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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 23 December 2025 and 24 September 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s are being considered by the Examiner in this Office Action.
Response to Amendment
Claims 1-28 were previously pending in this application. The amendment filed 23 December 2025 has been entered and the following has occurred: Claims 1 & 25 have been amended. Claim 7 has been cancelled. Claim 29 has been added.
Claims 1-6 & 8-29 remain pending in the application
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 1-6 & 8-29 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.
The claims recite subject matter within a statutory category as a process (claims 25-28) and machine (claims 1-6, 8-24, & 29) which recite steps of:
initially acquire at least one patient data value for a new patient of the plurality of patients;
store the at least one patient data value as part of the longitudinal data of the new patient;
further acquire a plurality of further patient data values for the new patient through a plurality of data streams;
include the plurality of further patient data values for the new patient as part of the longitudinal data of the new patient, whereby the longitudinal data of the new patient includes a plurality of patient data values;
retain the longitudinal data of the new patient for a time duration that extends through a plurality of medical processes, wherein the medical processes have different time periods;
compare the longitudinal data of the new patient to a requirement of at least one of a first clinical trial or a second clinical trial; and
from the plurality of data streams, replenish the patient database with at least one patient data value of an additional patient for the plurality of patients, wherein the at least one patient data value of the additional patient is stored as longitudinal data of the additional patient; and
the patient database module including an activity component configured to perform a longitudinal data action on the longitudinal data of the new patient.
These steps of receiving patient data values for a new patient, storing the patient data value as longitudinal data of the new patient, performing various data storage/data manipulation on the longitudinal data including retaining, comparing, and/or storing the data in a patient database, and determining/performing an action in response to said storage/manipulation efforts, as drafted, under the broadest reasonable interpretation (BRI), includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) describes various methods of organizing human activity including fundamental economic principles or practices, commercial or legal interactions, and/or managing personal behavior or interactions between people. Under BRI, the steps identified above fall into activity including commercial or legal interactions and/or managing personal behavior or relationships or interactions between people. That is, under BRI, the steps include determining candidacy for a current patient for one or more clinical trials based on historical/longitudinal patient data, and based on said candidacy, performing some subsequent action for the patient, thereby managing the typical interactions performed when determining candidates for clinical trials and/or behavior of a patient when being assigned to a clinical trial. At the broadest level, the system is at least managing the storage and manipulation of human activity in the form of longitudinal patient, e.g. clinical trial, data and making determinations of an action to perform on the longitudinal data of the patient. As such, the claims recite an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-6, 8-24, & 26-29, reciting particular aspects of how methods of organizing human activity, such as determining candidates for clinical trials, performing various actions on the patient data, e.g. securing, tracking, storing, transmitting, etc., may be performed but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a patient database module; a patient database; and a non-transitory computer-readable storage medium, an activity component amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification [000120]/[000356] for a patient database module; [000120] for a patient database; [000120] for a non-transitory computer-readable storage medium; [000127] for an activity component, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of acquire at least one patient data value for a new patient of the plurality of patients, further acquire a plurality of further patient data values for the new patient through a plurality of data streams amounts to mere data gathering; recitation of store patient data value as part of longitudinal data of the new patient, include the plurality of further patient data values for the new patient as part of the longitudinal data of the new patient, retain the longitudinal data of the new patient for a time duration that extends through a plurality of medical processes, wherein the medical processes have different time periods, comparing the longitudinal data of the new patient to a requirement of at least one of a first clinical trial or a second clinical trial, replenish the patient database with at least one patient data value of an additional patient for the plurality of patients amounts to selecting a particular data source or type of data to be manipulated; recitation of perform a longitudinal data action on the longitudinal data of the new patient amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of the steps for determining candidacy for clinical trials, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-6, 8-24, & 26-29, additional limitations which amount to invoking computers as a tool to perform the abstract idea such as recitation of a patient database system; patient database module; one or more memories; a computer network; a graphical user interface; an application programming interface (API); a machine learning model; a patient engagement platform; a clinical research system; a laboratory system; an imaging system; a healthcare system electronic medical record (EMR); a wearable device; see Applicant’s Specification [00120] for a patient database system; [000120] for a patient database module; [000359] for one or more memories; [000162] for a computer network; [000305] for a graphical user interface; [000139] for an application programming interface (API); [000341] for a machine learning model; [00086] for a patient engagement platform; [000229] for a clinical research system; [000292] for a laboratory system; [000195] for an imaging system, [000195] for a healthcare system electronic medical record (EMR); [000245] for a wearable device, see MPEP 2106.05(f); claims 2, 6, 10, 14, 16-22, which recites additional limitations relating to data relating to one or more clinical trials, the patient data values include geographic location of the patient, transmitting another message to the new patient over the computer network, the longitudinal data including a biomarker, acquiring additional patient data values for the new patient and include additional patient data values in the plurality of patient data values of the longitudinal data of the new patient, the longitudinal data of the new patient includes a geographic location, acquiring patient data value from an EMR, the patient data value including a name or contact information of the new patient, wherein the medical record indicates that the new patient has a first condition, the longitudinal data includes patient medical record, a patient communication, patient laboratory data, a patient data assessment, a patient medical condition, a patient medication, or a patient biomarker, which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 3-5, 8-11, 14-15, 17, 23-24, 26, & 29, which recite limitations relating to establishing a new patient record corresponding to receipt of at least one patient data value, encrypting longitudinal data according to one or more government regulations, storing patient data values that are relevant to matching a patient to a plurality of clinical trials in a higher-speed memory, tracking which types of patient data values are more likely to be used for matching patients to clinical trials, storing the types of patient data values, storing plurality of patient data values, converting the non-standardized updated medical data into a standardized format, comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial, determine a match of the new patient to one of the clinical trials, recognizing a pattern in the biomarker training a machine learning model on training data including prior biomarkers and patterns recognized therein, matching the new patient to clinical trials, and matching geographical location to a clinical site, provide a selection of longitudinal data of the plurality of patients aggregated, anonymizing the selection of longitudinal, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 2, 8, 12-13, 18, & 27-28, which recites limitations relating to one or more clinical trials, utilizing one or more computer networks to provide remote access, such as via a healthcare provider computer system, an application programming interface providing bidirectional interface access for user access, wherein the remote access is provided via a clinical research system, various data streams that interface with an application programming interface (API), additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as acquire at least one patient data value for a new patient of the plurality of patients, further acquire a plurality of further patient data values for the new patient through a plurality of data streams, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); comparing the longitudinal data of the new patient to a requirement of at least one of a first clinical trial or a second clinical trial, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); replenishing the patient database with at least one patient data value of an additional patient for the plurality of patients, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); store patient data value as part of longitudinal data of the new patient, include the plurality of further patient data values for the new patient as part of the longitudinal data of the new patient, retain the longitudinal data of the new patient for a time duration