Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-4, 6, 8-10, 12-17, and 19-24 are currently pending. Claim 11 is canceled and Claim 24 is newly added in the Claims filed on January 8, 2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 8, 2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6, 8-10, 12-17, and 19-24 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.
Step 1
Claims 1-4, 6, 8-10, 12-17, and 19-24 are within the four statutory categories. Claims 1-4, 6, 8-10, 12-15, and 21-24 are drawn to a method for organizing patients according to their probabilities of suffering an adverse event, which is within the four statutory categories (i.e. process). Claims 16-17 and 19 are drawn to a system for organizing patients according to their probabilities of suffering an adverse event, which is within the four statutory categories (i.e. machine). Claim 20 is drawn to a non-transitory medium for organizing patients according to their probabilities of suffering an adverse event, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A computer-implemented method comprising:
obtaining, by one or more server-side systems, a registration request for an individual having a condition, the registration request including a subset of data associated with the individual;
obtaining, by the one or more server-side systems, at least one device identification associated with the registration request, the at least one device identification including one or more of a first device identification associated with a first device of the individual or a second device identification associated with a second device;
determining, by the one or more server-side systems, a risk probability of an adverse event based on at least the subset of data;
assigning, by the one or more server-side systems, a position associated with the individual in an ordered list of individuals having respective conditions based on the risk probability;
causing, via the one or more server-side systems, an electronic application operating on at least one of the first device or the second device, based on the at least one device identifications, to output an indication of the position in the ordered list to the individual;
causing, via the one or more server-side systems, the electronic application to monitor a health status of the individual using one or more sensors of the first device or of the second device;
receiving an indication of a detection, via the electronic application and based on the monitoring with the one or more sensors, of an occurrence of one or more events from amongst a plurality of events predetermined to have an impact on the health status of the individual; and
in real-time with the detecting:
determining, by the one or more server-side systems, an updated risk probability of the adverse event based on the occurrence of the one or more events;
assigning, by the one or more server-side systems, an updated position associated with the individual in the ordered list of individuals based on the updated risk probability; and
causing, via the one or more server-side systems, the electronic application to output an indication of the updated position in the ordered list to the individual.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a mental process and/or a certain method of organizing human activity because they recite a process that could be practically performed in the human mind (i.e. observations, evaluations, judgments, and/or opinions – in this case, the steps of obtaining a registration request including a subset of data, obtaining device identification, determining a risk probability based on the subset of data, assigning a position in an ordered list of individuals based on the risk probability, detecting an occurrence of one or more events that have an impact on the health status of the individual, determining an updated risk probability based on the update information, and assigning an updated position in the ordered list recite observations and/or evaluations) or using a pen and paper, but for the recitation of generic computer components (i.e. one or more server-side systems, the sensors, the user device, and the first and second devices), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of obtaining a registration request including a subset of data, obtaining device identification, determining a risk probability based on the subset of data, assigning a position in an ordered list of individuals based on the risk probability, detecting an occurrence of one or more events that have an impact on the health status of the individual, determining an updated risk probability based on the update information, and assigning an updated position in the ordered list recite following rules or instructions to evaluate a patient’s status to determine a patient position on a list of patients for the purpose of, for example, organizing healthcare operations for treating a patient), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract ideas are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 16 and 20 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 16, and 20 is that Claim 1 recites a method, whereas Claim 16 recites a system, and Claim 20 recites a non-transitory computer readable medium.
Dependent Claims 2-4, 6, 8-10, 12-15, 17, 19, and 21-24 include other limitations, for example Claims 2-3 recite types of information, Claims 4 and 17 recite a user specifying the device for receiving the update requests, Claim 6 recites types of devices, Claims 8-9 recite determining and modifying a monitoring schedule based on the probability of the adverse event, Claims 10 and 19 recite removing or adding an individual to the ordered list, Claim 12 recites notifying a provider of the lowest individual on the ordered list, Claim 13 recites notifying a provider of the individual who is a next recipient of care, Claim 14 recites utilizing a trained machine learning model to determine the probability and updated probability, Claim 15 recites performing remote care and questioning, and performing treatment planning and scheduling, Claim 21 recites monitoring the individuals for one or more events and updating the position of each individual on the list based on the occurrence of the one or more events, Claim 22 recites capturing individual images and evaluating the images to determine the occurrence of one or more events, Claim 23 recites a plurality of types of events, and Claim 24 recites determining an other individual whose position should be updated and updating the other individual’s position, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-4, 6, 8-10, 12-15, 17, 19, and 21-24 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 16, and 20.
