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 Nonfinal Office Action is in response to the RCE filed 05/05/2026. Claims 1-20 are currently pending and considered herein.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/05/2026 has been entered.
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 1-20 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract idea is not emboldened:
A method for dynamically evaluating health care risk, the method being implemented by machine-readable instructions, the method comprising: managing, for a set of users, a corresponding set of digital healthcare profiles, where each digital healthcare profile includes a healthcare decision tree for the corresponding user; determining, for a first user of the set of users, a first subset of the set of users with a first set of similar digital healthcare profiles based on the corresponding set of healthcare decision trees; determining, based on correlation between a first healthcare decision tree of the first user with the first set of healthcare decision trees of the first subset of users, a first recommended action for the first user; providing, to the first user, information related to the first recommended action; updating, for a second user in the first subset of users, a second digital healthcare profile and second healthcare decision tree of the second user; determining, based on the updated second healthcare decision tree of the second user, that the updated second healthcare decision tree deviates from the first set of healthcare decision trees; removing, based on the determined deviation, the second user from the first subset of users; determining, based on the updated first set of digital healthcare profiles, a second recommended action to the first user; and providing, to the first user, information related to the second recommended action.
Independent claims 9 and 16 recite substantially similar limitations and further include a “physical processor” (Claim 9) and “A non-transitory machine-readable storage medium comprising instructions executable by a physical processor of a computing device” (Claim 16). The claimed invention is broadly directed to the abstract idea of collecting patient(s) information, analyzing the information, and determining a recommendation based on the analyses.
The limitations of “managing, for a set of users, a corresponding set of healthcare profiles, where each digital healthcare profile includes a healthcare decision tree for the corresponding user; determining, for a first user of the set of users, a first subset of the set of users with a first set of similar healthcare profiles based on the corresponding set of healthcare decision trees; determining, based on correlation between a first healthcare decision tree of the first user with the first set of healthcare decision trees of the first subset of users, a first recommended action for the first user; providing, to the first user, information related to the first recommended action; updating, for a second user in the first subset of users, a second healthcare profile and second healthcare decision tree of the second user; determining, based on the updated second healthcare decision tree of the second user, that the updated second healthcare decision tree deviates from the first set of healthcare decision trees; removing, based on the determined deviation, the second user from the first subset of users; determining, based on the updated first set of healthcare profiles, a second recommended action to the first user; and providing, to the first user, information related to the second recommended action,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic computer system language in the preamble of claim 1 and digital profiles of claim 1, “physical processor” and digital profiles of claim 9 and “non-transitory machine-readable storage medium,” “physical processor” and digital profiles of claim 16, analyzing patient data including comparing the patient data to other patients and those data are used for recommendations, in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing users to access patient data, analyze the data, and determine actions or recommendations based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a patient and physician (e.g. patient/user data compared to other patient/user data, analyzing the comparisons/healthcare profiles, and determining a course of action for the patient/user). Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a)-(g). The mere nominal recitation of a generic computer system and processor and non-transitory data storage medium does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The claims can also be classified as an abstract idea including mental processes. That is, other than reciting a generic computer system, processor and storage medium, and digital profiles nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the generic computing devices and language, generating diagnostic results based on patient data and a mathematical model, in the context of this claim, encompasses one skilled in the pertinent art to manually determine the details of the data for comparison and prognosis for a patient compared to other patients and decisions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by machine-readable instructions on a computer system, including processors and storage medium for dynamically evaluating health care risk and analyzing digital healthcare profiles. The devices and the digital healthcare profile in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying selected information, or as mathematical concepts) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient analysis comparing one group of patients to another group or individual patient and general diagnostic techniques between a clinician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
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 elements of being implemented by machine-readable instructions on a computer system, including processors and storage medium and digital healthcare profiles amounts to no more than mere instructions to apply the exception using a computer component and mathematical concepts. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the limitation, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claims 2, 10 and 17 describe how to update a digital healthcare profile, which is recited at a high level of generality and even in combination the updating of digital profiles does not integrate the exception into a practical application or provide significantly more than the abstract idea. Claims 3 and 11 describe a relationship between users and decision tree is statistically significant and further limit the abstract idea. Claims 4 and 12 describe a comparison to decision trees that are statistically closer to a different set of decision trees and further defines the abstract idea. Claim 5 determines a new healthcare action that was the reason the decision trees deviated and further defines the abstract idea. Claims 6, 13 and 18 describe determining subsequent actions and further defines the abstract idea. Claims 7, 14 and 19 further analyze information and provide a recommendation based on different decision trees and further defines the abstract idea. Claims 8, 15 and 20 prioritize a recommended action based on analyzing information and a digital healthcare profile of the user, which is recited at a high level of generality and even in combination providing recommendations based on analyzing digital healthcare profiles does not integrate the exception into a practical application or provide significantly more than the abstract idea.