that extends through a plurality of medical processes, wherein the medical processes have different time periods, replenish the patient database with at least one patient data value of an additional patient for the plurality of patients, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); acquiring patient data values, which under BRI, include extraction from physical patient documents, such as electronic medical records, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-6, 8-24 & 26-29, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2, 6, 10, 14, 16-22, which recites additional limitations relating to data relating to one or more clinical trials, the patient data values include geographic location of the patient, transmitting another message to the new patient over the computer network, the longitudinal data including a biomarker, acquiring additional patient data values for the new patient and include additional patient data values in the plurality of patient data values of the longitudinal data of the new patient, the longitudinal data of the new patient includes a geographic location, acquiring patient data value from an EMR, the patient data value including a name or contact information of the new patient, wherein the medical record indicates that the new patient has a first condition, the longitudinal data includes patient medical record, a patient communication, patient laboratory data, a patient data assessment, a patient medical condition, a patient medication, or a patient biomarker, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 3-5, 8-11, 14-15, 17, 23-24, & 26, which recite limitations relating to establishing a new patient record corresponding to receipt of at least one patient data value, encrypting longitudinal data according to one or more government regulations, storing patient data values that are relevant to matching a patient to a plurality of clinical trials in a higher-speed memory, tracking which types of patient data values are more likely to be used for matching patients to clinical trials, converting the non-standardized updated medical data into a standardized format, comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial, determine a match of the new patient to one of the clinical trials, recognizing a pattern in the biomarker training a machine learning model on training data including prior biomarkers and patterns recognized therein, matching the new patient to clinical trials, and matching geographical location to a clinical site, provide a selection of longitudinal data of the plurality of patients aggregated, anonymizing the selection of longitudinal, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 3, 5, 8-9, 10, & 26 storing the types of patient data values, storing plurality of patient data values, storing standardized updated medical data in the longitudinal data of the new patient in the patient database, such as in electronic medical records, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 3, 5, 8-9, 10, 26, & 29 storing the types of patient data values, storing plurality of patient data values, storing standardized updated medical data in the longitudinal data of the new patient in the patient database, an recitation of one or more memories for storing data , e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2, 6, 10, 14, 16-22, which recites additional limitations relating to capturing data from various source, which under BRI, includes scanning or extraction via various documents, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed inventions absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-13, & 16-29 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (U.S. Patent No. 11,664,099), hereinafter “Jain”, in view of Hitachi Ltd. (Japanese Patent Publication No. 2017/076159), hereinafter “Hitachi”.
Claim 1 –
Regarding Claim 1, Jain discloses a patient database system, comprising:
a patient database module and a patient database including a non-transitory computer-readable storage medium (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations),
the patient database configured to store longitudinal data for a plurality of patients in the non-transitory computer-readable storage medium (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study), and
the patient database module configured to:
initially acquire at least one patient data value for a new patient of the plurality of patients (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life);
store the at least one patient data value as part of the longitudinal data of the new patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
further acquire a plurality of further patient data values for the new patient through a plurality of data streams (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
include the plurality of further patient data values for the new patient as part of the longitudinal data of the new patient, whereby the longitudinal data of the new patient includes a plurality of patient data values (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 4, ll. 42-49 which discloses storing data for multiple individuals in one or more data storage devices, the data for each individual being stored in a different logical data storage area, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys);
retain the longitudinal data of the new patient for a time duration that extends through a plurality of medical processes, wherein the medical processes have different time periods (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study;);
compare the longitudinal data of the new patient to a requirement of at least one of a first clinical trial or a second clinical trial (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient); and
from the plurality of data streams, replenish the patient database with at least one patient data value of an additional patient for the plurality of patients (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. one or more patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study), wherein
the at least one patient data value of the additional patient is stored as longitudinal data of the additional patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study); and
the patient database module including an activity component configured to perform a longitudinal data action on the longitudinal data of the new patient (See Col. 3, ll. 50 – Col. 4, ll. 17 which discloses various activities that can be performed on data areas, including ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study, as described in Jain Col. 42, ll. 51-59; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added);
While Jain generally discloses generally performing a longitudinal data action on longitudinal data of a new patient, i.e. applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, Jain does not necessarily disclose the longitudinal data action specifically including the following aspects:
wherein the longitudinal data action includes:
tracking a frequency of use types of patient data values for longitudinal data of at least some of the plurality of patients and determining which of the types of patient data values are more likely to be used for matching patients to clinical trials based on the frequency of use, wherein
tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates; and
storing the types of patient data values that are more likely to be used in a higher-speed memory, wherein
the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used.
However, Hitachi disclose the following limitations:
wherein the longitudinal data action includes:
tracking a frequency of use types of patient data values for longitudinal data of at least some of the plurality of patients and determining which of the types of patient data values are more likely to be used for matching patients to clinical trials based on the frequency of use (See Hitachi Par [0026] (Boxes 3 & 4) which discloses knowing that data will be frequently referenced, i.e. access/retrieved, and thereby will be placed in high-speed storage; See Hitachi Par [0029] & [0033] (Boxes 5 & 6) which discloses for patients whose condition as a rare disease but who have been discharged from the hospital for one month and have not had access to their patient information in the past month should remain in the medium-speed storage because the information likely to be frequently referenced in information sharing with other doctors, disease analysis, and paper writing, i.e. tracking a frequency of use of patient data over time) wherein
tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates (See Hitachi Par [0026] (Boxes 3 & 4) which discloses knowing that data will be frequently referenced, i.e. access/retrieved, and thereby will be placed in high-speed storage; See Hitachi Par [0029] & [0033] (Boxes 5 & 6) which discloses for patients whose condition as a rare disease but who have been discharged from the hospital for one month and have not had access to their patient information in the past month should remain in the medium-speed storage because the information likely to be frequently referenced, i.e. accessed/retrieved, in information sharing with other doctors, disease analysis, and paper writing, i.e. tracking a frequency of use of patient data over time); and
storing the types of patient data values that are more likely to be used in a higher-speed memory (See Hitachi Par [0026] (Boxes 3 & 4) which discloses since it is known that admission reservation/data will be frequently referenced/recorded, i.e. accessed/retrieved, said data will be played in high-speed storage), wherein
the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used (See Hitachi Par [0004] (Box 1) which discloses the use of high speed storage with high input/output, i.e. retrieval, speed but high cost; See Hitachi Par [0014] (Box 2) which discloses the high-speed storage being semiconductor memory such as flash memory, i.e. short-term memory; See Hitachi Par [0026] (Boxes 3 & 4) which discloses since it is known that admission reservation/data will be frequently referenced/recorded, i.e. accessed/retrieved, said data will be played in high-speed storage; See Hitachi Par [0004] (Box 1) which discloses the use of low speed storage with low input speed, but cost can be saved due to lower; See Hitachi Par [0014] (Box 2) which discloses the medium-speed and low-speed storage use Hard Disk Drives, i.e. long-term memory).