Prong 2 of Step 2A
Claims 1, 16, and 20 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the one or more processors, the user device, the first and second devices, the sensors, the step of causing the first or second device to monitor a health status of the individual using the sensors, and the fact that the detecting of the events is perform in real-time) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the one or more server-side systems, the user device, the first and second devices, and the sensors, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0028]-[0029] and [0113] of the as-filed Specification, and see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the data being data usable to determine a risk of an adverse event for the individual, which amounts to limiting the abstract idea to the field of healthcare, e.g. see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of monitoring a health status of the individual and detecting of the events being done in real-time, which amounts to mere data gathering, and/or the recitation of causing the monitoring of the individual using the sensors and the causing of the output of the updated position in the ordered list, which amounts to an insignificant application, e.g. see MPEP 2106.05(g).
Additionally, dependent Claims 2-4, 6, 8-10, 12-15, 17, 19, and 21-24 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the types of sensors/devices recited in dependent Claims 2-3, 6, and 22, and the machine learning and training steps recited in dependent Claims 14-15), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-3 and 23), adding insignificant extra-solution activity to the abstract idea (i.e. the outputting of the updated positions of the one or more other individuals recited in dependent Claim 24), and/or do not include any additional elements beyond those already recited in independent Claims 1, 16, and 20, and hence also do not integrate the aforementioned abstract idea into a practical application.
Claims 1-4, 6, 8-10, 12-17, and 19-24 do not include additional elements that integrate the judicial exceptions into a practical application.
Step 2B
Claims 1, 16, and 20 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the one or more processors, the user device, the first and second devices, the sensors, the step of causing the first or second device to monitor a health status of the individual using the sensors, and the fact that the detecting of the events is perform in real-time), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0028]-[0029] and [0113] of the as-filed Specification discloses that the additional elements (i.e. the one or more processors, the user device, the first and second devices, the sensors) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives the request for registration and subset of data between the devices over a network, for example the Internet, e.g. see [0034]-[0035] of the as-filed Specification;
Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life – similarly, the current invention performs basic calculations (i.e. determining an order for the individuals on the ordered list, and re-determining the order for the ordered list based on the updated data) and does not impose meaningful limits on the scope of the claims;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the additional elements recite causing the sensors and the first or second device to monitor a health status of an individual (i.e. obtain and store the health status data), and utilize (i.e. retrieve) the data obtained from the monitoring in order to detect one or more events in real-time, updates the probability of an adverse event and updates the patient position, and outputs the updated patient position;
Dependent Claims 2-4, 6, 8-10, 12-15, 17, 19, and 21-24 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the types of sensors/devices recited in dependent Claims 2-3, 6, and 22, and the machine learning and training steps recited in dependent Claims 14-15), generally link the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-3 and 23), storing and retrieving information in memory (i.e. the identifying of one or more other individuals on the ordered list and causing the output of the updated position of the one or more other individuals recited in dependent Claim 24), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1, 16, and 20, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-4, 6, 8-10, 12-17, and 19-24 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-4, 6, 8-10, 12-17, and 19-24 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-4, 6, 8-9, 12, 15-17, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Bharmi (US 2021/0020294) in view of Court (US 2017/0239412), further in view of Sadeghzadeh (US 2020/0246543).