Therefore, for at least the reasons discussed above the claims are not patent eligible.
Claims 1, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0158909 A1 to Ng et al., hereinafter “Ng,” in view of U.S. 2015/0205921 A1 to Dick et al., hereinafter “Dick.”
Regarding claim 1, Ng discloses A method for dynamically evaluating health care risk, the method being implemented by machine-readable instructions, the method comprising: managing, for a set of users, a corresponding set of digital healthcare profiles, where each digital healthcare profile includes a healthcare decision tree for the corresponding user (See Ng at least at Abstract (“[C]omputing precision cohort treatment options from the decision points using precision cohort analytics on a patient group and analyzing the decision points by comparing the actual treatment options with the precision cohort treatment options for each of the decision points to determine recommended measures.”); Paras. [0003]-[0005], [0041]-[0049] (Electronic health records database and decision trees (points) for patients); Claims 2-7; Figs. 1-2, 6-9); determining, for a first user of the set of users, a first subset of the set of users with a first set of similar digital healthcare profiles based on the corresponding set of healthcare decision trees (See id. at least at Abstract; Paras. [0031]-[0034] (“A precision cohort groups patients based on a quantitative measure of similarity between patients in terms of a particular clinical outcome.”), [0041], [0048]-[0050] (“[T]he precision cohort analytics system 200 configured to compute precision cohort treatment options 253 for a patient group based on the decision points 230 identified by the records retriever 220. A precision cohort refers to a group of patients who are similar to a given patient group of interest in a clinically meaningful way.”), [0054]-[0055]; Claims 2-7; Figs. 1-2, 6-9); determining, based on correlation between a first healthcare decision tree of the first user with the first set of healthcare decision trees of the first subset of users, a first recommended action for the first user (See id. at least at Abstract (recommended measures); Paras. [0002]-[0005] (“A patient cohort refers to any group of individuals affected by common diseases, environmental or temporal influences, treatments, or other traits whose progress can be assessed.”), [0026]-[0030] (“Using precision cohort analytics, a more precise definition of a cohort can refine the treatments given to a specific grouping of patients. By analyzing those treatments and the effectiveness of those treatments, an alternative treatment option can be devised for the patient cohort. A comparison can be made between the actual treatment options and the precision cohort treatment options to determine recommended measures that can be taken.”), [0046]-[0050] (“[C]ompute precision cohort treatment options 253 for a patient group based on the decision points 230 identified by the records retriever 220.”), [0080]-[0084] (“The precision cohort calculator 250 computes the precision cohort treatment options relating to the decision points 230 using precision cohort analytics on a patient group.”); Claims 2-7; Figs. 1-2, 6-9); and providing, to the first user, information related to the first recommended action (See id.); determining, based on the updated first set of digital healthcare profiles, a second recommended action to the first user; and providing, to the first user, information related to the second recommended action ((See id. at least at Abstract (recommended measures); Paras. [0003]-[0005] (“[C]omputing precision cohort treatment options from the decision points using precision cohort analytics on a patient group and analyzing the decision points by comparing the actual treatment options with the precision cohort treatment options for each of the decision points to determine recommended measures. The patient group can be chosen from a group consisting of a population cohort, a separate patient cohort, a patient panel, and from an individual.”), [0026]-[0030] (“Using precision cohort analytics, a more precise definition of a cohort can refine the treatments given to a specific grouping of patients. By analyzing those treatments and the effectiveness of those treatments, an alternative treatment option can be devised for the patient cohort. A comparison can be made between the actual treatment options and the precision cohort treatment options to determine recommended measures that can be taken.”), [0046]-[0048] (“[C]ompute precision cohort treatment options 253 for a patient group based on the decision points 230 identified by the records retriever 220.”), [0056], [0080]-[0084] (“The precision cohort calculator 250 computes the precision