The disclosure of Hitachi is directly applicable to the disclosure of Jain, because both disclosures share limitations and capabilities, such as being directed towards the management of patient data in medical technology systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Jain, which already discloses applying automated stored procedures for a user’s health data, to further specifically include tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates and storing the types of patient data values that are more likely to be used in a higher-speed memory, the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used, as disclosed by Hitachi, because the use of high-speed storage with high input/output speed entails higher costs than medium and/or lower-speed storage, so by utilizing low speed storage with low input speed where applicable, costs can be saved (See Hitachi Par [0004] (Box 1)).
Claim 2 –
Regarding Claim 2, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the plurality of medical processes include the first clinical trial and the second clinical trial, and the first clinical trial and the second clinical trial have the different time periods (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial).
Claim 3 –
Regarding Claim 3, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the patient database module is further configured to:
establish a new patient record of a plurality of patient records corresponding to the plurality of patients upon receipt of the at least one patient data value for the new patient, including storing the new patient record having the at least one patient data value as part of the longitudinal data of the new patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 4, ll. 36-49 which discloses storing data for multiple individuals in one or more data storage devices, the data for each individual being stored in a different logical data storage area, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys, such that the tools can de-duplicate or combine data records for the same individual).
Claim 4 –
Regarding Claim 4, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the longitudinal data action includes encrypting some of the longitudinal data in accordance with at least one government regulation (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 4, ll. 42-49 which discloses storing data for multiple individuals in one or more data storage devices, the data for each individual being stored in a different logical data storage area, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys; See Jain Col. 4, ll. 2-17 which discloses the computer system creating and maintaining a registry of trusted applications that meet a set of governance standards to maintain security of the various data stores, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys, as described in Jain Col. 4, ll. 42-49).
Claim 5 –
Regarding Claim 5, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the longitudinal data action includes storing patient data values that are relevant to matching a patient to a plurality of clinical trials in a higher-speed memory (See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient).
Claim 6 –
Regarding Claim 6, Jain and Hitachi disclose the patient database system of claim 5 in its entirety. Jain further discloses a system, wherein:
the patient data values that are relevant include a geographic location of the patient (See Jain Col. 26 , ll. 54 – Col. 27, ll. 22 which describes the various patient data values that can be captured in said data lakes, including positioning or location data (e.g. GPS data, gyroscope data, altimeter data, accelerometer data, linear acceleration data, received signal strength indicator from an emitter such as a WIFI access point WIFI access point, data from a BLUETOOTH sensor or sensor network, data from a cellular tower)).
Claim 8 –
Regarding Claim 8, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the patient database module stores the plurality of patient data values of the longitudinal data of the new patient in a standardized format in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories), and wherein
at least one of the plurality of patient data values of the longitudinal data of the new patient includes medical data (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. one or more patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
the plurality of data streams communicate with the patient database module over at least one computer network (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations); and
the longitudinal data action performed by the activity component includes:
providing remote access to healthcare providers over the at least one computer network for any one of healthcare providers to update the medical data to which the healthcare providers have access in real time through a graphical user interface (See Jain Col. 4, ll. 18-22 which discloses the use of an API that enables interoperability among a decentralized ecosystem of applications, i.e. remote access; See Jain Col. 10, ll. 53-59 which discloses the various modules configured to use an API to access health data stored in different de-identified logical data storage areas; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing), wherein
the one of the healthcare providers provides the updated medical data in a non-standardized format dependent on a hardware or software platform used by the one of the healthcare providers (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
converting the non-standardized updated medical data into the standardized format (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
storing the standardized updated medical data in the longitudinal data of the new patient in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
automatically generating a message containing the updated medical data whenever updated medical data has been stored (See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added; See Jain Col. 34, ll. 4-12 which discloses alert a user when new access is attempted or when new data is added; See Jain Col. 61, ll. 11-20 which specifically discloses sending a notification message as an issued action to inform a user of some information); and
transmitting the message to the new patient over the computer network in real time such that the new patient has immediate notification of the updated medical data (See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added; See Jain Col. 34, ll. 4-12 which discloses alert a user when new access is attempted or when new data is added; See Jain Col. 61, ll. 11-20 which specifically discloses sending a notification message as an issued action to inform a user of some information).
Claim 9 –
Regarding Claim 9, Jain and Hitachi disclose the patient database system of claim 8 in its entirety. Jain further discloses a system, wherein:
the patient database module is configured to automatically compare the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial in real time upon the activity component storing the standardized updated medical data as part of the longitudinal data of the new patient (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient).
Claim 10 –
Regarding Claim 10, Jain and Hitachi disclose the patient database system of claim 9 in its entirety. Jain further discloses a system, wherein:
the patient database module is further configured to:
determine a match of the new patient to at least one of the first clinical trial or the second clinical trial based on the automatic comparison of the longitudinal data (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient);
automatically generate another message containing information regarding the match whenever the match of the new patient to at least one of the first clinical trial or the second clinical trial is determined (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial); and
transmit the another message to the new patient over the computer network in real time such that the new patient has real time access to information regarding the match of the new patient to at least one of the first clinical trial or the second clinical trial (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial).
Claim 11 –
Regarding Claim 11, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the patient database module stores the plurality of patient data values of the longitudinal data of the new patient in a standardized format in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories), and wherein
at least one of the plurality of patient data values of the longitudinal data of the new patient includes medical data (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. one or more patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
the plurality of data streams communicate with the patient database module over at least one computer network (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations); and
the longitudinal data action performed by the activity component includes:
providing remote access to healthcare providers over the at least one computer network for any one of the healthcare providers to update the medical data to which the any one healthcare provider has access in real time through a graphical user interface (See Jain Col. 4, ll. 18-22 which discloses the use of an API that enables interoperability among a decentralized ecosystem of applications, i.e. remote access; See Jain Col. 10, ll. 53-59 which discloses the various modules configured to use an API to access health data stored in different de-identified logical data storage areas; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing), wherein
the any one healthcare provider provides the updated medical data in a non-standardized format dependent on a hardware and/or software platform used by the any one healthcare provider (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
converting the non-standardized updated medical data into the standardized format (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
storing the standardized updated medical data in the longitudinal data of the new patient in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories), wherein
the patient database module is configured to automatically compare the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial in real time whenever the activity component stores the standardized updated medical data in the longitudinal data of the new patient, and to determine therefrom a match of the new patient to at least one of the first clinical trial or the second clinical trial (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient);
automatically generating a message containing the match whenever the match is determined (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial); and
transmitting the message to the new patient over the computer network in real time such that the new patient has immediate notification of the match (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial).