Regarding Claim 1, Bharmi teaches the following: A computer-implemented method comprising:
obtaining, by one or more server-side systems, a registration request for an individual having a condition, the registration request including a subset of data associated with the individual (The system includes a plurality of devices, wherein the devices include an encoder and register to store encoded patient IDs, e.g. see Bharmi [0153], and wherein the devices may transmit a data upload request (i.e. a request for registration) including a patient ID and test results (i.e. a subset of data) from a Body Generated Analyte (BGA) device, an Implantable Medical Device (IMD), and/or a Behavior Related Medical (BRM) device to a data storage device including a server and a medical network, e.g. see Bharmi [0151]-[0152], [0185], and [0204]. Additionally, any operations performed by a processor may be implemented by a server, e.g. see Bharmi [0140].);
determining, by the one or more server-side systems, a risk probability of an adverse event based on at least the subset of data (The system determines an initial risk score for a patient based on the patient’s physiological data, for example the system may determine the probability of a patient suffering a heart failure (i.e. an adverse event) based on the patient’s physiological data (i.e. the subset of data), e.g. see Bharmi [0308] and [0318].);
assigning, by the one or more server-side systems, a position associated with the individual in an ordered list of individuals having respective conditions based on the risk probability (The system generates a risk score for a patient and determines a priority score based on the risk score, wherein the priority score ranks (i.e. assigns a position) the patients on a patient list, e.g. see Bharmi [0308]-[0310], [0314], and [0316]-[0318].);
causing, via the one or more server-side systems, the electronic application to monitor a health status of the individual using one or more sensors of the first device or of the second device (The system gathers data from a plurality of devices including implantable and external devices such as the BGA device, the IMD, and/or the BRM device, e.g. see Bharmi [0151]-[0152].);
receiving an indication of a detection, via the electronic application and based on the monitoring with the one or more sensors, of an occurrence of one or more events from amongst a plurality of events predetermined to have an impact on the health status of the individual (The system determines level and trends for data (i.e. “one or more events predetermined to have an impact on the health status of the individual”) gathered from the devices, for example kidney failure, nutrition deficiencies, malnutrition, and additional abnormalities including an abnormal increase in a volume of blood and/or abnormal behavior, e.g. see Bharmi [0228], [0259], and [0297]-[0300], Fig. 9A.);
in real-time with the detecting (The system re-evaluates the data and the patient risk score in response to the updated data in real-time, e.g. see Bharmi [0141], [0297], and [0310]-[0324], Figs. 9B-9C.):
determining, by the one or more server-side systems, an updated risk probability of the adverse event based on the occurrence of the one or more events (The system determines an initial risk score for a patient based on the patient’s physiological data, for example the system may determine the probability of a patient suffering a heart failure (i.e. an adverse event) based on the patient’s physiological data (i.e. the subset of data), e.g. see Bharmi [0308] and [0318], and further over time receives updates in the patient’s BGA and/or IMD data detected by the BGA, IMD, and/or BRM devices, e.g. see Bharmi [0152], [0189], [0195], and [0297]. Additionally, the system re-evaluates the data and the patient risk score in response to the updated data in real-time, e.g. see Bharmi [0141], [0297], and [0310]-[0324], Figs. 9B-9C.);
assigning, by the one or more server-side systems, an updated position associated with the individual in the ordered list of individuals based on the updated risk probability (The patient risk score is used to determine the position of the patient in the list, wherein the patient risk score and list are updated over time as data is updated, e.g. see Bharmi [0310]-[0324], Figs. 9B-9C.).
But Bharmi does not teach and Court teaches the following:
obtaining, by the one or more server-side systems, at least one device identification associated with the registration request, the at least one device identification including one or more of a first device identification associated with a first device of the individual or a second device identification associated with a second device (The system includes a blood treatment device and a medical accessory (i.e. a first or second device), wherein the system includes configuration status including an accessory ID for the medical accessory and data collected by the medical accessory (i.e. a subset of data), for example blood pressure and other patient parameters, and confirms the configuration data to perform various functions, e.g. see Court [0020]-[0021], [0126], and [0142]-[0145].);
wherein the causing of the first device or the second device to monitor a health status of the individual is based on the one or more obtained device identifications (The blood treatment device may use the configuration data to establish wireless communications with the medical accessory, e.g. see Court [0143], wherein the accessory may be used in conjunction with the blood treatment device to measure data (i.e. monitor a health status), for example blood pressure or oxygenation, and the medical device may deny the establishing of wireless communications if it is determined that the accessory is not of the required type or lacks the required configuration, e.g. see Court [0145] – that is, the monitoring of the patient is performed based on the configuration data including the accessory ID indicating that the accessory is suitable for operation with the blood treatment device.);
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Bharmi to incorporate verifying the device identification as taught by Court in order to prevent users from mistakenly establishing data communication between devices not intended for being linked, e.g. see Court [0139].
