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 Application filed 11/12/2024. Claims 1-20 are currently pending and considered herein.
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 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.
Claim 11 recites, wherein the abstract elements are not emboldened:
A system comprising: one or more processors, and memory storing computer program code executable by the one or more processors to cause the system to: receive electronic health data associated with a patient; identify, by a first machine learning model, one or more medical condition keywords from the electronic health data associated with the patient, wherein the first machine learning model is trained to identify medical condition keywords from training data comprising de-identified electronic health records paired with medical condition training keywords, the de-identified electronic health records comprising international classification of diseases (ICD) codes, clinical reports records, vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balances, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, or any combination thereof, associated with a plurality of de-identified patients; determine, by a second machine learning model, one or more recommended care providers for the patient based on the identified one or more medical condition keywords; generate, using the second machine learning model, an ordered list of the one or more recommended care providers, wherein the second machine learning model is trained to generate the ordered list from training data comprising medical condition training keywords paired with recommendations by care providers of other care providers treating one or more of the medical conditions indicated by the training keywords; and display the ordered list of the one or more recommended care providers on a user interface.
Independent claims 1 and 20 recite substantially similar limitations. The claimed invention is broadly directed to the abstract idea of collecting patient information including health data related to a patient, analyzing the information, and generating recommended care providers based on the analyses.
The limitations of “receive health data associated with a patient; identify one or more medical condition keywords from the health data associated with the patient, to identify medical condition keywords from training data comprising de-identified health records paired with medical condition training keywords, the de-identified health records comprising international classification of diseases (ICD) codes, clinical reports records, vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balances, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, or any combination thereof, associated with a plurality of de-identified patients; determine one or more recommended care providers for the patient based on the identified one or more medical condition keywords; generate an ordered list of the one or more recommended care providers, to generate the ordered list from training data comprising medical condition training keywords paired with recommendations by care providers of other care providers treating one or more of the medical conditions indicated by the training keywords; and display the ordered list of the one or more recommended care providers” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as organizing human activity. For example, but for the generic computer system including reciting training one or more machine learning models, a processor and memory, and displaying on a user interface, analyzing patient data, 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. The claim recites as a whole a method of organizing human activity because the limitations include a method that allows users to access myriad patient data, analyze the data and determine whether certain conditions are met based on the analyses (whether a medical condition is present based on the patient data and what provider to recommend based on the condition and patient data). This is a method of managing interactions between people. The mere nominal recitation of a generic training of a machine learning model, a processor and memory and a display on a user interface does not take the claims out of the method of organizing human interactions grouping. The additional limitations amount to computer methods for further implementing the abstract idea of organizing human activity. Thus, the claims recite an abstract idea.
The claims also recited an abstract idea including mental processes. But for the generic recitation the computer system including reciting training one or more machine learning models, a processor and memory, and displaying on a user interface, nothing in the claims is precluded from being performed in the mind. For example, a clinician can collect the de-identified patient data and analyze it and determine a recommended physician based on the analyses. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of the generic computer system including reciting training one or more machine learning models, a processor and memory, and displaying on a user interface. The computer and/or medical devices and functions 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) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 limitations seem to monopolize the abstract idea of patient data analysis and conditions and general techniques between a clinician and treating her patient. Furthermore, there is no clear improvement to the underlying computer technology found 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 the generic computer system including reciting training one or more machine learning models, a processor and memory, and displaying on a user interface, amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
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. Claims 2 and 12 further specifies displaying an interactive map, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the interactive map does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 3 and 13 defines filters and displaying drop-down menus, which are recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the filters and displaying drop-down menus do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Claims 4 and 14 include a user selection at a user interface which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the user selection at user interface does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 5 and 15 describe services and further limit the abstract idea. Claims 6 and 16 describe a readmission risk and machine learning model which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning and readmission risk do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 7 and 17 identify using another machine learning model CPT codes, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning and CPT codes do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 8 and 18 details affordance of a health plan and limits the abstract idea. Claim 9 describes providers and limits the abstract idea. Claims 10 and 19 detail a FHIR data format which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the use of FHIR format does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 10-12 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0038375 A1 to Mohtar et al., hereinafter “Mohtar,” in view of U.S. 2021/0257093 A1 to Griffin et al., hereinafter “Griffin” and further in view of U.S. 2024/0152990 A1 to Krafft et al., hereinafter “Krafft.”