Claim 12 –
Regarding Claim 12, Jain and Hitachi disclose the patient database system of claim 11 in its entirety. Jain further discloses a system, wherein:
the patient database system includes at least one application programming interface (API) providing a bidirectional interface for user access (See Jain Col. 4, ll. 18-22 which discloses the use of an API that enables interoperability among a decentralized ecosystem of applications; See Jain Col. 10, ll. 53-59 which discloses the various modules configured to use an API to access health data stored in different de-identified logical data storage areas; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing), and
the providing remote access to the healthcare providers is via the at least one API (See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing).
Claim 13 –
Regarding Claim 13, Jain and Hitachi disclose the patient database system of claim 11 in its entirety. Jain further discloses a system, wherein:
the remote access is provided to the healthcare providers via a clinical research system (See Jain Col. 13, ll. 32-37 which discloses the system is applicable to research efforts as a tool to assist researchers and facilitate scientific discovery, the system may be leveraged to benefit researchers in designing, monitoring, updating, and enhancing a health research study such as a clinical trial, a cohort study, or other research endeavor, constituting a “clinical research system” under BRI; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing).
Claim 16 –
Regarding Claim 16, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
acquire, during a journey of the new patient through participation in at least one of the first clinical trial or the second clinical trial, additional patient data values for the new patient and include the additional patient data values in the plurality of patient data values of the longitudinal data of the new patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 4, ll. 36-49 which discloses storing data for multiple individuals in one or more data storage devices, the data for each individual being stored in a different logical data storage area, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys, such that the tools can de-duplicate or combine data records for the same individual).
Claim 17 –
Regarding Claim 17, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the longitudinal data of the new patient includes a geographic location, and the patient database module is further configured to:
match the new patient to a plurality of clinical trials including at least one of the first clinical trial or the second clinical trial using the longitudinal data of the first patient (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient), wherein
the matching the new patient includes matching the geographical location of the first patient to a clinical site of the at least one of the first clinical trial or the second clinical trial (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 26 , ll. 54 – Col. 27, ll. 22 which describes the various patient data values that can be captured in said data lakes, including positioning or location data (e.g. GPS data, gyroscope data, altimeter data, accelerometer data, linear acceleration data, received signal strength indicator from an emitter such as a WIFI access point WIFI access point, data from a BLUETOOTH sensor or sensor network, data from a cellular tower)).
Claim 18 –
Regarding Claim 18, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the further acquiring at least one of the further patient data values via at least one application programming interface (API) from at least one of the data streams (See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation) including at least one of:
a patient engagement platform (Only one of the following data streams sources has to be disclosed according to the language “including at least one of” so this limitation does not have to be disclosed by Jain for the entirety of the claim to be effectively met by Jain);
a clinical research system (See Jain Col. 13, ll. 32-37 which discloses the system is applicable to research efforts as a tool to assist researchers and facilitate scientific discovery, the system may be leveraged to benefit researchers in designing, monitoring, updating, and enhancing a health research study such as a clinical trial, a cohort study, or other research endeavor, constituting a “clinical research system” under BRI;);
a laboratory result from a laboratory system (See Jain Col. 16, ll. 29-47 which discloses the use of biospecimen analysis results, i.e. lab results from lab systems), wherein
the at least one of the further patient data values includes a biomarker of the patient (Only one of the following data streams sources has to be disclosed according to the language “including at least one of” so this limitation does not have to be disclosed by Jain for the entirety of the claim to be effectively met by Jain);
an imaging result from an imaging system (Only one of the following data streams sources has to be disclosed according to the language “including at least one of” so this limitation does not have to be disclosed by Jain for the entirety of the claim to be effectively met by Jain);
a healthcare system electronic medical record (EMR) (See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR/EMR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing; See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses various data sources, including electronic health records); or
a wearable device (See Jain Col. 13, ll. 45-59 which discloses the use of software and digital devices, such as smartphones and wearable devices for data collection efforts, i.e. data streams; See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses various data sources, including one or more wearable devices).
Claim 19 –
Regarding Claim 19, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the initial acquiring includes receiving the at least one patient data value from an electronic medical record of a healthcare provider (See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR/EMR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing; See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses various data sources, including electronic health records).
Claim 20 –
Regarding Claim 20, Jain and Hitachi disclose the patient database system of claim 19 in its entirety. Jain further discloses a system, wherein:
the at least one patient data value is at least one of a name or a contact (See Jain Col. 30, ll. 6-22 which discloses, when authorized, storing one or more of user identification and/or an electronic communication address for the user (e.g., phone number, email address, etc.)).
Claim 21 –
Regarding Claim 21, Jain and Hitachi disclose the patient database system of claim 19 in its entirety. Jain further discloses a system, wherein:
the electronic medical record indicates that the new patient has a first condition (See Jain Col. 19, ll. 20-35 which discloses one or more medical conditions that define the various participants; See Jain Col. 23, ll. 4-35 which discloses receiving health information relating to a persons’ current or past medical history, i.e. electronic medical records, including whether the person has a disease such as diabetes, cancer, etc., i.e. a condition).
Claim 22 –
Regarding Claim 22, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the longitudinal data of the plurality of patients includes at least one of a patient medical record, a patient communication, patient laboratory data, a patient data assessment, a patient medical condition, a patient medication, or a patient biomarker (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses various data sources, including electronic health records; See Jain Col. 30, ll. 6-22 which discloses, when authorized, storing one or more of user identification and/or an electronic communication address for the user (e.g., phone number, email address, etc.); See Jain Col. 16, ll. 29-47 which discloses the use of biospecimen analysis results, i.e. lab results from lab systems; See Jain Col. 19, ll. 20-35 which discloses one or more medical conditions that define the various participants; See Jain Col. 23, ll. 4-35 which discloses receiving health information relating to a persons’ current or past medical history, i.e. electronic medical records, including whether the person has a disease such as diabetes, cancer, etc., i.e. a medical condition; See Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses receiving data related to substance use, including medication; A “biomarker” is understood to be any measurable indicator of some biological state or condition, such as those measured and evaluated using blood, urine, or soft tissues, therefore see Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses various data and values associated therewith that may reflect a wide variety of health conditions and behaviors relating to biological, physical, mental, emotional, environmental, social, and other inputs, such that data may be omics data (e.g., data relating to genomics, proteomics, pharmacogenomics, epigenomics), biologically sampled or derived data (e.g., data related to blood, urine, saliva, breath sample, skin scrape, hormone level, glucose level, a breathalyzer, DNA, perspiration), lab or diagnostic data (e.g., assay data, blood test results, tissue sample results, endocrine panel results), which can all constitute “biomarkers” under BRI;).