But the combination of Bharmi and Court does not teach and Sadeghzadeh teaches the following:
causing, via the one or more server-side systems, an electronic application operating on at least one of the first device or the second device, based on the at least one device identifications, to output an indication of the position in the ordered list to the individual (The system includes one or more electronic devices including display devices, e.g. see Sadeghzadeh [0029]-[0030], Fig. 1, wherein the display devices display a graphical user interface (GUI) displaying a listing of patients in accordance with one or more prioritization rules, and wherein the listing is updated in real-time in response to changes in the physiological condition to the patients, for example by moving a patient up or down the list based on the prioritization rules, e.g. see Sadeghzadeh [0027]-[0028], [0033], and [0040]. Furthermore, in order to obtain the physiological data, the electronic devices establish a pairing with medical devices by obtaining and storing network identification information for one another (i.e. device identifications), e.g. see Sadeghzadeh [0063].);
causing, via the one or more server-side systems, the electronic application to output an indication of the updated position in the ordered list to the individual (The patient listing is updated in real-time in response to changes in the patient physiological condition, for example by moving a patient up or down the list according to prioritization rules, e.g. see Sadeghzadeh [0027]-[0028], [0033], and [0040].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Bharmi and Court to incorporate outputting the patient list and the updated patient list as taught by Sadeghzadeh in order to improve awareness of symptomatic patients to facilitate improved outcomes and improve efficiency while minimizing the burdens of physicians, e.g. see Sadeghzaedeh [0004].
Regarding Claim 2, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, wherein the one or more sensors include an accelerometer configured to monitor movements by the individual (The test data used to determine the risk score and the updated risk score includes accelerometer signatures indicative of activity (i.e. movements by the individual), e.g. see Bharmi [0121].).
Regarding Claim 3, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, wherein the one or more sensors include a blood glucose monitoring device configured to monitor a blood glucose level of the individual (The test data used to determine the risk score and the updated risk score includes blood glucose data, e.g. see Bharmi [0106] and [0152].).
Regarding Claim 4, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, wherein the first device includes a communications device specified by the individual for receiving individualized update requests (The IMD, BGA, and BRM devices provide data over time (i.e. later data is interpreted as updated data), e.g. see Bharmi [0189] and [0195], wherein the data obtained from the devices may be obtained via manual input by the patient (i.e. specified by the individual), e.g. see Bharmi [0158], and [0165].).
Regarding Claim 6, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, wherein the first device includes a communications device and the second device includes a health monitoring device configured to monitor health parameters (The devices are configured to communicate data between each other and to/from a server on a medical network (i.e. any of the devices may be interpreted as a “communications device”), and include a BGA, an IMD, and/or a BRM device (i.e. health monitoring devices that monitor health parameters), e.g. see Bharmi [0152], [0185], and [0204].), the health parameters including at least one of movements, vitals, or blood glucose levels of the individual (The data collected from the devices includes patient accelerometer data, blood glucose data, e.g. see Bharmi [0106], [0121], and [0152].).
Regarding Claim 8, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, further comprising determining, by the one or more server-side systems, a monitoring schedule of the electronic application based on the probability of an adverse event (The system determines a risk category for the patient based on the risk score, wherein patient appointments (i.e. monitoring) may be scheduled based on the patient risk category, e.g. see Bharmi [0308], [0312], and [0317].).
