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 .
DETAILED ACTION
In the Amendment dated 06 March 2026, the following occurred:
Claims 1, 7, and 15 were amended.
Claims 1-21 are pending.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 7, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite systems and methods for determining a patient prioritization score of a patient, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1 being representative) receiving healthcare data for each particular patient of the plurality of patients from a plurality of different sources, wherein the healthcare data has a plurality of differently-structured computer data formats; determining, based on processing the healthcare data for each particular patient having the plurality of differently-structured computer data formats, a respective patient prioritization score for each particular patient of the plurality of patients, the determining comprising: using a first model layer to reduce complexity of the healthcare data for each particular patient having the plurality of differently-structured computer data formats by consolidating the healthcare data for each particular patient having the plurality of differently-structured computer data formats into an output of the first model layer having a standardized format configured for inputting to a second model layer, comprising: determining, using a care gap model in the first model layer, a care gap score for the particular patient based on a first subset of the healthcare data associated with the particular patient, the care gap score representing opportunities to improve care of the particular patient, and the determining comprising: determining a score for a particular care gap based on a result of determining whether the particular patient has the particular care gap; applying a weight to the score based on a type of the particular care gap to obtain a weighted score for the particular care gap; and determining the care gap score for the particular patient based on the weighted score for the particular care gap; determining, using a risk score model in the first model layer, a risk score for the particular patient based on a second subset of the healthcare data associated with the particular patient, the risk score representing risks to health of the particular patient; determining, based on a plurality of risk scores determined using the risk score model in the first model layer, a change over a time period in the risk score for the particular patient; determining, using a patient willingness model in the first model layer, a measure representing a willingness of the particular patient to receive care; determining, using a cost prediction model in the first model layer, a prediction of future costs associated with care of the particular patient; and using the second model layer to process the output of the first model layer having the standardized format, comprising determining, using a patient prioritization score model in the second model layer, the patient prioritization score for the particular patient based on the care gap score, the risk score, the change over the time period, and the prediction of the future costs; determining, based on the respective patient prioritization score determined for each of the plurality of patients, a respective level of care to be administered to each particular patient of the plurality of patients; and prompting the healthcare professional to administer the determined respective level of care to each particular patient of the plurality of patients, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting methods and a system implemented by at least one non-transitory computer-readable storage medium and at least one computer processor (a general-purpose computing device), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. The Examiner notes the various “models” are not exclusively defined in the Specification as being machine learning models and are merely functionally claimed. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of at least one computer-readable storage medium (claim 15) and at least one computer processor (claim 15) that implement the identified abstract idea. The computing elements are not exclusively described by the applicant and are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims recite the additional element of a graphical user interface (GUI). The GUI merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a general-purpose computer to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a GUI was considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more.
Claims 2-6, 8-14, and 16-21 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes the plurality of patients, which further defines the abstract idea.
Claim 3 merely describes determining the level of care, which further defines the abstract idea.
Claims 4, 8, and 17 merely describe determining a care gap score, which further defines the abstract idea.
Claims 5 and 9 merely describe the first subset of the healthcare data and applying the rule to the first subset of the healthcare data, which further defines the abstract idea.
Claims 6 and 14 merely describe receiving the healthcare data, which further defines the abstract idea.
Claim 10 merely describes the healthcare data and determining a risk score, which further defines the abstract idea.
Claims 11 and 18 merely describe determining a change over a time period in the risk score and determining the patient prioritization score, which further defines the abstract idea.
Claims 12 and 19 merely describe determining a measure representing a willingness of the patient to receive care and determining the patient prioritization score, which further defines the abstract idea.
Claims 13 and 20 merely describe determining a prediction of future costs and determining the patient prioritization score, which further defines the abstract idea.
Claim 16 merely describes receiving healthcare data, determining the care gap score, determining the risk score, and determining the patient prioritization score, which further defines the abstract idea.
Claim 21 merely describes authorizing remote access, receiving updated healthcare data, determining an updated patient prioritization score and a respective updated level of patient care, and prompting the healthcare professional to administer, which further defines the abstract idea.
