz [0017] the system can compare change in score to a threshold.);
generating an alert identifying the first resident (See Yudkovicz [0017] when a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated.);
selecting one or more interventions based on the first acuity score (See Yudkovicz [0017] when a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated.); and
initiating the one or more interventions (See Yudkovicz [0017] When a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated, such as escalating the warning event to a supervisor or medical personnel for evaluation via prompt communication messaging or calling.).
Yudkovicz does not disclose:
wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score.
Soenksen teaches:
wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score (See Soenksen [0098] the system identifies one or more patient risk factors based on an analysis of the patient information, patient diseases, patient symptoms and patient cognitive state and determines the degree to which each identified patient risk factor contributes to acute incident risk.).
The system of Soenksen is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to quantifying patient acute risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include selecting and outputting media as taught by Soenksen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to reduce burden and stress for caregivers of dementia patients, increase health-related quality of life (HRQOL) for dementia patients and their caregivers, and reduce costs of healthcare for dementia patients (see Soenksen [0008]).
Regarding claim 3, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Yudkovicz further discloses a method, wherein:
the respective weights are defined based on prior resident data for a plurality of residents (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data. [0011] initial data is collected for a predetermined amount of time. Therefore, the system is training the models using prior collected data. [0017] A weighted ensemble of models may be employed.), the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data.), and
a respective acuity score (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.).
Regarding claim 5, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Claim 5 recites a method that is substantially similar to the method of claim 1. Accordingly, claim 5 is rejected based on the same analysis.
Regarding claim 6, Yudkovicz in view of Soenksen discloses the method of claim 5 as discussed above. Yudkovicz further discloses a method, comprising:
determining that the first acuity score exceeds a defined threshold (See Yudkovicz [0017] When a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated, such as escalating the warning event to a supervisor or medical personnel for evaluation via prompt communication messaging or calling.); and
generating an alert identifying the first resident (See Yudkovicz [0017] When a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated, such as escalating the warning event to a supervisor or medical personnel for evaluation via prompt communication messaging or calling.).
Yudkovicz does not disclose:
wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score.
Soenksen teaches:
wherein the alert indicates a first resident attribute, of the first set of resident attributes, that has a highest contribution to the first acuity score (See Soenksen [0098] the system identifies one or more patient risk factors based on an analysis of the patient information, patient diseases, patient symptoms and patient cognitive state and determines the degree to which each identified patient risk factor contributes to acute incident risk.).
The system of Soenksen is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to quantifying patient acute risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include selecting and outputting media as taught by Soenksen. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to reduce burden and stress for caregivers of dementia patients, increase health-related quality of life (HRQOL) for dementia patients and their caregivers, and reduce costs of healthcare for dementia patients (see Soenksen [0008]).
Regarding claim 7, Yudkovicz in view of Soenksen discloses the method of claim 6 as discussed above. Yudkovicz further discloses a method, comprising:
selecting the one or more interventions based on at least one of the first set of resident attributes (See Yudkovicz [0017] When a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated, such as escalating the warning event to a supervisor or medical personnel for evaluation via prompt communication messaging or calling.).
Regarding claim 8, Yudkovicz in view of Soenksen discloses the method of claim 5 as discussed above. Yudkovicz further discloses a method, wherein:
the respective weights are defined based on prior resident data for a plurality of residents (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data. [0011] initial data is collected for a predetermined amount of time. Therefore, the system is training the models using prior collected data. [0017] A weighted ensemble of models may be employed.), the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data.), and
a respective acuity score (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.).
Regarding claim 9, Yudkovicz in view of Soenksen discloses the method of claim 8 as discussed above. Yudkovicz further discloses a method, wherein:
the acuity model is a static statistical model with manually-curated weights (See Yudkovicz [0014] manual inspection of outliers in the data before use in creating the model. [0017] A weighted ensemble of models may be employed. [0027] system can use supervised machine learning for pattern and association analysis.).
Regarding claim 10, Yudkovicz in view of Soenksen discloses the method of claim 8 as discussed above. Yudkovicz further discloses a method, wherein:
the acuity model is a trained machine learning model (See Yudkovicz [0014] system uses modeling based on training set of data. [0015] the system can train a predictive model.), and
wherein the respective weights were learned during a training phase (See Yudkovicz [0017] A weighted ensemble of models may be employed. [0020] system is trained to determine which covariates are significantly associated with either a greater or lower odds of experiencing a specific outcome. [0026] the system can use unsupervised machine learning.).