Regarding Claim 9, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 8, and Bharmi further teaches the following:
The computer-implemented method of claim 8, further comprising modifying the monitoring schedule based on the updated probability of an adverse event (The risk category for the patient may be updated in accordance with the updated patient risk score, e.g. see Bharmi [0316]-[0317], wherein a patient appointment is scheduled based on the patient risk category, e.g. see Bharmi [0308], [0312], and [0317].).
Regarding Claim 12, the combination of Bharmi and Court teaches the limitations of Claim 1, and Bharmi further teaches the following:
The computer-implemented method of claim 1, further comprising transmitting a notification to a provider system associated with the condition based on the ordered list, wherein the notification identifies one of the individuals included in the ordered list as having a lowest position in the ordered list and a next recipient of care associated with the condition (The system visually illustrates the patient list on an interface (i.e. transmits a notification) of a clinician (i.e. a provider system), wherein the list displays the priority score and the position of the patient, and wherein the system displays the patient with the highest score as rank one (i.e. the lowest position in the ordered list) and may schedule a high-risk patient for the first available appointment within a given time (i.e. a next recipient of care), e.g. see Bharmi [0315]-[0317].).
Regarding Claim 15, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following: The computer-implemented method of claim 1, further comprising:
automatically initiating remote care and questioning based on the data and machine learning risk prediction data; or
automatically performing treatment planning and scheduling based on the data (The system schedules appointments based on the final patient rankings, e.g. see Bharmi [0324].).
Regarding Claims 16 and 20, the limitations of Claims 16 and 20 are substantially similar to those claimed in Claim 1, with the sole difference being that Claim 1 recites a method, whereas Claim 16 recites a system, and Claim 20 recites a non-transitory computer readable medium. Specifically pertaining to Claims 16 and 20, Examiner notes that Bharmi teaches a method, system, and computer readable medium, e.g. see Bharmi [0002] and [0368], and hence the grounds of rejection provided above for Claim 1 are similarly applied to Claims 16 and 20.
Regarding Claim 17, the limitations of Claim 17 are substantially similar to those claimed in Claim 4, with the sole difference being that Claim 4 recites a method, whereas Claim 17 recite a system. Specifically pertaining to Claim 17, Examiner notes that Bharmi teaches a method and system, e.g. see Bharmi [0002], and hence the grounds of rejection provided above for Claim 4 are similarly applied to Claim 17.
Regarding Claim 21, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following: The computer-implemented method of claim 1, further comprising:
monitoring the health status of each of the individuals having the condition via one or more further sensors of a respective device of each individual (The system includes various devices that sense various patient parameters, e.g. see Bharmi [0151]-[0152], [0185], and [0204]), such that a respective position on the ordered list of each individual is updated in response to an occurrence of one or more events for that individual based on the monitoring (The patient ranking on the list is determined based on the patient risk score, e.g. see Bharmi [0310]-[0324], Figs. 9B-9C, wherein the patient risk score is updated based on patient data that is continuously obtained, e.g. see Bharmi [0322].).
Regarding Claim 22, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following: The computer-implemented method of claim 1, wherein the monitoring via the one or more sensors includes:
operating a camera to capture one or more images of the individual (The system includes a camera that captures still images and/or video of the individual, e.g. see Bharmi [0132] and [0173]-[0175].); and
evaluating the one or more images of the individual to detect the occurrence of the one or more events (The captured still images and/or video may be analyzed to recognize various events such as eating/drinking, e.g. see Bharmi [0132] and [0173]-[0175].).
Regarding Claim 23, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Bharmi further teaches the following: The computer-implemented method of claim 1, wherein the plurality of events predetermined to have an impact on the health status of the individual (The system determines level and trends for data (i.e. “one or more events predetermined to have an impact on the health status of the individual”) gathered from the devices, e.g. see Bharmi [0297]-[0300], Fig. 9A.) include one or more of:
an application of a medication to the individual (The system may detect and/or determine when a patient has taken medication, e.g. see Bharmi [0132] and [0151].);
a deviation of one or more physiological parameters from a baseline health status defined by the subset of data (The system detects abnormal behaviors and departures of values from normal patterns, e.g. see Bharmi [0228] and [0259].); or
an abnormal movement pattern of the individual.