Claim 23 –
Regarding Claim 23, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
the patient database module is further configured to, in response to a query, provide a selection of longitudinal data of the plurality of patients aggregated according to a criteria specified in the query (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient).
Claim 24 –
Regarding Claim 24, Jain and Hitachi disclose the patient database system of claim 23 in its entirety. Jain further discloses a system, wherein:
the patient database module anonymizes the selection of longitudinal data (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient; See Jain Col. 30, ll. 6-10 which discloses the data areas/data lakes can be arranged to be anonymous or de-identified so that the data area does not indicate the user identity of the user that owns the data area or whose data is stored in the data area).
Claim 25 –
Regarding Claim 25, Jain discloses a method, comprising:
storing longitudinal data for a plurality of patients in a patient database (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
initially acquiring at least one patient data value for a new patient of a plurality of patients (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life);
storing the at least one patient data value as part of the longitudinal data of the new patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
further acquiring a plurality of further patient data values for the new patient through a plurality of data streams (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
including the plurality of further patient data values for the new patient as part of the longitudinal data of the new patient, whereby the longitudinal data of the new patient includes a plurality of patient data values (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 4, ll. 42-49 which discloses storing data for multiple individuals in one or more data storage devices, the data for each individual being stored in a different logical data storage area, wherein the data storage areas are respectively assigned unique identifiers and different data storage areas have contents encrypted using one or more encryption keys);
retaining the longitudinal data of the new patient for a time duration that extends through a plurality of medical processes, wherein the medical processes have different time periods (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
comparing the longitudinal data of the new patient to a requirement of at least one of a first clinical trial or a second clinical trial (See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient);
from the plurality of data streams, replenishing the patient database with at least one patient data value of an additional patient for the plurality of patients (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. one or more patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study), wherein
the at least one patient data value of the additional patient is stored as longitudinal data of the additional patient (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study); and
performing a longitudinal data action on the longitudinal data of the new patient (See Col. 3, ll. 50 – Col. 4, ll. 17 which discloses various activities that can be performed on data areas, including ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study, as described in Jain Col. 42, ll. 51-59; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added).
While Jain generally discloses generally performing a longitudinal data action on longitudinal data of a new patient, i.e. applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, Jain does not necessarily disclose the longitudinal data action specifically including the following aspects:
wherein the longitudinal data action includes:
tracking a frequency of use types of patient data values for longitudinal data of at least some of the plurality of patients and determining which of the types of patient data values are more likely to be used for matching patients to clinical trials based on the frequency of use, wherein
tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates; and
storing the types of patient data values that are more likely to be used in a higher-speed memory, wherein
the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used.
However, Hitachi disclose the following limitations:
wherein the longitudinal data action includes:
tracking a frequency of use types of patient data values for longitudinal data of at least some of the plurality of patients and determining which of the types of patient data values are more likely to be used for matching patients to clinical trials based on the frequency of use (See Hitachi Par [0026] (Boxes 3 & 4) which discloses knowing that data will be frequently referenced, i.e. access/retrieved, and thereby will be placed in high-speed storage; See Hitachi Par [0029] & [0033] (Boxes 5 & 6) which discloses for patients whose condition as a rare disease but who have been discharged from the hospital for one month and have not had access to their patient information in the past month should remain in the medium-speed storage because the information likely to be frequently referenced in information sharing with other doctors, disease analysis, and paper writing, i.e. tracking a frequency of use of patient data over time) wherein
tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates (See Hitachi Par [0026] (Boxes 3 & 4) which discloses knowing that data will be frequently referenced, i.e. access/retrieved, and thereby will be placed in high-speed storage; See Hitachi Par [0029] & [0033] (Boxes 5 & 6) which discloses for patients whose condition as a rare disease but who have been discharged from the hospital for one month and have not had access to their patient information in the past month should remain in the medium-speed storage because the information likely to be frequently referenced, i.e. accessed/retrieved, in information sharing with other doctors, disease analysis, and paper writing, i.e. tracking a frequency of use of patient data over time); and
storing the types of patient data values that are more likely to be used in a higher-speed memory (See Hitachi Par [0026] (Boxes 3 & 4) which discloses since it is known that admission reservation/data will be frequently referenced/recorded, i.e. accessed/retrieved, said data will be played in high-speed storage), wherein
the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used (See Hitachi Par [0004] (Box 1) which discloses the use of high speed storage with high input/output, i.e. retrieval, speed but high cost; See Hitachi Par [0014] (Box 2) which discloses the high-speed storage being semiconductor memory such as flash memory, i.e. short-term memory; See Hitachi Par [0026] (Boxes 3 & 4) which discloses since it is known that admission reservation/data will be frequently referenced/recorded, i.e. accessed/retrieved, said data will be played in high-speed storage; See Hitachi Par [0004] (Box 1) which discloses the use of low speed storage with low input speed, but cost can be saved due to lower; See Hitachi Par [0014] (Box 2) which discloses the medium-speed and low-speed storage use Hard Disk Drives, i.e. long-term memory).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Jain, which already discloses applying automated stored procedures for a user’s health data, to further specifically include tracking the frequency of use includes tracking at least one of frequency of access, frequency of retrieval, or frequency of updates and storing the types of patient data values that are more likely to be used in a higher-speed memory, the higher-speed memory has a higher reading speed and/or a higher writing speed than a lower-speed memory that stores at least some of the types of patient data values that are less likely to be used, to thereby provide faster retrieval of the types of patient data values that are more likely to be used, as disclosed by Hitachi, because the use of high-speed storage with high input/output speed entails higher costs than medium and/or lower-speed storage, so by utilizing low speed storage with low input speed where applicable, costs can be saved (See Hitachi Par [0004] (Box 1)).