Regarding claim 1, Mohtar discloses A computer-implemented method comprising: receiving electronic health data associated with a patient (See Mohtar at least at Paras. [000]-[0005], [0009]-[0010]; Figs. 1-4); identifying, by a first machine learning model, one or more medical condition keywords from the electronic health data associated with the patient (See id. at least at Abstract; Paras. [0003]-[0005] (“[D]eployment of trained machine learning models that identify relevant patient data that is to be incorporated into the fields of medical compliance forms […] , [0009]-[0010], [0141]-[0142]; Figs. 3-4, 7), wherein the first machine learning model is trained to identify medical condition keywords from training data comprising de-identified electronic health records paired with medical condition training keywords (See id. at least at Abstract; Paras. [0003]-[0005] (“[T]he patient associated with a patient identifier; transmitting the patient identifier to an entity, wherein the entity has access to the patient identifier, an identity of the patient, and corresponding patient data of the patient; receiving, from the entity, the corresponding patient data of the patient without receiving the identity of the patient.”), [0009]-[0010], [0040], [0114]-[0115], [0118]-[0119] (training data), [0141]-[0142] (“[T]raining documents (e.g., patient assessment forms and de-identified medical data) are supplied to train the predictive model.”); Fig. 3), the de-identified electronic health records comprising international classification of diseases (ICD) codes, clinical reports records, vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balances, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, or any combination thereof, associated with a plurality of de-identified patients (See id. at least at Paras. [0007]-[0009] (procedure codes and condition codes), [0071]-[0077] (ICD codes), [0114]-[0115]).
Mohtar may not specifically describe but Griffin teaches determining, by a second machine learning model, one or more recommended care providers for the patient based on the identified one or more medical condition keywords (See Griffin at least at Abstract; Paras. [0093]-[0094] (“[S]ent to the patient where the intervention provides a recommendation that the patient visit with a specialist to evaluate their condition […] data related to the patient such as the patient's medical condition, health metrics, which specialist the patient visited, and so forth (as discussed above), as well as the positive result may be used as a training data example from which a machine learning model may learn.”); Figs. 1, 5; Claims 3, 6, 7);
The references may not specifically describe but Krafft teaches generating, using the second machine learning model, an ordered list of the one or more recommended care providers, wherein the second machine learning model is trained to generate the ordered list from training data comprising medical condition training keywords paired with recommendations by care providers of other care providers treating one or more of the medical conditions indicated by the training keywords (See Krafft at least at Abstract; Paras. [0059]-[0060], [0092] (“FIG. 14 is a functional block diagram of an example neural network 1402 that can be used for the inference engine or other functions (e.g., engines) as described herein to produce a predictive model. The predictive model can identify a list of medical providers to recommend for a particular patient.”), [0095]-[0096]; Figs. 5-6, 14; Claims 3, 10; and displaying the ordered list of the one or more recommended care providers on a user interface (See id. at least at Paras. [0054], [0073] (“[T]he patient provider matching system 130 causes display of the ranked set of search results on the graphical user interface of the client device.”), [0084], [0099]; Fig. 1).
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 Mohtar to incorporate the teachings of Griffin and Krafft and provide a second machine learning model and displaying of a list of recommended providers. Griffin is directed to a decision engine based on disparate data sources. Krafft relates to a provider search and matching system. Incorporating the decision engine and sources of Griffin with the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 2, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1, and Krafft further teaches displaying an interactive map on the user interface indicating locations of the one or more recommended care providers (See Krafft at least at Paras. [0026], [0054] (“the UI Subsystem 308 further includes a visualization of a map indicating the locations of each of the providers provided by the ranking subsystem 304. The UI Subsystem 308 may provide functionality for a patient user to interact with the map.”), [0073]; Fig. 3).
Regarding claim 10, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1, and Mohtar further discloses prior to receiving electronic health data associated with a patient: receiving unstructured electronic health information associated with the patient; and converting, using a fast healthcare interoperability resources application programming interface (FHIR API), the unstructured electronic health information into electronic health data associated with the patient, the electronic health data comprising a FHIR data format (See Mohtar at least at Abstract; Paras. [0106]-[0112]; Figs. 3-5).