Regarding Claim 24, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, and Sadeghzadeh further teaches the following: The computer-implemented method of claim 1, further comprising:
identifying, via the one or more server-side systems, one or more other individuals on the ordered list with an updated position due to the assigning (The system constructs a list of patients (i.e. any of which may be interpreted as “one or more other individuals”), wherein the list is constructed accordance with one or more prioritization rules and dynamically updated in real-time in response to changes to the physiological conditions to one or more patients, such that a patient may be moved up or down on the list, e.g. see Sadeghzadeh [0027]-[0028].);
causing, via the one or more server-side systems, one or more electronic applications operating on one or more devices associated with the one or more other individuals to output a respective indication of the updated position of the one or more other individuals (The system includes one or more electronic devices including display devices, e.g. see Sadeghzadeh [0029]-[0030], Fig. 1, wherein the display devices display a graphical user interface (GUI) displaying the updated listing of patients, e.g. see Sadeghzadeh [0027]-[0028], [0033], and [0040].); and
in real-time with the detection (The patient list is dynamically updated in real-time in response to changes to the physiological condition to the one or more patients, e.g. see Sadeghzadeh [0028].):
identifying, via the one or more server-side systems, one or more further other individuals on the ordered list with a further updated position due to the updated position of the individual (The system constructs a list of patients (i.e. any of which may be interpreted as “one or more other individuals”), wherein the list is constructed accordance with one or more prioritization rules and dynamically updated in real-time in response to changes to the physiological conditions to one or more patients, such that a patient may be moved up or down on the list, e.g. see Sadeghzadeh [0027]-[0028].); and
causing, via the one or more server-side systems, one or more electronic applications operating on one or more devices associated with the one or more further other individuals to output respective indications of the further updated position (The system includes one or more electronic devices including display devices, e.g. see Sadeghzadeh [0029]-[0030], Fig. 1, wherein the display devices display a graphical user interface (GUI) displaying the updated listing of patients, e.g. see Sadeghzadeh [0027]-[0028], [0033], and [0040].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Bharmi and Court to incorporate outputting the patient list and the updated patient list as taught by Sadeghzadeh in order to improve awareness of symptomatic patients to facilitate improved outcomes and improve efficiency while minimizing the burdens of physicians, e.g. see Sadeghzaedeh [0004].
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bharmi, Court, and Sadeghzadeh in view of Amarasingham (US 2014/0074509).
Regarding Claim 10, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, but does not teach and Amarasingham teaches the following:
The computer-implemented method of claim 1, further comprising removing the individual from the ordered list or adding the individual to another list based on the monitoring (The system includes a plurality of real-time data streams and historical data from a plurality of sources for patient data, e.g. see Amarasingham [0017], wherein new or updated patient data (i.e. monitoring data) causes the system to automatically remove a patient from a disease list, e.g. see Amarasingham [0039] and [0057]-[0059].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skill in the art to modify the combination of Bharmi, Court, and Sadeghzadeh to incorporate the removal of the patient from the list based on updated patient data as taught by Amarasingham in order to provide a user with an accurate and realistic depiction of the patient’s health status in real-time, e.g. see Amarasingham [0006] and [0025].
Regarding Claim 19, the limitations of Claim 19 are substantially similar to those claimed in Claim 10, with the sole difference being that Claim 10 recites a method whereas Claim 19 recites a system. Specifically pertaining to Claim 19, Examiner notes that Bharmi teaches a method and system, e.g. see Bharmi [0002], and hence the grounds of rejection provided above for Claim 10 are similarly applied to Claim 19.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bharmi, Court, and Sadeghzadeh in view of Seymour (US 2012/0053963).
