DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 19 June 2026. No claims were amended. Claims 6 and 16 were previously canceled. Claims 1-5, 7-15, and 17-20 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 1-5, 7-15, and 17-20 are rejected under 35 USC § 101
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-5, 7-15, and 17-20 fall within one or more statutory categories. Claims 1-5 and 7-12 fall within the category of a process. Claims 13-15 and 17-19 fall within the category of a machine. Claim 20 falls within the category of a manufacture.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1-5, 7-15, and 17-20 recite an abstract idea. Representative claim 1 recites:
receiving, at regular intervals from a plurality of data sources, healthcare information data characterizing healthcare information associated with a plurality of patients, each of the data sources containing healthcare information associated with at least one of the plurality of patients, at least a subset of the data sources being maintained by an unrelated third-party, healthcare information data from at least two of the data sources being in different formats, at least a subset of the plurality of patients being unrelated to each other and receiving healthcare from different, unrelated healthcare providers;
normalizing the healthcare information data received at regular intervals such that healthcare information data from each of the data sources is normalized into a unified format to create normalized data;
retrieving a plurality of health metrics for at least a subset of the plurality of patients from the normalized data;
determining … a risk prediction for a particular patient based on at least some of the plurality of metrics of the normalized data, the determining the risk prediction including determining … a risk factor that predicts likelihood of a negative health outcome based on the at least some of the plurality of metrics, … [the risk prediction is] based on risk data of a first population of patients and historical data characterizing the healthcare information of the first population of patients;
triggering generation of a treatment recommendation for the particular patient based on the risk prediction;
generating … the treatment recommendation for the particular patient, based on the risk prediction, healthcare information data of a second, unrelated population of patients, and the healthcare information data of the particular patient;
triggering, in real time, an alert to contact the particular patient based, at least in part, on the risk prediction;
providing at least part of the treatment recommendation and an indication of the risk prediction … to facilitate the contact with the particular patient;
receiving an indication of a result of the contact with the particular patient; and
updating the particular patient's healthcare information data to enable updating of the treatment recommendation.
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). The broadest reasonable interpretation of the claims, in view of the specification, includes the interaction between a healthcare provider and a patient.
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):
a risk prediction model;
the risk prediction model having been trained;
a treatment engine;
a graphical user display of a digital device of a user.
The additional elements individually or in combination do not integrate the exception into a practical application. These additional 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, 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, claim 1 is ineligible.
Dependent claim 2 recites the method of claim 1, wherein:
the determining, by the risk prediction model, the risk prediction for the particular patient includes: comparing at least some of healthcare information data of the particular patient to the healthcare information data of the second, unrelated population of patients, wherein at least some of the second, unrelated population of patients includes similar health characteristics to the particular patient; and
determining a deficiency in the received healthcare information data of the particular patient based on a predetermined set of healthcare parameters.
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 2 is ineligible.
Dependent claim 3 recites the method of claim 2, wherein:
querying a questionnaire rules engine for the at least one question based on the determined one or more deficiencies, the questionnaire rules engine configured to generate the at least one question, wherein the questionnaire rules engine is modified by a questionnaire predictive model based on the risk prediction, and
providing the at least one question from the questionnaire rules engine to the graphical user display before or at a time of contact.
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 3 is ineligible.
Dependent claim 4 recites the method of claim 1, further comprising:
updating training of the treatment engine by incorporating responses from the contact with the particular patient to update future treatment recommendations for the at least some of the plurality of patients.
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. Therefore, claim 4 is considered to be ineligible.
Dependent claim 5 recites the method of claim 1, further comprising:
updating the training of the risk prediction model by incorporating responses from contact with the particular patient to potentially update the risk prediction for the particular patient.
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. Therefore, claim 5 is considered to be ineligible.
Dependent claim 7 recites the method of claim 1, wherein:
the generating the treatment recommendation includes: automatically querying the treatment engine, based in part on the risk prediction, the querying including execution of a recommendation rule by the treatment engine, and
generating a recommendation string that characterizes the treatment recommendation.