Regarding claim 11, Yudkovicz in view of Soenksen discloses the method of claim 5 as discussed above. Yudkovicz further discloses a method, comprising:
for each respective resident of a plurality of residents in the healthcare facility: identifying a respective set of resident attributes (See Yudkovicz [0008] healthcare setting including an assisted care facility or an assisted living facility. [0010] the system can collect various metrics connected to Activities of Daily Life (ADL), with assistance from caregivers or other personnel. [0012] system uses sensors to collect resident daily data to build a thorough time-series of resident activity that can later be correlated with behaviors, incidents (falls), and monitored activities (e.g., sleep quality, activity levels, etc.); and
generating a respective acuity score for the respective resident by processing the respective set of resident attributes using the acuity model (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.).
Regarding claim 13, Yudkovicz in view of Soenksen discloses the method of claim 5 as discussed above. Yudkovicz further discloses a method, wherein:
the first set of resident attributes comprises a predicted fall risk (See Yudkovicz [0012] the collected data includes fall incidents. [0019] the system can estimate the risk or the probability of a certain outcome or medical event. Therefore, it is understood that the system includes the use of risk of fall.), and
the predicted fall risk is generated by using the one or more machine learning model (See Yudkovicz [0012] the collected data includes fall incidents. [0019] the system can estimate the risk or the probability of a certain outcome or medical event. Therefore, it is understood that the system includes the use of risk of fall. [0014] system uses modeling based on training set of data. [0015] the system can train a predictive model.).
Regarding claim 14, Yudkovicz in view of Soenksen discloses the method of claim 5 as discussed above. Yudkovicz further discloses a method, wherein:
the defined set of features comprises: one or more features relating to resident diagnoses (See Yudkovicz [0019] the covariates used in the prediction include the patient’s medical history.),
one or more features relating to needed assistance actions (See Yudkovicz [0024] the prediction can be weighed based on level assistance required.),
one or more features relating to clinical assessments (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.).), and
one or more features relating to therapies (See Yudkovicz [0019] the individual sub scores used in the prediction can be based on medication (meds) levels.).
Regarding claim 19, Yudkovicz in view of Soenksen discloses the method of claim 14 as discussed above. Yudkovicz further discloses a method, wherein:
the one or more features relating to clinical assessments comprise a defined set of conditions, recorded by one or more caregivers, relating to functional states of residents (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors connected to pain or mood).), and
the first set of resident attributes indicate, for each respective condition of the defined set of conditions, whether the first resident has the respective condition (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors connected to pain or mood).).
Regarding claim 20, Yudkovicz in view of Soenksen discloses the method of claim 19 as discussed above. Yudkovicz further discloses a method, wherein:
the defined set of conditions comprises at least one of: (i) weight loss, (ii) use of intravenous (IV) feeding, (iii) congestive heart failure, (iv) pain, (v) incontinence, (vi) a catheter or ostomy, (vii) a deep tissue injury, (viii) a skin tear, (ix) an ulcer, (x) use of supplemental oxygen, (xi) use of a bilevel positive airway pressure (BIPAP) device, (xii) required isolation, (xiii) one or more mood or behavioral issues, or (xiv) one or more hallucinations or delusions (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors (e.g., a calm demeanor, agreeability, clear-headedness, sociability, aggression, apathy, confusion, motor agitation, verbal agitation, resisting care, alertness, mood, sleep, activity, pain, medication (meds) levels, gait, etc.).).
Regarding claim 23, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Claim 23 recites a method that is substantially similar to the method of claim 1. However, claim 5 also recites the following, which is disclosed by Yudkovicz:
training an acuity model (See Yudkovicz [0024] these composites scores may be provided as a weighted estimate of general behavioral well-being. See also [0018] the score can be based on sub-scores. [0020] system is trained to determine which covariates are significantly associated with either a greater or lower odds of experiencing a specific outcome.); and
deploying the acuity model (See Yudkovicz [0017] When a deviation beyond a threshold is detected, a warning signal or a warning event may be generated and/or a corrective action may be initiated, such as escalating the warning event to a supervisor or medical personnel for evaluation via prompt communication messaging or calling.).
Accordingly, claim 23 is rejected based on this analysis and the analysis of claim 1.