Regarding Claim 13, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, but does not teach and Seymour teaches the following:
The computer-implemented method of claim 1, further comprising transmitting a notification to one individual from the individuals based on a respective position in the ordered list, wherein the notification identifies the one individual as a next recipient of care (The system includes a patient contact list, wherein the system contacts (i.e. notifies) the patients on the contact list immediately prior to the patient appointment to request that patients confirm their availability for the next available appointment time (i.e. the patient is a next recipient of care), e.g. see Seymour [0037].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skill in the art to modify the combination of Bharmi, Court, and Sadeghzadeh to incorporate notifying the patient prior to the next available appointment time as taught by Seymour in order to confirm the patient’s availability, e.g. see Seymour [0037].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bharmi, Court, and Sadeghzadeh in view of Brown (US 2020/0350076).
Regarding Claim 14, the combination of Bharmi, Court, and Sadeghzadeh teaches the limitations of Claim 1, but does not teach and Brown teaches the following:
The computer-implemented method of claim 1, wherein the probability of an adverse event and the updated probability of an adverse event are determined using a trained machine learning model (The system utilizes artificial intelligence and/or machine learning to determine a likelihood of an adverse event, e.g. see Brown [0059], wherein the model may be trained in order to improve accuracy over time, e.g. see Brown [0071].), wherein the trained machine learning model is generated by:
receiving, as training data, a plurality of individual conditions and corresponding probabilities of an adverse event (The system includes previously reviewed responses indicative of an adverse event and established clinical measures, e.g. see Brown [0059].); and
training a machine learning model based on at least a portion of the plurality of individual conditions and corresponding probabilities of an adverse event to output a probability distribution over the plurality of individual conditions, each probability in the probability distribution indicating a likelihood an individual will experience an adverse event due to a corresponding individual condition (The system compares the previously reviewed responses indicative of an adverse event and the established clinical measures in order to determine the likelihood of an adverse event, e.g. see Brown [0059], wherein the model is trained using the verified data, e.g. see Brown [0071].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skill in the art to modify the combination of Bharmi, Court, and Sadeghzadeh to incorporate utilizing a trained machine learning model to determine the likelihood of an adverse event as taught by Brown in order to improve the accuracy of the determination, e.g. see Brown [0071].
Response to Arguments
Applicant’s arguments, see Remarks, filed January 8, 2026, with respect to the rejections of Claims 1-4, 6, 8-10, 12-17, and 19-24 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claimed invention is patent eligible because the claimed limitations cannot be performed mentally, e.g. see pg. 13 of Remarks – Examiner disagrees.
Initially Examiner notes that, as shown above, the step of causing the first and/or second devices to monitor the health status of the individual is not characterized as part of the abstract idea, but instead is considered an additional element and furthermore represents insignificant extra-solution activity because it recites mere data gathering used in order to execute the abstract ideas of the mental process and/or organizing human activities, and is claimed at such a high level so as to amount to an insignificant application of the abstract ideas. That is, as presently claimed, the first and/or second devices performing the actual monitoring operations merely gather and supply the data in any manner, wherein the data is then used to determine the occurrence of the events and determine the updated risk probability, which are interpreted as operations that are capable of being performed mentally and/or are operations that amount to following rules or instructions for a user to follow to evaluate a patient’s status to determine a patient position on a list of patients for the purpose of, for example, organizing healthcare operations for treating a patient.
Additionally, Examiner asserts that a user, for example a physician, is eminently capable of viewing gathered patient data, and determining the occurrence of an event. For example, [0029] of the as-filed Specification discloses that a sensor may capture ECG data and heart rate, and a doctor is eminently capable of identifying or at least suspecting the occurrence of a heart attack by viewing continuously gathered ECG data that is clearly abnormal.
Applicants further allege that the claimed invention is patent eligible because it recites technical improvements to integrate any abstract idea into a practical application, specifically in that it “[improves] allocation of computing resources between server(s) and device(s) for performing real-time monitoring and triage” because it enables the recited functions of the claimed invention across a plurality of elements, e.g. see pgs. 13-14 of Remarks – Examiner disagrees.