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 7 is ineligible.
Dependent claim 8 recites the method of claim 7, wherein:
the treatment engine is automatically modified by a predictive model that identifies a predictor variable characterizing a likelihood of success of an intervention characterized by the treatment recommendation, the predictive model configured to automatically identify when feedback data from the contact indicates a level of success of the intervention is received, and automatically modify the training of the treatment engine.
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 8 is ineligible.
Dependent claim 9 recites the method of claim 7, wherein:
the providing at least part of the treatment recommendation includes transmitting the recommendation string a list of the plurality of tasks for presentation on a graphical user interface of the digital device, wherein the plurality of tasks are associated with carrying out the treatment recommendation and at least one of the plurality of tasks are presented with a priority level characterized by at least one risk subcategory level prediction.
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 7, wherein:
the treatment engine is trained by a recommendation predictive model that identifies a predictor variable characterizing a pattern in adherence to interventions suggested by the treatment recommendation and automatically modifies a rule of the treatment engine based on the identification.
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 6, wherein:
the determining of the risk prediction for the particular patient includes automatically determining a clinical risk parameter characterizing a level of clinical risk, determining a social risk parameter characterizing a level of social risk, and determining a behavioral risk parameter characterizing a level of behavioral risk, the risk prediction being based on at least one of the clinical risk parameter, the social risk parameter, and the behavioral risk parameter.
This merely further limits the abstract idea of claim 1 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:
one or more of the clinical risk parameter, the social risk parameter, and the behavioral risk parameter is automatically updated based on the particular patient's response during the contact.
This merely further limits the abstract idea of claim 1 discussed above and does not provide further additional elements. Therefore, claim 12 is considered to be ineligible.
Independent claim 13 recites a system that performs the method of claim 1. Claim 13 further recites the following additional elements
at least one data processor; and
memory storing instructions configured to cause the at least one data processor to perform operations.
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.
Claims 14-15 and 17-19 are parallel in nature to claims 2-3, 7-8, and 10. Accordingly claims 14-19 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
Claim 20 is parallel in nature to claim 13. Accordingly claim 20 is rejected as being directed towards ineligible subject matter based upon the same analysis above.
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 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kil (U.S. 2008/0146334), hereinafter “Kil,” in view of Francois et al. (U.S. 2017/0039324), hereinafter “Francois.”
Regarding Claim 1, Kil discloses a method comprising:
receiving, at regular intervals from a plurality of data sources, healthcare information data characterizing healthcare information associated with a plurality of patients, each of the data sources containing healthcare information associated with at least one of the plurality of patients (See Kil [0045] the system can collect claims data, self-reported data, and consumer behavior marketing data. [0058] the system can receive input data in the form of claims data, self-reported data, consumer behavior marketing (CBM) data, and biometric, all from an EHR. See also Fig. 2. [0169] the system can collect and report on a regular basis. [0205] the data is collected and analyzed in real-time.), at least a subset of the data sources being maintained by an unrelated third-party (See Kil [0051] data libraries used by the system can include at least the third-party Dun & Bradstreet databases.), healthcare information data from at least two of the data sources being in different formats (See Kil [0045] the system performs extract-transform-load (ETL) of disparate data assets to form a consumer-centric view while cleaning data prior to weak-signal transformation through digital signal processing (DSP) and feature extraction. This use of ETL is understood to mean that the data is received in multiple formats.);
normalizing the healthcare information data received at regular intervals such that healthcare information data from each of the data sources is normalized into a unified format to create normalized data (See Kil [0045] the system performs extract-transform-load (ETL) of disparate data assets to form a consumer-centric view while cleaning data prior to weak-signal transformation through digital signal processing (DSP) and feature extraction. This is understood to include data normalization.);