cohort treatment options relating to the decision points 230 using precision cohort analytics on a patient group.”); Claims 2-7; Figs. 1-2, 6-9); updating, for a second user in the first subset of users, a second digital healthcare profile and second healthcare decision tree of the second user (See id. at least at Paras. [0003]-[0005] (“[C]omputing precision cohort treatment options from the decision points using precision cohort analytics on a patient group and analyzing the decision points by comparing the actual treatment options with the precision cohort treatment options for each of the decision points to determine recommended measures. The patient group can be chosen from a group consisting of a population cohort, a separate patient cohort, a patient panel, and from an individual.”), [0026]-[0030] (“Using precision cohort analytics, a more precise definition of a cohort can refine the treatments given to a specific grouping of patients. By analyzing those treatments and the effectiveness of those treatments, an alternative treatment option can be devised for the patient cohort. A comparison can be made between the actual treatment options and the precision cohort treatment options to determine recommended measures that can be taken.”), [0041]-[0050], [0055]-[0057]); and determining, based on the updated second healthcare decision tree of the second user, that the updated second healthcare decision tree deviates from the first set of healthcare decision trees (See id. at least at Paras. [0003]-[0005], [0026]-[0032] (“Also, the treatment options can further be compared to quantify the amount of treatment decision overlap that has occurred. The outcomes can also be further compared to quantify differences in expected control of the conditions […] the precision cohort treatment options can be performed for different patient groups. The different patient groups include, and without limitation, population groups, different patient cohorts, patient panels, as well as individual patients […] A precision cohort groups patients based on a quantitative measure of similarity between patients in terms of a particular clinical outcome. The precision cohort analytics can generate personalized treatment options based on the decision points. Once generated, treatment option suggestion can be chosen from the generated personalized treatment options. These suggestions can be the treatment options which have the highest likelihood of a positive outcome […] a comparison can be made of the actual treatment options with precision cohort treatment options to quantify an amount of treatment decision overlap. The comparison can assist healthcare providers in determining if the actual treatment options are the correct course of action for a given patient cohort. The outcomes can also be compared to further give insight into how closely the results of the actual treatment options are with the precision treatment options.”), [0053]-[0059], [0063] (“Decision points 230 with not treatment option change is listed, as well as decision points 230 with precision cohort treatment options indicating a significant different estimated associated outcome 259 to the actual associated outcome 248 are listed.”); Claims 2-7; Figs. 1-3, 6-9).
Ng may not specifically describe but Dick teaches removing, based on the determined deviation, the second user from the first subset of users (See Dick at least at Paras. [0067], [0069], [0095]; Figs. 2, 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng to incorporate the teachings of Dick and provide decision tree comparison and removal of certain variables including related or unrelated patients. Dick is directed to systems for electronic healthcare data and associations between users. Incorporating the healthcare data management and comparisons as in Dick with the precision cohort analytics and generating treatment outcomes with decision points as in Ng would thereby improve the functionality and applicability of evaluating health risks and treatments.
Regarding claims 9 and 16, claims 9 and 16 recite substantially the same limitations as included in claim 1. Thus, claims 9 and 16 are rejected under the same grounds of rejection and for the same reasoning as applied to claim 1, above. Furthermore, the physical processor of claim 9 is found in Ng at Fig. 7, Paras. [0004]-[0005], [0071]; while the non-transitory machine-readable storage medium executable by a physical processor of a computing device of claim 16 is found at least at Fig. 7 and Paras. [0004]-[0005], [0071], [0091] and [0120]-[0126] of Ng.
Claims 2-5, 7-8, 10-12, 14-15, 17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ng, in view of Dick and further in view of U.S. 11,322,250 B1 to Laster et al., hereinafter “Laster.”