Claim 26 –
Regarding Claim 26, Jain and Hitachi disclose the method of claim 25 in its entirety. Jain further discloses a method, wherein:
the plurality of patient data values of the longitudinal data of the new patient are stored in a standardized format in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories), and
at least one of the plurality of patient data values of the longitudinal data of the new patient includes medical data (See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. one or more patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study);
the plurality of data streams communicate with the patient database over at least one computer network (See Jain Col. 14, ll. 35-42 which discloses a server system including one or more computers, such that a data repository can include data storage devices including local data storage, remote data storage, cloud computing data storage, network-attached data storage, etc.; See Jain Claim 20 which discloses the use of one or more non-transitory computer-readable media storing instructions to cause the one or more computing devices to perform operations); and
the method further comprises:
the longitudinal data action comprising:
providing remote access to healthcare providers over the at least one computer network for any one of the healthcare providers to update the medical data to which the any one healthcare provider has access in real time through a graphical user interface (See Jain Col. 4, ll. 18-22 which discloses the use of an API that enables interoperability among a decentralized ecosystem of applications, i.e. remote access; See Jain Col. 10, ll. 53-59 which discloses the various modules configured to use an API to access health data stored in different de-identified logical data storage areas; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing), wherein
the any one healthcare provider provides the updated medical data in a non-standardized format dependent on a hardware and/or software platform used by the any one healthcare provider (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
converting the non-standardized updated medical data into the standardized format (See Jain Col. 14, ll. 35 – Col. 15, ll. 3 which discloses receiving data from various data sources, including data with multiple different formats, structured data, unstructured data, text, numerical values, images, documents, etc. constituting non-standardized format; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories); and
storing the standardized updated medical data in the longitudinal data of the new patient in the patient database (See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 2, ll. 3-9 which discloses the system storing data in standardized formats, for example according toa predetermined taxonomy, or including code to translate between data formats to a single, standardized format; See Jain Col. 38, ll. 17-25 which discloses data classifiers being provided as part of a standardized taxonomy of the various data categories, such that the system enhances interoperability among different applications and systems by standardizing said data types and data categories);
automatically comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial in real time whenever the standardized updated medical data is stored in the longitudinal data of the new patient and determining therefrom a match of the new patient to at least one of the first clinical trial or the second clinical trial (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 13, ll. 32 – Col. 14, ll. 12 which discloses applying the functionalities of the system for one or more clinical trials and/or phases for clinical trials, such that the phases, such as a phase 0 trial, a phase I trial, a phase II trial, and/or a phase III trial which all have different time periods; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 13, ll. 38-59 which discloses remote monitoring of various aspects of the health of individuals, i.e. patients, including physiological measures, behaviors, activities, mood or mental state, etc., such as during decentralized trials such as while visiting a designated trial site or in day-to-day life; See Jain Col. 42, ll. 51-59 which discloses data storage capturing ongoing, longitudinal health data that is generated over time, whether in daily life or as part of a research study; See Jain Col. 18, ll. 9-40 which discloses selectively linking/sharing data lakes or data areas for patient data values that are “relevant to matching a patient”, such that as described in Jain Col. 25, ll. 1-8, this enables researchers to search for candidates using the metadata associated with particular data lakes, such that, the system identifies which data lakes best fit with the needs of a research study (e.g., have the most complete or extensive sets of data collected or the most relevant ongoing data collection and data collection devices and are thereby most relevant to matching a patient), and invite the individuals corresponding to the identified data lakes to participate in a research study, and results in increasing the efficiency of storage by avoiding the duplication of data, i.e. higher-speed memory; See Jain Col. 25, ll. 9-40 which discloses the computer system specifically providing data lakes or data areas that meet certain characteristics, such that the computer system searches through the metadata to find data lakes or data areas that, according to the corresponding metadata meet the researcher’s criteria or which most closely meets the researcher’s criteria, i.e. most relevant to matching a patient);
automatically generating a message containing the match whenever the match is determined (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial); and
transmitting the message to the new patient over the computer network in real time such that the new patient has immediate notification of the match (See Jain Col. 22, ll. 44 – Col. 23, ll. 3 which discloses the metadata facilitating data sharing and the effective matching of individuals to the most relevant clinical trial research opportunities; See Jain Col. 31, ll. 1-15 which discloses one or more different clinical trials, such as in the case of a first clinical trial and a second clinical trial; See Jain Col. 2, ll. 30-40 which discloses applying automated stored procedures for a user’s health data, such that a user or application can set alerts to be provided when certain actions affecting a data storage area occur or fail to occur, including when a new record is added or is failed to be added, etc.; See Jain Col. 26, ll. 29-45 which specifically discloses that when a candidate is identified as a good fit for a research study, i.e. clinical trial, researchers can incentivize candidates to join a study, such that communications can be sent to the candidate regarding various incentives for participating in the matched study, constituting generating a message containing information regarding the match, specifically in the form of incentives for partaking in the matched clinical trial).
Claim 27 –
Regarding Claim 27, Jain and Hitachi disclose the method of claim 26 in its entirety. Jain further discloses a method, wherein:
the providing remote access to the healthcare providers is via at least one application programming interface (API) (See Jain Col. 4, ll. 18-22 which discloses the use of an API that enables interoperability among a decentralized ecosystem of applications, i.e. remote access; See Jain Col. 10, ll. 53-59 which discloses the various modules configured to use an API to access health data stored in different de-identified logical data storage areas; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing).
Claim 28 –
Regarding Claim 28, Jain and Hitachi disclose the method of claim 26 in its entirety. Jain further discloses a method, wherein:
the remote access is provided to the healthcare providers via a clinical research system (See Jain Col. 13, ll. 32-37 which discloses the system is applicable to research efforts as a tool to assist researchers and facilitate scientific discovery, the system may be leveraged to benefit researchers in designing, monitoring, updating, and enhancing a health research study such as a clinical trial, a cohort study, or other research endeavor, constituting a “clinical research system” under BRI; See Jain Col. 21, ll. 59 – Col. 22, ll. 43 which discloses the computer system is configured to interface with various other systems via API implementation, including EHR providers, insurance providers, healthcare providers ((e.g., individual hospitals, doctor's offices, and other facilities), such that the computer system can store information about the communication protocols and APIs of these different server systems to facilitate data transfer and data synchronization with these third-party systems, i.e. bidirectional communication/interfacing).