retrieving a plurality of health metrics for at least a subset of the plurality of patients from the normalized data (See Kil Fig. 1 and [0045] the system can form disease clusters and estimate disease progression probabilities. See also [0112] the system builds models for predicting outcome metrics for various evidence-based-medicine guidelines. Fig. 6 and [0162] disease states may depend on observed behavioral/lifestyle factors including the attributes of the consumer (i.e. the patient).);
determining, by a risk prediction model, a risk prediction for a particular patient based on at least some of the plurality of metrics of the normalized data (See Kil [0045] the system can form disease clusters and estimate disease progression probabilities (i.e., “risk prediction”). [0162] the system can show the probability of transitioning from one disease state to another disease state based on whether the consumer obtains a prescribed treatment. [0048] the system uses predictive modeling, combinatorial and stochastic feature optimization with respect to outcomes, and propensity-score shaping. See also [0148].), the determining the risk prediction including determining, by a risk prediction model, a risk factor that predicts likelihood of a negative health outcome based on the at least some of the plurality of metrics (See Kil See Kil [0045] the system can form disease clusters and estimate disease progression probabilities (i.e., “risk prediction”). Fig. 6 and [0162] the disclosure gives an example of risk prediction for disease progression related to diabetes. This include the over risk prediction from one step to another, as well as the sub categories including risk or progression with treatment and without treatment.), the risk prediction model having been trained based on risk data of a first population of patients and historical data characterizing the healthcare information of the first population of patients (See Kil [0045] the system can collect claims data, self-reported data, and consumer behavior marketing data. The system can form disease clusters and estimate disease progression probabilities. [0047] the system can determine what works for which population segments, by how much, and why. [0078] the system is trained/updated for new medical developments, such as introduction of new medical technologies and drugs, changes in benefit plans and fee-reimbursement schedules, changing demographics, and even macroeconomic cycles can affect data characteristics. [0134] the input for the system can include patient history. See also [0231]);
triggering generation of a treatment recommendation for the particular patient based on the risk prediction (See Kil [0046] the system can include an intervention opportunity finder. Impact assessment can be made based on the aggregate future impact of all the identified targets of opportunities. [0048] the system can then create rules of engagement for statistically significant outcomes.);
generating, by a treatment engine, the treatment recommendation for the particular patient, based on the risk prediction, healthcare information data of a second, unrelated population of patients, and the healthcare information data of the particular patient (See Kil [0046] the system can include an intervention opportunity finder. Impact assessment can be made based on the aggregate future impact of all the identified targets of opportunities. [0048] the system can then create rules of engagement for statistically significant outcomes. [0163] the system can determine treatments or interventions that the patient can be used to reduce probability of a disease progression. See also Figs. 6 and 9 and [0233].);
triggering, in real time, an alert to contact the particular patient based, at least in part, on the risk prediction (See Kil [0266] feedback to the user may be asynchronous, regular, or on demand (i.e. “real-time”) through the preferred or available communication channel.);
providing at least part of the treatment recommendation and an indication of the risk prediction (See Kil [0233] the system can determine the appropriate intervention/treatment to address the state of health of the participant. And encourage the participant to follow the recommendation. See also [0266]-[0267] the system can explain the recommendation and the variables that led to the risk prediction.), to a graphical user display of a digital device of a user to facilitate the contact with the particular patient (See Kil Fig. 7 and [0180] the system can include a display device. [0266] feedback to the user may be asynchronous, regular, or on demand (i.e. “real-time”) through the preferred or available communication channel.);
receiving an indication of a result of the contact with the particular patient (See Kil [0269] in this example, the user takes the advice to heart and works with the system to set up specific goals on fitness, social life, creativity, and spirituality.); and
updating the particular patient's healthcare information data to enable updating of the treatment recommendation (See Kil [0269] as the user works his way back to good health, he sees the immediate impact on the way his avatar looks, plays the game, and achieves results. This is an update to the recommendation.).
Kil does not disclose:
at least a subset of the plurality of patients being unrelated to each other and receiving healthcare from different, unrelated healthcare providers.
Francois teaches:
at least a subset of the plurality of patients being unrelated to each other and receiving healthcare from different, unrelated healthcare providers (See Francois Fig. 1 and [0056] data from multiple different providers and patients.).