Regarding claim 2, Ng as modified by Dick discloses all the limitations of claim 1. The references may not specifically describe but Laster teaches updating the first healthcare profile of the first user by adding a new healthcare action to the first healthcare decision tree of the first user (See Laster at least at Col. 2, ln. 13-25; Col. 14, ln. 32-51; Col. 15 ln. 1-13; Col. 15 ln. 18-49; Col 18., ln. 41 – Col. 19, ln. 33 (peer pairing and creating new hybrid living care paths); Figs. 1, 7A-E, 8, 10-11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and provide adding and removing users and subsets of a patient population for analyses. Laster is directed to systems for an intelligent medical care path and comparison between patients. Incorporating the intelligent medical care path and patient analyses as in Laster with the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would improve the applicability of dynamically evaluating health care risk for patients.
Regarding claim 3, Ng as modified by Dick discloses all the limitations of claim 1. The references may not specifically describe but Laster teaches wherein determining that the updated second healthcare decision tree deviates from the first set of healthcare decision trees comprises: determining that the updated second healthcare decision tree is statistically significantly different from the first set of healthcare decision trees. (See Laster at least at Col. 2, ln. 13-25 (differential between living care path and present performance and a plurality of measures); Col. 8, ln. 23-63; Col. 13, ln. 57 – Col. 15 ln. 67; Col. 16, ln. 33 – Col. 17 ln. 37); Figs. 1, 7A-E, 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and provide statistically significant deviations. Laster is directed to systems for an intelligent medical care path and comparison between patients. Incorporating the intelligent medical care path and patient analyses as in Laster with the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would improve the outcomes for evaluating health care risk for patients.
Regarding claim 4, Ng as modified by Dick discloses all the limitations of claim 1. The references may not specifically describe but Laster teaches wherein determining that the updated second healthcare decision tree deviates from the first set of healthcare decision trees comprises: determining that the updated second healthcare decision tree is statistically closer to a different set of healthcare decision trees than the first set of healthcare decision trees of the first subset of users (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and provide statistically significant deviations and comparisons. Laster is directed to systems for an intelligent medical care path and comparison between patients. Incorporating the intelligent medical care path and patient analyses as in Laster with the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would improve the chances for success of treatment.
Regarding claim 5, Ng as modified by Dick discloses all the limitations of claim 1. The references may not specifically describe but Laster teaches wherein determining that the updated second healthcare decision tree deviates from the set of healthcare decision trees comprises: determining that a new healthcare action that caused the updating of the second healthcare caused the updated second healthcare decision tree to deviate from the first set of healthcare decision trees (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and provide statistically significant deviations. Laster is directed to systems for an intelligent medical care path and comparison between patients. Incorporating the intelligent medical care path and patient analyses as in Laster with the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would benefit analyzing potential treatment for potential patient cohort groups or subsets.
Regarding claim 7, Ng as modified by Dick discloses all the limitations of claim 1 and Ng further discloses determining, for the first user, a second subset of users with a second set of similar digital healthcare profiles based on a second corresponding set of healthcare decision trees (See Ng at least at Abstract; Paras. [0002]-[0005], [0023], [0028] (patient cohorts, i.e., sets and subsets of patients and decisions related to them), [0048]-[0050], [0079]-[0085]; Claims 7-11 (“[A]nalyzing the decision points by comparing the actual treatment options with the precision cohort treatment options comprises: determining a clinical inertia by analyzing the decision points for no treatment change; comparing the actual treatment options with the precision cohort treatment options to quantify an amount of treatment decision overlap; comparing actual treatment outcomes with precision cohort outcomes to quantify a difference in expected control.”); Figs. 1-3, 6-9).
The references may not specifically describe but Laster teaches determining, based on correlation between the first healthcare decision tree of the first user with the first set of healthcare decision trees of the first subset of users and the second set of healthcare decision trees of the second subset of users, a third recommended action for the first user; and providing, to the first user, information related to the third recommended action (See Laster at least at Col. 2, ln. 13-25; Col. 8, ln. 23-63; Col. 13, ln. 57 – Col. 15 ln. 67; Col. 16, ln. 33 – Col. 17 ln. 37); Figs. 1, 7A-E, 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and provide statistically significant deviations and various recommendations. Laster is directed to systems for an intelligent medical care path and comparison between patients. Incorporating the intelligent medical care path and patient analyses as in Laster with the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would improve treatment for potential patient cohort groups or subsets.