Regarding claims 11 and 20, claims 11 and 20 recite substantially the same limitations included in independent claim 1 and are rejected for the same reasoning applied above.
Regarding claim 19, claim 19 recites substantially the same limitations included in claim 10 and is rejected for the same reasoning applied above.
Claims 3-4, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mohtar, in view of Griffin, in view of Krafft, in view of U.S. 2023/0395215 A1 to Long et al., hereinafter “Long” and further in view of U.S. 2011/0301982 A1 to Green et al., hereinafter “Green.”
Regarding claim 3, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1. The references may not specifically describe but Long teaches determining a first set of filters for filtering the ordered list by one or more primary services, and a second set of filters for filtering the ordered list by one or more secondary services (See Long at least at Paras. [0040]-[0041], [0056] (“:The service of care type selection system 156 can present the medical providers received from the server in the user interface 400. The medical providers can be filtered and/or sorted to generate a subset of providers based on how closely their qualifications and available medical services match the patient information.”), [0086]-[0087] (“[U]se the response information to filter the matched providers.”); Figs. 1-3).
The references may not specifically describe but Green teaches displaying a first dropdown menu with the first set of filters and a second dropdown menu with the second set of filters on the user interface (See Green at least at Paras. [0263]-[0265] (“[T]opics may include one of the HCFA recommended element names (e.g., chronology, onset, description, intensity, exacerbation, etc.) or one of the user's own choosing. As shown in FIG. 17, the template builder component 1302 provides a drop-down menu for the topic name field. The user can select from the defined list or type in new topics that will be displayed within the History of Present Illness section during an encounter with a patient.” Several drop-down menus for patient data and condition and provider are available.), [0268]-[0273] (pull-down menus); Figs. 1-5, 17-18).
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 Mohtar, Griffin and Krafft to incorporate the teachings of Long and Green and provide filtering and a drop-down menu. Long is directed to a digital mesh system for medical providers. Green relates to an integrated medical software system with clinical decision support. Incorporating the digital mesh system as in Long with the clinical decision support of Green, the decision engine and sources of Griffin, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 4, Mohtar as modified by Griffin, Krafft, Long and Green discloses the limitations of claim 3 and Green further teaches receiving, at the user interface, a user selection comprising one or more primary services from the first dropdown menu, one or more secondary services from the second dropdown menu, or both; and updating the ordered list of the one or more recommended care providers based on the user selection (See Green at least at Paras. [0219], [0229]-[0232] (list automatically updated), [0263]-[0265] (“[T]opics may include one of the HCFA recommended element names (e.g., chronology, onset, description, intensity, exacerbation, etc.) or one of the user's own choosing. As shown in FIG. 17, the template builder component 1302 provides a drop-down menu for the topic name field. The user can select from the defined list or type in new topics that will be displayed within the History of Present Illness section during an encounter with a patient.” Several drop-down menus for patient data and condition and provider are available.), [0268]-[0273] (pull-down menus), [0296] (“differential diagnosis list 2200 will appear in the clinical document as choices to be selected by a clinician”), [0310] (updated list); Figs. 1-5, 12, 17-18).
Regarding claims 13 and 14, claims 13 and 14 recite substantially the same limitations included in claims 3 and 4, respectively, and are rejected for the same reasoning applied above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mohtar, in view of Griffin, in view of Krafft, in view of Long, in view of Green and further in view of U.S. 2015/0106123 A1 to Amarasingham et al., hereinafter “Amarasingham.”
Regarding claim 5, Mohtar as modified by Griffin, Krafft, Long and Green discloses the limitations of claim 3. The references may not specifically describe but Amarasingham teaches wherein the one or more primary services, the one or more secondary services, or both comprise literacy support services, housing support services, transportation support services, food security resources, financial support resources, or any combination thereof (See Amarasingham at least at Paras. [0023], [0046], [0088], [0095]).
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 Mohtar, Griffin, Krafft, Long and Green to incorporate the teachings of Amarasingham and provide readmission risk. Amarasingham is directed to continuity of care information system and techniques. Incorporating the continuity of care as in Amarasingham with the digital mesh system as in Long with the clinical decision support of Green, the decision engine and sources of Griffin, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 15, claim 15 recites substantially the same limitations included in claim 5 and is rejected for the same reasoning applied above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mohtar, in view of Griffin, in view of Krafft, and further in view of Amarasingham.