The system of Francois is applicable to the disclosure of Kil as they both share characteristics and capabilities, namely, they are directed to using patient data to make treatment recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kil to include third party databases and updated learning models as taught by Francois. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kil in order to address issues associated with the fragmentary and often inaccessible nature of existing health data (see Francois [0003]).
Regarding claim 2, Kil in view of Francois discloses the method of claim 1 as discussed above. Kil further discloses a method, wherein:
the determining, by the risk prediction model, the risk prediction for the particular patient includes: comparing at least some of healthcare information data of the particular patient to the healthcare information data of the second, unrelated population of patients, wherein at least some of the second, unrelated population of patients includes similar health characteristics to the particular patient (See Kil [0045] the system can collect claims data, self-reported data, and consumer behavior marketing data. The system can form disease clusters and estimate disease progression probabilities. [0047] the system can determine what works for which population segments, by how much, and why. [0229] The actual algorithm will be a combination of predictive models and population normalization algorithms using propensity scores.); and
determining a deficiency in the received healthcare information data of the particular patient based on a predetermined set of healthcare parameters (See Kil [0170] the system calculates future health trajectories and guides the user through the benefit selection process based on an adaptive questionnaire tree designed to minimize the number of questions while maximizing predictive accuracy. The questionnaire is a combination of Predictive Health Risk Assessment (PHRA) interspersed with Adaptive Conjoint Analysis (ACA) questions. [0149] the ACA is tailored to each consumer. Therefore, it is understood that the system only ask questions that are required based on information it does not already have, in order to tailor the questionnaire to each user and minimize the number of questions presented. See also [0048].).
Regarding claim 3, Kil in view of Francois discloses the method of claim 2 as discussed above. Kil further discloses a method, comprising:
querying a questionnaire rules engine for the at least one question based on the determined one or more deficiencies (See Kil [0170] the system calculates future health trajectories and guides the user through the benefit selection process based on an adaptive questionnaire tree designed to minimize the number of questions while maximizing predictive accuracy. The questionnaire is a combination of Predictive Health Risk Assessment (PHRA) interspersed with Adaptive Conjoint Analysis (ACA) questions. [0149] the ACA is tailored to each consumer. Therefore, it is understood that the system only ask questions that are required based on information it does not already have, in order to tailor the questionnaire to each user and minimize the number of questions presented. See also [0048].), the questionnaire rules engine configured to generate the at least one question, wherein the questionnaire rules engine is modified by a questionnaire predictive model based on the risk prediction (See Kil [0170] the system uses an adaptive questionnaire tree designed to minimize the number of questions while maximizing predictive accuracy. The questionnaire is a combination of Predictive Health Risk Assessment (PHRA) interspersed with Adaptive Conjoint Analysis (ACA) questions. [0149] the ACA is tailored to each consumer. [0045] the system uses the health data input coupled with an inference engines, to provide a comprehensive set of future attributes useful to assess the level of impact through various consumer-engagement channels.), and
providing the at least one question from the questionnaire rules engine to the graphical user display before or at a time of contact (See Kil [0170] the system calculates future health trajectories and guides the user through the benefit selection process based on an adaptive questionnaire tree. [0171] the evaluation is based on the user responses to the PHRA questionnaire.).
Regarding claim 4, Kil in view of Francois discloses the method of claim 1 as discussed above. Kil does not further disclose a method, comprising:
updating training of the treatment engine by incorporating responses from the contact with the particular patient to update future treatment recommendations for the at least some of the plurality of patients.
Francois teaches:
updating training of the treatment engine by incorporating responses from the contact with the particular patient to update future treatment recommendations for the at least some of the plurality of patients (See Francois [0118] the system can use learning models which take historical patient raw data and final outcomes data to derive appropriate scoring regimes. This includes models that evolve over time using scoring models derived from the raw data and outcome data gathered by the system. This meets the broadest reasonable interpretation of updating the training model from newly gathered patient data.).