Regarding claim 24, Yudkovicz in view of Soenksen discloses the method of claim 23 as discussed above. Yudkovicz further discloses a method, wherein:
training the acuity model comprises generating the respective weight for each respective feature in the defined set of features based on the resident data and based further on prior resident data for a plurality of residents (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data. [0011] initial data is collected for a predetermined amount of time. Therefore, the system is training the models using prior collected data. [0017] A weighted ensemble of models may be employed.), the prior resident data indicating, for each respective resident of the plurality of residents:
a respective set of resident attributes (See Yudkovicz [0010] data is originally collected for a population of patients to serve as training set data.), and
a respective acuity score (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.).
Regarding claim 25, Yudkovicz in view of Soenksen discloses the method of claim 23 as discussed above. Yudkovicz further discloses a method, wherein:
the first set of resident attributes comprises a predicted fall risk (See Yudkovicz [0012] the collected data includes fall incidents. [0019] the system can estimate the risk or the probability of a certain outcome or medical event. Therefore, it is understood that the system includes the use of risk of fall.), and
the predicted fall risk is generated by the one or more machine learning model (See Yudkovicz [0012] the collected data includes fall incidents. [0019] the system can estimate the risk or the probability of a certain outcome or medical event. Therefore, it is understood that the system includes the use of risk of fall. [0014] system uses modeling based on training set of data. [0015] the system can train a predictive model.).
Regarding claim 26, Yudkovicz in view of Soenksen discloses the method of claim 23 as discussed above. Yudkovicz further discloses a method, wherein:
the defined set of features comprises: one or more features relating to resident diagnoses (See Yudkovicz [0019] the covariates used in the prediction include the patient’s medical history.),
one or more features relating to needed assistance actions (See Yudkovicz [0024] the prediction can be weighed based on level assistance required.),
one or more features relating to clinical assessments (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.).), and
one or more features relating to therapies (See Yudkovicz [0019] the individual sub scores used in the prediction can be based on medication (meds) levels.).
Regarding claim 31, Yudkovicz in view of Soenksen discloses the method of claim 26 as discussed above. Yudkovicz further discloses a method, wherein:
the one or more features relating to clinical assessments comprise a defined set of conditions, recorded by one or more caregivers, relating to functional states of residents (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors connected to pain or mood).), and
the first set of resident attributes indicate, for each respective condition of the defined set of conditions, whether the first resident has the respective condition (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors connected to pain or mood).).
Regarding claim 32, Yudkovicz in view of Soenksen discloses the method of claim 31 as discussed above. Yudkovicz further discloses a method, wherein:
the defined set of conditions comprises at least one of: (i) weight loss, (ii) use of intravenous (IV) feeding, (iii) congestive heart failure, (iv) pain, (v) incontinence, (vi) a catheter or ostomy, (vii) a deep tissue injury, (viii) a skin tear, (ix) an ulcer, (x) use of supplemental oxygen, (xi) use of a bilevel positive airway pressure (BIPAP) device, (xii) required isolation, (xiii) one or more mood or behavioral issues, or (xiv) one or more hallucinations or delusions (See Yudkovicz [0019] the covariates used in the prediction include frailty status, cognitive function, and vascular health (as indicated by systolic blood pressure, pulse, etc.). The sub scores can be based on distinct behaviors (e.g., a calm demeanor, agreeability, clear-headedness, sociability, aggression, apathy, confusion, motor agitation, verbal agitation, resisting care, alertness, mood, sleep, activity, pain, medication (meds) levels, gait, etc.).).
Regarding claim 35, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Claim 35 recites a system that performs a method that is substantially similar to the method of claim 1. Accordingly, claim 35 is rejected based on the same analysis.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yudkovicz et al. (U.S. 2022/0351857), hereinafter “Yudkovicz,” in view of Soenksen et al. (U.S. 2015/0154372), hereinafter “Soenksen,” and further in view of McGillin (U.S. 2006/0287906), hereinafter “McGillin.”
Regarding Claim 4, Yudkovicz in view of Soenksen discloses the method of claim 1 as discussed above. Yudkovicz further discloses a method, comprising:
for each respective resident of a plurality of residents: identifying a respective set of resident attributes (See Yudkovicz [0008] healthcare setting including an assisted care facility or an assisted living facility. [0010] the system can collect various metrics connected to Activities of Daily Life (ADL), with assistance from caregivers or other personnel. [0012] system uses sensors to collect resident daily data to build a thorough time-series of resident activity that can later be correlated with behaviors, incidents (falls), and monitored activities (e.g., sleep quality, activity levels, etc.); and
generating a respective acuity score for the respective resident by processing the respective set of resident attributes using the acuity model (See Yudkovicz [0017] system can generate a resident score. A baseline score may be established, for example, that will likely move lower over time, for each resident and highlight when they move significantly from that baseline. [0018] The composite score may be utilized as an alert mechanism for staff.).