Regarding claim 6, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1. The references may not specifically describe but Amarasingham teaches determining, by a third machine learning model, a readmission risk for the patient based on the electronic health data, wherein the third machine learning model is trained to determine a readmission risk from training data comprising de-identified electronic health records paired with one or more readmission-causing events; and displaying an indication of the readmission risk for the patient on the user interface (See Amarasingham at least at Paras. [0023], [0025] (risk of re-admission), [0039] (readmission score), [0046] (comparison of aggregate risk of readmissions), [0050]-[0054] (displaying), [0088], [0095]; Claim 2 (user interface configurations)).
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 Mohtar, Griffin and Krafft to incorporate the teachings of Amarasingham and provide readmission risk. Amarasingham is directed to continuity of care information system and techniques. Incorporating the continuity of care as in Amarasingham with the decision engine and sources of Griffin, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 16, claim 16 recites substantially the same limitations included in claim 6 and is rejected for the same reasoning applied above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mohtar, in view of Griffin, in view of Krafft, and further in view of U.S. 2021/0313022 A1 to Chaballout, hereinafter “Chaballout”.
Regarding claim 7, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1. The references may not specifically describe but Chaballout teaches identifying, by a fourth machine learning model, one or more current procedural technology (CPT) codes in the electronic health data associated with the patient, wherein the fourth machine learning model is trained to identify the one or more CPT codes from training data comprising de-identified health records paired with corresponding CPT codes, and generating, by the fourth machine learning model, an insurance claim based on the one or more CPT codes (See Chaballout at least at Abstract; Paras. [0021]-[0026] (CPT and insurance), [0065], [0073], [0075]-[0076]; Fig. 7; See also Mohtar at least at Paras. [0010]-[0011]).
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 Mohtar, Griffin and Krafft to incorporate the teachings of Chaballout and provide CPT and insurance information. Chaballout is directed to an engine for augmented medical coding. Incorporating the augmented medical coding of Chaballout with the decision engine and sources of Griffin, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 17, claim 17 recites substantially the same limitations included in claim 7 and is rejected for the same reasoning applied above.
Claims 8, 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mohtar, in view of Griffin, in view of Krafft, and further in view of U.S. 2023/0282358 A1 to Shah et al., hereinafter “Shah.”
Regarding claim 8, Mohtar as modified by Griffin and Krafft discloses the limitations of claim 1. The references may not specifically describe but Shah teaches receiving, at the user interface, a user selection of a visual affordance for generating an integrated care plan; in response to receiving the user selection of the visual affordance, generating, using the second machine learning model, an integrated care plan for the patient, wherein the integrated care plan comprises the ordered list of the one or more recommended care providers and a natural language explanation for why each of the one or more recommended care providers were recommended; and displaying the integrated care plan on the user interface (See Shah at least at Abstract; Paras. [0043], [0090]-[0092] (affordability and other factors), [0098] (user selection), [0107] (machine learning modules), [0120], [0135], [0147], [0150]-[0155]; Figs. 1-7).
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 Mohtar, Griffin and Krafft to incorporate the teachings of Shah and provide user selections and affordability of programs or healthcare. Shah is directed to a predictive health plan optimization for users of healthcare services. Incorporating the health plan optimization of Shah with the decision engine and sources of Griffin, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 9, Mohtar as modified by Griffin, Krafft and Shah discloses the limitations of claim 8 and Griffin further teaches wherein the integrated care plan comprises, for each of the one or more recommended care providers, a care provider location, services offered, insurance providers accepted, cost information, or any combination thereof (See Griffin at least at Paras. [0017], [0063], [0086]).
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 Mohtar, Shah and Krafft to incorporate the teachings of Griffin and provide service provider location and services. Griffin is directed to a healthcare decision engine. Incorporating the decision engine and sources of Griffin with the health plan optimization of Shah, the matching system and provider list of Krafft and the machine learning applications for improving medical outcomes and compliance of Mohtar would thereby increase the applicability, utility, and efficacy of the claimed healthcare coordination platform and user interface.
Regarding claim 18, claim 18 recites substantially the same limitations included in claim 8 and is rejected for the same reasoning applied above.
Conclusion
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681