The system of Francois is applicable to the disclosure of Kil as they both share characteristics and capabilities, namely, they are directed to using patient data to make treatment recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kil to include third party databases and updated learning models as taught by Francois. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kil in order to address issues associated with the fragmentary and often inaccessible nature of existing health data (see Francois [0003]).
Regarding claim 5, Kil in view of Francois discloses the method of claim 1 as discussed above. Kil does not further disclose a method, comprising:
updating the training of the risk prediction model by incorporating responses from contact with the particular patient to potentially update the risk prediction for the particular patient.
Francois teaches:
updating the training of the risk prediction model by incorporating responses from contact with the particular patient to potentially update the risk prediction for the particular patient (See Francois [0118] the system can use learning models which take historical patient raw data and final outcomes data to derive appropriate scoring regimes. This includes models that evolve over time using scoring models derived from the raw data and outcome data gathered by the system. This meets the broadest reasonable interpretation of updating the training model from newly gathered patient data.).
The system of Francois is applicable to the disclosure of Kil as they both share characteristics and capabilities, namely, they are directed to using patient data to make treatment recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kil to include third party databases and updated learning models as taught by Francois. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kil in order to address issues associated with the fragmentary and often inaccessible nature of existing health data (see Francois [0003]).
Regarding claim 7, Kil in view of Francois discloses the method of claim 1 as discussed above. Kil further discloses a method, wherein:
the generating the treatment recommendation includes: automatically querying the treatment engine, based in part on the risk prediction (See Kil [0046] the system can include an intervention opportunity finder. Impact assessment can be made based on the aggregate future impact of all the identified targets of opportunities. [0048] the system can then create rules of engagement for statistically significant outcomes. [0163] the system can determine treatments or interventions that the patient can be used to reduce probability of a disease progression. See also Figs. 6 and 9 and [0233].), the querying including execution of a recommendation rule by the treatment engine (See Kil [0046] the system can include an intervention opportunity finder. Impact assessment can be made based on the aggregate future impact of all the identified targets of opportunities. [0048] the system can then create rules of engagement for statistically significant outcomes.), and
generating a recommendation string that characterizes the treatment recommendation (See Kil [0163] the system can determine treatments or interventions that the patient can be used to reduce probability of a disease progression. See also Figs. 6 and 9 and [0233].).
Regarding claim 8, Kil in view of Francois discloses the method of claim 7 as discussed above. Kil further discloses a method, wherein:
the treatment engine is automatically modified by a predictive model that identifies a predictor variable characterizing a likelihood of success of an intervention characterized by the treatment recommendation, the predictive model configured to automatically identify when feedback data from the contact indicates a level of success of the intervention is received, and automatically modify the training of the treatment engine (See Kil [0162] the system can determine the probability of transitioning from one disease state to another disease state based on whether the consumer obtains a prescribed treatment. [0048] the system uses predictive modeling, combinatorial and stochastic feature optimization with respect to outcomes, and propensity-score shaping. The system create rules of engagement for statistically significant outcomes (i.e., “level of success” based on “likelihood of success”), which are further validated through focus-group study and survey. Validated rules (i.e., the modified recommendation rule) are stored in the master rules database for production implementation. [0050] the algorithm library (which includes the learning algorithms) gets updated with the latest discoveries. See also Fig. 1 shows the validation performed by the impact analysis engine is used in a cycle with the other parts of the system, including the health trajectory predictors and the predictive models.).
Regarding claim 9, Kil in view of Francois discloses the method of claim 7 as discussed above. Kil further discloses a method, wherein:
the providing at least part of the treatment recommendation includes transmitting the recommendation string a list of the plurality of tasks for presentation on a graphical user interface of the digital device (See Kil [0144] the system can present results on a GUI. [0180] the system can include a display device. See also Fig. 7 and [0174]-[0176].), wherein the plurality of tasks are associated with carrying out the treatment recommendation and at least one of the plurality of tasks are presented with a priority level characterized by at least one risk subcategory level prediction (See Kil Fig. 6 and [0162] the disclosure gives an example of risk prediction for disease progression related to diabetes. This include the over risk prediction from one step to another, as well as the sub categories including risk or progression with treatment and without treatment. It is understood that the tasks involved in the treatment subcategory are a higher priority to the tasks in the no treatment category.).