Regarding claim 8, Ng as modified by Dick discloses all the limitations of claim 1 and Ng further discloses wherein determining the first recommended action comprises: determining, based on the correlation between the first healthcare decision tree and the first set of healthcare decision trees of the first subset of users, a set of recommended actions, the set of recommended actions including the first recommended action; prioritizing the set of recommended actions based on factors relevant to the first user based on the first healthcare profile of the first user; and determining, based on the prioritization, the first recommended action (See id. at least at Abstract (recommended measures); Paras. [0002]-[0005] (“A patient cohort refers to any group of individuals affected by common diseases, environmental or temporal influences, treatments, or other traits whose progress can be assessed.”), [0026]-[0031] (“Using precision cohort analytics, a more precise definition of a cohort can refine the treatments given to a specific grouping of patients. By analyzing those treatments and the effectiveness of those treatments, an alternative treatment option can be devised for the patient cohort. A comparison can be made between the actual treatment options and the precision cohort treatment options to determine recommended measures that can be taken […] A precision cohort groups patients based on a quantitative measure of similarity between patients in terms of a particular clinical outcome. The precision cohort analytics can generate personalized treatment options based on the decision points. Once generated, treatment option suggestion can be chosen from the generated personalized treatment options. These suggestions can be the treatment options which have the highest likelihood of a positive outcome.”), [0046]-[0050] (“[C]ompute precision cohort treatment options 253 for a patient group based on the decision points 230 identified by the records retriever 220.”), [0080]-[0084] (“The precision cohort calculator 250 computes the precision cohort treatment options relating to the decision points 230 using precision cohort analytics on a patient group.”); Claims 2-7; Figs. 1-2, 6-9).
Regarding claims 10 and 17, claims 10 and 17 recite substantially the same limitations as included in claim 2. Thus, claims 10 and 17 are rejected under the same grounds of rejection and for the same reasoning as applied to claim 2, above.
Regarding claim 11, claim 11 recites substantially the same limitations as included in claim 3. Thus, claim 11 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 3, above.
Regarding claim 12, claim 12 recites substantially the same limitations as included in claim 4. Thus, claim 12 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 4, above.
Regarding claims 14 and 19, claims 14 and 19 recite substantially the same limitations as included in claim 7. Thus, claims 14 and 19 are rejected under the same grounds of rejection and for the same reasoning as applied to claim 7, above.
Regarding claims 15 and 20, claims 15 and 20 recite substantially the same limitations as included in claim 8. Thus, claims 15 and 20 are rejected under the same grounds of rejection and for the same reasoning as applied to claim 8, above.
Claims 6, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ng, in view of Dick, in view of Laster and further in view of U.S. 2016/0092641 A1 to Delaney et al., hereinafter “Delaney,”
Regarding claim 6, Ng as modified by Dick discloses all the limitations of claim 1 and Ng further discloses updating the first healthcare decision tree based on the first recommended action; updating the first subset of users based on the updated first healthcare decision tree (See id. at least at Abstract (recommended measures); Paras. [0002]-[0005] (“A patient cohort refers to any group of individuals affected by common diseases, environmental or temporal influences, treatments, or other traits whose progress can be assessed.”), [0026]-[0032] (“Using precision cohort analytics, a more precise definition of a cohort can refine the treatments given to a specific grouping of patients. By analyzing those treatments and the effectiveness of those treatments, an alternative treatment option can be devised for the patient cohort. A comparison can be made between the actual treatment options and the precision cohort treatment options to determine recommended measures that can be taken.”), [0046]-[0050] (“[C]ompute precision cohort treatment options 253 for a patient group based on the decision points 230 identified by the records retriever 220.”), [0058]-[0067] (changes in treatment options and decision points), [0080]-[0084] (“The precision cohort calculator 250 computes the precision cohort treatment options relating to the decision points 230 using precision cohort analytics on a patient group.”); Claims 2-7; Figs. 1-2, 6-9);
The references may not specifically describe but Laster teaches determining a fourth recommended action based on the correlation between the first healthcare decision tree and the updated first subset of users; and providing the fourth recommendation to the first user (See Laster at least at Col. 2, ln. 13-25; Col. 14, ln. 32-51; Col. 15 ln. 1-13; Col. 15 ln. 18-49; Figs. 1, 7A-E, 8).