Yudkovicz does not disclose:
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents, wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores.
McGillin teaches:
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents (See McGillin [0040] the system can combine scores of patients located at user defined geographic location, creating aggregated acuity score for the patients occupying the location so that the total acuity score value for the location is known.), wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores (See McGillin Fig. 2 and [0031] the scores for each location can include a range (i.e. “distribution”) and their corresponding staffing requirements. See also [0040] od the aggregating acuity scores by location.).
The system of McGillin is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to calculating patient acuity. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include calculating aggregate scores as taught by McGillin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to address changes in patient acuity that result in an underperforming work environment with patients not getting required care due to healthcare provider delay or omission in administering required patient care (see McGillin [0040]).
Regarding claim 12, Yudkovicz in view of Soenksen discloses the method of claim 11 as discussed above. Yudkovicz does not further disclose a method, comprising:
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents, wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores.
McGillin teaches:
generating one or more aggregate acuity scores based on the respective acuity score of each respective resident of the plurality of residents (See McGillin [0040] the system can combine scores of patients located at user defined geographic location, creating aggregated acuity score for the patients occupying the location so that the total acuity score value for the location is known.) wherein the one or more aggregate acuity scores comprise, for each respective location of a set of locations, a respective distribution of acuity scores (See McGillin Fig. 2 and [0031] the scores for each location can include a range (i.e. “distribution”) and their corresponding staffing requirements. See also [0040] od the aggregating acuity scores by location.).
The system of McGillin is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to calculating patient acuity. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include calculating aggregate scores as taught by McGillin. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to address changes in patient acuity that result in an underperforming work environment with patients not getting required care due to healthcare provider delay or omission in administering required patient care (see McGillin [0040]).
Claims 15-18, 21-22, 27-30, and 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Yudkovicz et al. (U.S. 2022/0351857), hereinafter “Yudkovicz,” in view of Soenksen et al. (U.S. 2015/0154372), hereinafter “Soenksen,” and further in view of Kayser et al. (U.S. 2019/0336085), hereinafter “Kayser.”
Regarding claim 15, Yudkovicz in view of Soenksen discloses the method of claim 14 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses, and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis.
Kayser teaches:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.), and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 16, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 15 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer.
Kayser teaches:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 17, Yudkovicz in view of Soenksen discloses the method of claim 14 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents, and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action.
Kayser teaches:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.), and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 18, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 17 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance.
Kayser teaches:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 21, Yudkovicz in view of Soenksen discloses the method of claim 14 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to therapies comprise a defined set of therapies, and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy.
Kayser teaches:
the one or more features relating to therapies comprise a defined set of therapies (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.), and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 22, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 21 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring.
Kayser teaches:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 27, Yudkovicz in view of Soenksen discloses the method of claim 26 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses, and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis.
Kayser teaches:
the one or more features relating to resident diagnoses comprise a defined set of diagnoses (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.), and
the first set of resident attributes indicate, for each respective diagnosis of the defined set of diagnoses, whether the first resident has the respective diagnosis (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 28, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 27 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer.
Kayser teaches:
the defined set of diagnoses comprises at least one of: (i) malnutrition, (ii) sarcopenia, (iii) congestive heart failure, (iv) chronic obstructive pulmonary disease (COPD), (v) cirrhosis, (vi) renal failure, (vii) chronic kidney disease, (viii) human immunodeficiency virus (HIV), (ix) diabetes, or (x) cancer (See Kayser [0044] the system can use data related to patient conditions for calculating medical risk. This includes determining if the patient has one of the following conditions: acquired immunodeficiency syndrome (AIDS), congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), diabetes, cirrhosis.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 29, Yudkovicz in view of Soenksen discloses the method of claim 26 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents, and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action.
Kayser teaches:
the one or more features relating to needed assistance actions comprise a defined set of actions that can be performed by one or more caregivers to assist residents (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.), and
the first set of resident attributes indicate, for each respective action of the defined set of actions, whether caregivers assist the first resident with the respective action (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 30, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 29 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance.