Claim 29 –
Regarding Claim 29, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Hitachi further discloses a system, wherein:
the higher-speed memory is a short term memory (See Hitachi Par [0004] (Box 1) which discloses the use of high speed storage with high input/output speed but high cost; See Hitachi Par [0014] (Box 2) which discloses the high-speed storage being semiconductor memory such as flash memory, i.e. short-term memory) and the lower-speed memory is a long term memory (See Hitachi Par [0004] (Box 1) which discloses the use of low speed storage with low input speed, but cost can be saved due to lower; See Hitachi Par [0014] (Box 2) which discloses the medium-speed and low-speed storage use Hard Disk Drives, i.e. long-term memory).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Jain, which already discloses applying automated stored procedures for a user’s health data, to further specifically include utilizing a higher-speed memory that comprises short-term memory and a lower-speed memory that comprises long-term memory, as disclosed by Hitachi, because the use of high-speed storage with high input/output speed entails higher costs than medium and/or lower-speed storage, so by utilizing low speed storage with low input speed where applicable, costs can be saved (See Hitachi Par [0004] & [0014] (Boxes 1 & 2)).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jain, in view of Hitachi, further in view of Longmire et al. (U.S. Patent Publication No. 2021/0375459), hereinafter “Longmire”.
Claim 14 –
Regarding Claim 14, Jain and Hitachi disclose the patient database system of claim 1 in its entirety. Jain further discloses a system, wherein:
at least one of the plurality of further patient data values included in the longitudinal data of the new patient is received from a laboratory via an application programming interface (API) of the patient database module, and the at least one of the plurality of further patient data values includes a biomarker (A “biomarker” is understood to be any measurable indicator of some biological state or condition, such as those measured and evaluated using blood, urine, or soft tissues, therefore see Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses various data and values associated therewith that may reflect a wide variety of health conditions and behaviors relating to biological, physical, mental, emotional, environmental, social, and other inputs, such that data may be omics data (e.g., data relating to genomics, proteomics, pharmacogenomics, epigenomics), biologically sampled or derived data (e.g., data related to blood, urine, saliva, breath sample, skin scrape, hormone level, glucose level, a breathalyzer, DNA, perspiration), lab or diagnostic data (e.g., assay data, blood test results, tissue sample results, endocrine panel results), which can all constitute “biomarkers” under BRI);
the longitudinal data action includes using a machine learning model to recognize a pattern in the biomarker (A “biomarker” is understood to be any measurable indicator of some biological state or condition, such as those measured and evaluated using blood, urine, or soft tissues, therefore see Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses various data and values associated therewith that may reflect a wide variety of health conditions and behaviors relating to biological, physical, mental, emotional, environmental, social, and other inputs, such that data may be omics data (e.g., data relating to genomics, proteomics, pharmacogenomics, epigenomics), biologically sampled or derived data (e.g., data related to blood, urine, saliva, breath sample, skin scrape, hormone level, glucose level, a breathalyzer, DNA, perspiration), lab or diagnostic data (e.g., assay data, blood test results, tissue sample results, endocrine panel results), which can all constitute “biomarkers” under BRI; See Jain Col. 11, ll. 32-49 which discloses operating on the data set, i.e. the above “biomarkers”, as a whole, including running machine learning tasks (such as model training, generating predictions or inferences, and testing models)).
While Jain and Hitachi disclose operating on data sets such as the “biomarkers” identified above including running machine learning tasks (such as model training, generating predictions or inferences, and testing models) on said biomarker data sets, Jain and Hitachi do not explicitly disclose utilizing the machine learning models trained on the biomarker data set to compare the recognized pattern in the biomarker for the purpose of matching the patient to at least one of the first clinical trial or the second clinical trial as given by the following limitation:
comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial includes comparing the recognized pattern in the biomarker to match the patient to at least one of the first clinical trial or the second clinical trial.
However, Longmire discloses comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial includes comparing the recognized pattern in the biomarker to match the patient to at least one of the first clinical trial or the second clinical trial (See Longmire Par [0009] which discloses predicting outcomes, sch as of clinical trials, as described in Longmire Par [0010] & [0043], by analyzing using intelligent algorithmic capabilities that enable automated treatment recommendations based on patient digital biomarker and other data; See Longmire Par [0043] which specifically discloses the system providing the ability to recruit and retain population-based samples and test digital biomarkers, in which genotype and phenotype can help identify suitable therapies and predict prognosis for participants for determining clinical trial candidacy). The disclosure of Longmire is directly applicable to the combined disclosure of Jain and Hitachi, because the disclosures share limitations and capabilities, such as being directed towards determining candidates/patients for clinical trials by matching certain criteria of said candidates/patients with required criteria for participation in the clinical trial.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Jain and Hitachi, which already discloses running machine learning tasks (such as model training, generating predictions or inferences, and testing models) on longitudinal biomarker data sets, to further include comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial includes comparing the recognized pattern in the biomarker to match the patient to at least one of the first clinical trial or the second clinical , as disclosed by Longmire, because this allows for early notice in clinical trials of potential adverse events or reduction in quality of life based on said predictive analysis of digital biomarkers performed by machine learning efforts, as well as, identifying suitable therapies and predicting prognosis for participants for determining clinical trial candidacy (See Longmire Par [0010] & [0043]).
Claim 15 –
Regarding Claim 15, Jain, Hitachi, and Longmire disclose the patient database system of claim 14 in its entirety. Jain and Longmire further disclose a system, wherein:
the machine learning model is trained on a training data set including prior biomarkers and patterns recognized therein (A “biomarker” is understood to be any measurable indicator of some biological state or condition, such as those measured and evaluated using blood, urine, or soft tissues, therefore see Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses various data and values associated therewith that may reflect a wide variety of health conditions and behaviors relating to biological, physical, mental, emotional, environmental, social, and other inputs, such that data may be omics data (e.g., data relating to genomics, proteomics, pharmacogenomics, epigenomics), biologically sampled or derived data (e.g., data related to blood, urine, saliva, breath sample, skin scrape, hormone level, glucose level, a breathalyzer, DNA, perspiration), lab or diagnostic data (e.g., assay data, blood test results, tissue sample results, endocrine panel results), which can all constitute “biomarkers” under BRI; See Jain Col. 11, ll. 32-49 which discloses operating on the data set, i.e. the above “biomarkers”, as a whole, including running machine learning tasks (such as model training, generating predictions or inferences, i.e. recognizing patterns, and testing models); See Longmire Par [0009] which discloses predicting outcomes, sch as of clinical trials, as described in Longmire Par [0010] & [0043], by analyzing using intelligent algorithmic capabilities that enable automated treatment recommendations based on patient digital biomarker and other data; See Longmire Par [0043] which specifically discloses the system providing the ability to recruit and retain population-based samples and test digital biomarkers, in which genotype and phenotype can help identify suitable therapies and predict prognosis for participants for determining clinical trial candidacy), and
the activity component provides the biomarker as an input to the machine learning model (A “biomarker” is understood to be any measurable indicator of some biological state or condition, such as those measured and evaluated using blood, urine, or soft tissues, therefore see Jain Col. 26, ll. 54 – Col. 27, ll. 22 which discloses various data and values associated therewith that may reflect a wide variety of health conditions and behaviors relating to biological, physical, mental, emotional, environmental, social, and other inputs, such that data may be omics data (e.g., data relating to genomics, proteomics, pharmacogenomics, epigenomics), biologically sampled or derived data (e.g., data related to blood, urine, saliva, breath sample, skin scrape, hormone level, glucose level, a breathalyzer, DNA, perspiration), lab or diagnostic data (e.g., assay data, blood test results, tissue sample results, endocrine panel results), which can all constitute “biomarkers” under BRI; See Jain Col. 11, ll. 32-49 which discloses operating on the data set, i.e. the above “biomarkers”, as a whole, including running machine learning tasks (such as model training, generating predictions or inferences, and testing models)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Jain and Hitachi, which already discloses running machine learning tasks (such as model training, generating predictions or inferences, and testing models) on longitudinal biomarker data sets, to further include comparing the longitudinal data of the new patient to a requirement of at least one of the first clinical trial or the second clinical trial includes comparing the recognized pattern in the biomarker to match the patient to at least one of the first clinical trial or the second clinical , as disclosed by Longmire, because this allows for early notice in clinical trials of potential adverse events or reduction in quality of life based on said predictive analysis of digital biomarkers performed by machine learning efforts, as well as, identifying suitable therapies and predicting prognosis for participants for determining clinical trial candidacy (See Longmire Par [0010] & [0043]).