While Delaney teaches determining whether the first user executed on the first recommended action (See Delaney at least at Paras. [0071], [0073], [0075], [0132], [0155], [0157], [0158], [0233]-[0234]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Ng and Dick to incorporate the teachings of Laster and Delaney and provide determining a user action and adding and removing users and subsets of a patient population for analyses. Laster is directed to systems for an intelligent medical care path and comparison between patients. Delaney relates to facilitating clinically informed decisions to improve healthcare performance. Incorporating the intelligent medical care path and patient analyses as in Laster with the clinical decision-making techniques of Delaney, the healthcare data management and comparisons as in Dick and the precision cohort analytics and generating treatment outcomes with decision points as in Ng would improve treatments and adherence to treatment for patient cohorts or groups as well as individuals.
Regarding claims 13 and 18, claims 13 and 18 recite substantially the same limitations as included in claim 6. Thus, claims 13 and 18 are rejected under the same grounds of rejection and for the same reasoning as applied to claim 6, above.
Response to Arguments
Applicant’s remarks filed May 5, 2026 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks at Pages 15-18 are not persuasive. The arguments at Pages 15-18 regarding Schmidt and Bostic are moot in light of new reference Ng. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for patient data analysis and diagnoses/recommendations, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative and the claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under the relevant sections of the MPEP. The arguments at pages 10-15 of Applicant’s Remarks are not persuasive. At pages 10-12 the Examiner respectfully disagrees that there is not an abstract idea. The claims are directed to the abstract idea of methods of organizing human activity (judicial exception) as well as mental processes, discussed above. Moreover, there is no practical application and there is no technological improvement recited in the claims. Thus, the claims recite an abstract idea.
The Examiner respectfully disagrees at Pages 12-14 that the claims recite an exception that is integrated into a practical application in the claims. The Examiner disagrees that the decision trees are at all similar to the “well-known Arrhenius equation” as in Diehr. The decision trees are part of the abstract idea judicial exception of mental processes and organizing human activity. The Examiner further disagrees at Page 13, because there is not a recitation of a “reduction in errors in realtime by recommending actions based on [those] undertaken by users with similar decision trees,” in the claims as otherwise asserted by Applicant. Further, it is not apparent in the claims or the Specification that the filtering of decision trees and updating user lists is improving any underling computer technology. Moreover, “Recommending appropriate action to a user,” as argued by Applicant is actually part of the abstract idea and is not a practical application thereof. The nominal recitation of the additional elements of a generic computer system and digital healthcare profiles is not sufficient to integrate the abstract idea into a practical application. The computer and digital profiles are recited at a high level, and amount to applying the exception using a generic computer component (See e.g. Updated PEG Example 47, claim 2, where the “detecting” and “analyzing” were mental processes, and “using the trained ANN” (similar to software executing on a computer for analyzing and comparing healthcare decision trees and detecting variations) amounted to generic computer implementation). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claims recite an abstract idea.
Furthermore, the Examiner respectfully disagrees with arguments at Page 15 that the claims do not preempt an abstract idea. The claims amount to collecting patient data for multiple patients and determining recommendations based on analyzing the data, a process that can be done manually and that is also a method of organizing human activity of following rules or instructions between a patient and physician/healthcare group. It appears the claims try to monopolize the abstract idea of patient analysis comparing one group of patients and decisions to another group or individual patient and general diagnostic techniques between a clinician and her patient. The claims do not include additional elements to overcome the abstract idea and show a practical application thereof in the claims or technological improvement to the underlying computer devices.
For at least these reasons and those stated above, the claims are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2018/0144820 A1 to Grimmer for health decision trees for users. U.S. 2017/0006135 A1 to Siebel et al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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