Kayser teaches:
the defined set of actions comprises at least one of: (i) assistance to eat, (ii) use a mechanical lift assist, (iii) bed mobility assistance, (iv) transfer assistance, (v) walking assistance, or (vi) bathing assistance (See Kayser [0162] the system can collect clinical examination data for use in calculating the risk score. This data includes whether the patient needs for assistance with activities of daily living (ADLS), which is understood to include at least bathing, dressing, eating, and toileting.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 33, Yudkovicz in view of Soenksen discloses the method of claim 26 as discussed above. Yudkovicz does not further disclose a method, wherein:
the one or more features relating to therapies comprise a defined set of therapies, and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy.
Kayser teaches:
the one or more features relating to therapies comprise a defined set of therapies (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.), and
the first set of resident attributes indicate, for each respective therapy of the defined set of therapies, whether the first resident receives the respective therapy (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Regarding claim 34, Yudkovicz in view of Soenksen and Kayser discloses the method of claim 33 as discussed above. Yudkovicz does not further disclose a method, wherein:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring.
Kayser teaches:
the defined set of therapies comprises at least one of: (i) total parenteral nutrition, (ii) psychotropic medication, (iii) anticoagulant medication, or (iv) blood glucose monitoring (See Kayser [0050] the system can use medications data (i.e., therapy) about the patient in calculating the risk score. This includes the use of anticoagulants. See also [0261] and Table 11 (on page 36) the use of psychotropic medication can be used in the calculation of the risk score.).
The system of Kayser is applicable to the disclosure of Yudkovicz as they both share characteristics and capabilities, namely, they are directed to predicting patient risk. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yudkovicz to include attributes used in the calculation as taught by Kayser. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Yudkovicz in order to provide more timely information regarding risk assessments of patients and provide the risk assessment information to be more readily available to caregivers.
Response to Arguments
Applicant's arguments filed 24 February 2026, with respect to the 35 U.S.C. §101 rejection of the claims, have been fully considered but they are not persuasive. First, Applicant argues that the claims do not recite an abstract idea under step 2A Prong One (see Applicant Remarks pages 13-16). This is not persuasive. “Treating a patient” is considered to be a method of organizing human activity because it is an example of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of the claims include the interaction between a patient and a healthcare provider. Further, the broadest reasonable interpretation of the claims include a mental process, a concept capable of being performed in the human mind (including an observation, evaluation, judgment, opinion). Present independent claim 23 does recite the training of machine learning models similar to the examples 39 and 47 (Examiner notes that the other presently recited independent claims do not recite the explicit training of the models). However, the present claims also recite more elements than the training of the models. This further recitation of elements is where we find the abstract idea of the present claims, and therefore distinguish the present claims from example 39 and 47. The elements connected to the use or training of machine learning models are recited at such a high level of generality that they are considered additional elements, separate from the abstract idea, and are considered under Step 2A Prong Two and Step 2B. therefore, the claims recite an abstract idea under Step 2A Prong One, and the analysis must continue to Prong Two and Step 2B in order to consider the recited additional elements.
Next, Applicant argues that the claims recite a technological improvement that integrates the abstract idea into a practical application (see Applicant Remarks pages 16-18). This is not persuasive. First, stating that the method and system result in “reducing computational expense” is understood to be an intended result and not a clear technological improvement based on the recited claim elements. Second, the additional elements are recited as general purpose computer functions and hardware, which amounts to using a computer as a tool to perform the abstract idea. This is not enough to integrate the abstract idea into a practical application because it does not show an improvement to technology (see MPEP 2106.05(f)). Accordingly, the claims remain rejected as being directed to ineligible subject matter.
Finally, Applicant argues that the claims are integrated into a practical application because they select a prophylactic media and output that media (see Applicant Remarks page 18). This is not persuasive. Applicant describes these elements as “a clear practical application of any alleged abstract idea” without providing any support for that argument. The selection and output of the prophylactic media is considered to be part of the abstract idea (i.e. treating the patient) and therefore is not an additional element that amounts to a practical application (see MPEP 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”). Therefore, the claims do not recite a practical application, and remain rejected as being directed to ineligible subject matter.
Applicant's arguments filed 24 February 2026, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of newly cited portions of the Soenksen reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Freeseman-Freeman et al. (U.S. 2020/0214648) teaches a system and method for determining risk of deterioration of monitored patients.
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/B.L.H./Examiner, Art Unit 3684
/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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