Regarding claim 10, Kil in view of Francois discloses the method of claim 7 as discussed above. Kil further discloses a method, wherein:
the treatment engine is trained by a recommendation predictive model that identifies a predictor variable characterizing a pattern in adherence to interventions suggested by the treatment recommendation and automatically modifies a rule of the treatment engine based on the identification (See Kil Fig. 1 and [0046] the system can use treatment adherence as part of determining the recommendation. [0047] the system can determine what works for which population segments, by how much, and why. [0078] the system is trained/updated for new medical developments, such as introduction of new medical technologies and drugs, changes in benefit plans and fee-reimbursement schedules, changing demographics, and even macroeconomic cycles can affect data characteristics. [0134] the input for the system can include patient history. See also [0231].).
Regarding claim 11, Kil in view of Francois discloses the method of claim 6 as discussed above. Kil further discloses a method, wherein:
the determining of the risk prediction for the particular patient includes automatically determining a clinical risk parameter characterizing a level of clinical risk (See Kil Fig. 1 and [0045] the system can output, based on the received data and prediction modeling, a set of health scores including health scores and clinical scores. See also [0084]-[0090].), determining a social risk parameter characterizing a level of social risk (See Kil Fig. 1 and [0045] the system can output, based on the received data and prediction modeling, a set of health scores including behavior/lifestyle scores, engagement scores, and impact scores. [0046] the system can also include the use of psychosocial parameters. See also [0084]-[0090].), and
determining a behavioral risk parameter characterizing a level of behavioral risk, the risk prediction being based on at least one of the clinical risk parameter, the social risk parameter, and the behavioral risk parameter (See Kil Fig. 1 and [0045] the system can output, based on the received data and prediction modeling, a set of health scores including behavior/lifestyle scores and engagement scores. See also [0084]-[0090].).
Regarding claim 12, Kil in view of Francois discloses the method of claim 11 as discussed above. Kil further discloses a method, wherein:
one or more of the clinical risk parameter, the social risk parameter, and the behavioral risk parameter is automatically updated based on the particular patient's response during the contact (See Kil [0048] the system uses predictive modeling, combinatorial and stochastic feature optimization with respect to outcomes, and propensity-score shaping. [0172] the health scores can be updated to show improvement, by having the user take the PHRA over time. [0266] system can include consumer feedback that can be asynchronous (adheres to predefined or user-customizable feedback criteria), regular (once a day, for example), or on demand.).
Regarding claim 13-15 and 17-19, Kil in view of Francois discloses the method of claims 1-3, 7-8, and 10 as discussed above. Claim 13-15 and 17-19 recite a system that performs a method substantially similar to the method of claims 1-3, 7-8, and 10. Accordingly, claims 13-15 and 17-19 are rejected based on the same analysis.
Regarding claim 20, Kil in view of Francois discloses the method of claim 1 as discussed above. Claim 20 recites a non-transitory computer program product storing instructions to perform a method substantially similar to the method of claim 1. Accordingly, claim 20 is rejected based on the same analysis.
Response to Arguments
35 U.S.C. §101
Applicant's arguments filed 19 June 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 are not directed to a method of organizing human activity because they recite computational architecture that cannot be separated from the risk model or other computer structures (see Applicant Remarks pages 9-12). This is not persuasive. The structure recited can be reasonably separated from the performance of the method because the architecture is recited at such a high level of generality. Therefore, the broadest reasonable interpretation of the claims include the collecting patient data, analyzing patient data, and determining a proper diagnoses/treatment based on that analysis. This is a method of organizing human activity because it is an interaction between a patient and a healthcare provider. Under Step 2A Prong One, the broadest reasonable interpretation of the claims include the interaction between a healthcare provide and a patient.