Claim 21 further recites the additional element of a plurality of local devices having a GUI, which is considered to “generally link” under 2A2 and 2B.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-21, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Obtaining, storing, and processing such massive amounts of healthcare data for each of one or more (e.g., tens to millions) patients from several different sources to determine a PPS is a massive computational burden… The Specification describes that the combination of the first and second model layers improves the functioning of the machine learning models, which in turn “improve[s] efficiency” of a computer in “processing large volumes of healthcare data,” compared to conventional techniques.
Regarding (a), the Examiner respectfully disagrees. Claim 2 states, “the plurality of patients include[s] at least 2 patients.” Processing massive amounts of data is not reflected in the claims, and even if it were, that is using a computer for its intended task. There is no machine learning claimed. The various “models” are not exclusively defined in the Specification as being machine learning models as described below:
Care gap: Para. 0088 (the care gap score determination module may process the healthcare data using a machine learning model); Para. 0089 (the care gap score determination module 222 may be configured to use a machine learning model); Para. 0204 (For example, as described herein including at least with respect to FIG. 2, FIG. 3A, and the section “Care Gap Score,” a machine learning model may be used to identify and/or collect clinically-relevant data from a patient.)
Risk score: Not exclusively defined as machine learning anywhere in the Specification.
Patient willingness: Para. 0097 (For example, the patient willingness determination module 230 may be configured to use a machine learning model, such as a large language model, to generate prompts requesting input from a user (e.g., user(s) 260).); Para. 0204 (As another example, as described herein including at least with respect to FIG. 2, FIG. 3A, and the section “Patient Willingness,” a machine learning model may be used to generate prompts for users to provide responses to statements and/or questions used for measuring patient willingness.)
Cost Prediction: Para. 0187 (In some embodiments, a machine learning model may be used to predict future cost(s) and/or a cost range for a patient based on characteristics of the patient. For example, the machine learning model may be configured to process data such as patient gender and age, for example, to obtain an output indicative of the predicted future costs associated with care of that patient.)
Desjardins… By evaluating data associated with multiple healthcare factors, the techniques developed by the inventors are more comprehensive and therefore an improvement to conventional techniques…
Regarding (b), the Examiner respectfully disagrees. Initially, as described above, there is no machine learning claimed and thus machine learning cannot be improved. Even if it was, the Examiner respectfully submits that there is no improvement to the claimed machine learning as there is in Desjardins. As found by the Desjardins Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application- “improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Even assuming arguendo that Applicant’s claims recited machine learning, Applicant’s claims do not provide such an improvement. There is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the comprehensiveness of a machine learning model by supplying it with specific data (multiple healthcare factors) is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the comprehensiveness of the model by optimizing the data selected/used by the model. Improving the comprehensiveness of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
…the claims provide significant additional limitation beyond any alleged methods of organizing human activity…
Regarding (c), the Examiner respectfully disagrees. There is no machine learning present and even if there was, no improvement is present. The claim is directed to an abstract idea and the only additional element is a general-purpose computer.
As compared with well-understood, routine, conventional AI models, the limitations… provide for an inventive machine learning technique in which large volumes of data are processed by a first model layer to output a standardized format of intermediate scores, with the intermediate scores further processed by a second layer to determine a patient prioritization score, thereby improving the efficiency and accuracy of the machine learning model as compared to conventional techniques…
Regarding (d), the Examiner respectfully disagrees. There is no machine learning in the claim, and even if there was Applicant has not identified a specific improvement to the underlying operation of a computer, machine learning, or other technology. Merely applying multiple model layers to process data and generate a prioritization score (and/or other scores) does not demonstrate that the claimed technique is non-conventional or improves computer functionality. The alleged improvements in efficiency and accuracy are not tied to a particular technological environment.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Reddy et al. (U.S. 2015/0199782) which discloses methods and systems for population health risk stratification.
Morris et al. (U.S. 2011/0105852) which discloses techniques for generating prediction of risks of medical outcomes and benefit scores for medical interventions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert W Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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