Response to Arguments
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 101 rejections of claims 1-28, Applicant argues on p. 11-15 of Arguments/Remarks that the additional elements integrate the purported judicial exception of organizing human activity at least due to the similarities found between the additional elements of the instant application, such as at claim 8, and the additional elements found in Example 42 of the 2019 Subject Matter Eligibility Examples, at least by similarities of converting non-standardized data formats into standardized data formats. Applicant further argues in view of claims 9-10, stating that determining a match based on automatic comparison and generating and transmitting another message to the patient in real time regarding the match provide significant and valuable technical improvements to the claimed technology. Examiner respectfully disagrees with Applicant’s arguments. The Specification and disclosure of Example 42 substantially described shortcomings in prior art systems regarding records often being stored locally on a computer in a non-standard format selected by whichever hardware or software platform is in use in the medical provider’s local office, and difficulties associated therewith. Following this, Example 42 established a clear nexus between said described shortcomings found in the disclosure of Example 42 and the improvements thereof in the claims of Example 42. This is not substantially similar to the instant application. That is, the instant application does not describe technological problems/shortcomings of prior art systems regarding standardization of formats, but rather seems to describe said aspects as necessary or well-understood, routine, conventional aspects of healthcare data and management thereof. For instance, Par [00163]-[00164] of Applicant’s Specification describe well-known API architectures and/or processing techniques for processing data with non-standardized or native formats to a provider system. These efforts are also described throughout the prior art as typically necessary actions when it comes to healthcare data management. Therefore, without specifically describing shortcomings in prior art systems regarding lack of standardized formats, the inventive concept of the instant application does not seem to be directed towards solving said shortcomings, but rather performs said actions as necessary steps for data management efforts, i.e. to further the already-characterized abstraction. Therefore, Example 42 and the instant application significantly differ because the disclosure of Example 42 was substantially directed towards the inventive concept of solving shortcomings regarding non-standardized data formats at the time of Example 42’s invention, whereas the inventive concept of the instant application is not directed towards said shortcomings. Furthermore, regarding arguments of claims 9-10 providing significant and valuable technical improvements to the claimed technology via determining a match based on automatic comparison and generating and transmitting another message to the patient in real time, Examiner contends that each of these limitations represent insignificant, extra-solution activity, and/or well-understood, routine, and/or conventional activity found in prior art systems. That is, utilizing well-known learning architecture to automatically compare, generate, and transmit a message to a patient regarding analyses performed by said learning architecture does not represent a practical application, but instead furthers the abstraction at-hand, as discussed in the “Claim Rejections - 35 U.S.C. 101” portion of this Office Action. Therefore, claims 1-13 & 16-29 remain rejected under 35 U.S.C. 101
Regarding 35 U.S.C. 102 rejections of claims 1-28, Applicant argues on p. 15-16 of Arguments/Remarks that Jain does not disclose the newly amended limitations found in independent claims 1 & 25 regarding tracking a frequency of use of types of patient data values and storing said types of patient data values that are more likely to be used in higher-speed memory, and patient data values that are less likely to be used in lower-speed memory. Therefore, the 35 U.S.C. 102 rejections of claims 1-13 & 16-28 should be withdrawn Examiner agrees with Applicant’s arguments. Therefore, the 35 U.S.C. 102 rejections for claims 1-13 & 16-28 have been withdrawn. However, upon further consideration, a new ground of rejection has been made under 35 U.S.C. 103 over Jain in view of Hitachi. This new ground of rejection relies on Hitachi to teach the entirety of the newly amended limitations argued by Applicant. Therefore, claims 1-6, 8-13 & 16-28 remain rejected under 35 U.S.C. 103 over Jain in view of Hitachi. Furthermore, newly pending claim 29 is also rejected under 35 U.S.C. 103 over Jain in view of Hitachi.
Regarding 35 U.S.C. 103 rejections of claims 14-15, Applicant argues on p. 16-17 of Arguments/Remarks that because Jain does not disclose the above-mentioned limitations, and Longmire does not cure said deficiencies of Jain, that dependent claims are allowable over the prior art at least due to their respective dependencies from allowable independent claims 1 & 25. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, claims 1-13 & 16-28, including independent claims 1 & 25 remain rejected under a new ground of rejection under 35 U.S.C. 103 over Jain in view of Hitachi, therefore rendering moot the arguments regarding claims 1 & 25 being allowable over the prior art. As such, dependent claims remain rejected under 35 U.S.C. 103. Furthermore, Longmire does not need to cure the deficiencies of Jain, because Hitachi has cured the deficiencies of Jain. Therefore, claims 14-15 also remain rejected under 35 U.S.C. 103 over Jain in view of Hitachi, further in view of Longmire.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rost et al. (U.S. Patent No. 6,725,200) discloses a personal data archive system with portable personal storage devices allowing the owner to enter and store personal data, such that a small, high-speed memory can advantageously be used for frequently used data, and a large, slow memory can be used for large amounts of seldom-needed data;
Bonageri et al. (U.S. Patent Publication No. 2020/0410614) discloses a system that utilizes machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation.
Applicant's amendment necessitated the new ground of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNTER J RASNIC whose telephone number is (571)270-5801. The examiner can normally be reached M-F 8am-5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.R./Examiner, Art Unit 3684
/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
Prosecution Insights