Next, Applicant argues that the abstract idea is integrated into a practical application because it aggregates and analyzes population data in a more efficient manner than previously possible (under step 2A Prong Two), which Applicant argues is an improvement to technology (see Applicant Remarks pages 12-14). This is not persuasive. The claims do note recite an improvement to technology. While the claims could collect vast amounts of data, this is understood to be an intended result and not a clear technological improvement based on the recited claim elements. The additional elements present in the claims do no more than 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)). This is not enough to integrate the abstract idea into a practical application.
Next, Applicant argues that the present claims are analogous to Example 42 of the PEG because they recite “novel technical features solving technical problems” (see Applicant Remarks pages 20-22). This is not persuasive. First, the use of the word “novel” implies a prior art finding under section 102. The prior art analysis under 102 (and 103) are not applicable to the eligibility analysis under section 101. Second, as already discussed, the additional elements present in the newly amended claims do no more than 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)). This is not enough to integrate the abstract idea into a practical application.
Finally, Applicant argues that the claims include significantly more and are therefore eligible under Step 2B (see Applicant Remarks page 22). This is not persuasive. The additional elements present in the newly amended claims do no more than 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)). This is not enough to amount to significantly more than the judicial exception. Accordingly, the claims remain rejected for being directed to ineligible subject matter under section 101.
35 U.S.C. §103
Applicant’s arguments filed 19 June 2026, with respect to the 35 U.S.C. §103 rejection of the claims, have been fully considered but they are not persuasive. First, Applicant argues that the references fail to teach or suggest receiving healthcare information data at regular intervals from a plurality of data sources (see Applicant Remarks pages 16-17). This is not persuasive, in paragraph Kil [0172] (mislabeled as [0169] in the office action, however, [0172] is cited later in the office action) it states that the multimode health-trajectory predictors perform predictions both on a regular basis and asynchronously. This meets the broadest reasonable interpretation of the broadly stated “regular intervals.”
Next, Applicant argues that the references fail to teach or suggest a risk prediction that includes an overall risk prediction and a plurality of risk subcategory predictions in claim 1 (see Applicant Remarks pages 18-19). This is not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “plurality of risk subcategory predictions”) are not recited in rejected claim 1. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Accordingly, the references do not need to teach or suggest this unrecited element in claim 1.
Next, Applicant argues that the references fail to teach or suggest associating a predictor variable with the risk prediction and using the predictor variable to generate a treatment recommendation in claim 1 (see Applicant Remarks pages 19-20). This is not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “predictor variable”) are not recited in rejected claim 1. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Accordingly, the references do not need to teach or suggest this unrecited element in claim 1.
Next, Applicant argues that the references fail to teach or suggest determining a plurality of tasks characterized by the treatment recommendation, utilization pattern, risk prediction, risk subcategory predictions, and predictor variable and providing those tasks on a display (see Applicant Remarks pages 20-21). This is not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a plurality of tasks”) are not recited in rejected claim 1. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Accordingly, the references do not need to teach or suggest this unrecited element in claim 1.
Finally, Applicant argues that the motivation to combine does not support the claimed combination (see Applicant Remarks pages 21). In response to Applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Francois provides the motivation to combine, doing so to address issues associated with the fragmentary and often inaccessible nature of existing health data (see Francois [0003]). Applicant argues that this does not apply to “modifying Kil to include the claimed regular-interval multi-source data receipt architecture, the hierarchical risk prediction structure, the predictor-variable association mechanism, or the integrated task-determination and display requirements discussed above.” Francois is not being used to teach these elements, and so this argument is moot. Accordingly, the claims are remain rejected as being obvious in view of the Kil and Francois references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Heywood et al (U.S. 2009/0125333) discloses a system for personalized management and comparison of medical condition and outcome based on patient profiles of a community of patients.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.L.H./Examiner, Art Unit 3684
/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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