The claimed invention is not patent eligible merely because it recites certain discrete/different components performing different functions. Moreover, Applicants do not provide any evidence demonstrating that the functions performed by the servers and devices represent functions that are not well-understood, routine, or conventional functions for the servers and devices performing the functions. For example, Claim 1 recites the server-side systems obtaining data, analyzing the obtained data, causing the first or second device to output the results of the analysis, causing sensors to monitor additional data, and repeating the process to update the results of the analysis and update the output data. None of the aforementioned functions are considered functions that are not well-understood, routine, or conventional functions for the components performing them, as any generic server is eminently capable of receiving and analyzing data and outputting the results of the analysis, any generic computing device is capable of outputting the results of the analysis, and any generic biological sensor is capable of receiving a command and performing an operation in response to the command. That is, each of the recited devices merely performs functions that they are expected to be capable of performing, and hence even when considered as an ordered combination, the claimed invention does not integrate the abstract idea into a practical application and/or is not significantly more than an abstract idea.
Additionally, [0002] of the as-filed Specification discloses various problems in the art such as needing to maximize time and resources, despite processes being manual, and [0003] of the as-filed Specification discloses that the claimed invention “[relates] to problems associated with individuals that have been put in a queue and are waiting, often time indeterminately, for a service or resource from a provider.” That is, the present Specification discloses that the claimed invention addresses the problem of automating a manual process for tracking the providing of services to patients, which is a problem that has existed since long before the advent of any type of computer technology, and hence is not a technological problem. Moreover, even assuming, arguendo, that the claimed invention achieves the improvement of, for example, a more efficient workflow for handling patient appointments, this represents an improvement to the abstract idea of a mental process and/or a certain method of organizing human activities merely by virtue of automating the execution of the abstract idea on a computer, and an improvement to the abstract idea itself is not a technical improvement, e.g. see MPEP 2106.05(a)(II).
For the aforementioned reasons, Claims 1-4, 6, 8-10, 12-17, and 19-24 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed January 8, 2026, with respect to the rejections of Claims 1-4, 6, 8-10, 12-17, and 19-24 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicants allege that Bharmi does not teach the presently claimed language because it does not teach updating the risk score for the patient “in real-time with the detecting,” e.g. see pgs. 14-15 of Remarks – Examiner disagrees.
As Applicants note, Bharmi teaches that the patient score “may be updated daily,” e.g. see Bharmi [0316]. However, this merely teaches that the score “may be” updated daily, and not that it is “only” updated at that interval and/or it “must be” updated at that interval.
Additionally, Bharmi teaches that the patient’s physiological data is continuously collected, e.g. see Bharmi [0152] – that is, data is collected and updated in real-time. Additionally, Bharmi [0141] teaches that “cardiac activity signals are analyzed in real time,” and Bharmi [0297] reaches that “The method of [determining a risk score for a patient] may be initiated based on various criteria such as…in response to updates in a patient’s BGA and/or IMD data.” Hence, Bharmi teaches collecting patient physiological data continuously (in real-time), and in response to the collected data, triggering the predicting of the risk score for the patient. Furthermore, Bharmi [0322] teaches that the system determines a priority patient ranking based on an initial risk score, wherein the system utilizes a priority ranking algorithm comprising “an artificial intelligence, or machine learning type algorithm that continuously receives heart failure data,” and Bharmi [0324] teaches determining a final patient ranking based on the priority patient ranking and scheduling appointments based on the final patient rankings. That is, Bharmi teaches continuously receiving data that triggers the calculation of an initial and an updated patient risk score, and calculating a patient priority ranking based on the continuously received data, wherein the patient priority ranking is used to schedule appointments.
Hence, Bharmi is not deficient to teach determining the updated risk probability in real-time with the detecting of the events and/or the assigning of the updated position for the individual.
Moreover, as shown above, Bharmi is not cited to specifically teach the output/display of the patient position and/or the updated patient position, as Sadeghzadeh is now cited to teach these limitations, and hence any arguments pertaining to these features in relation to Bharmi are moot.
For the aforementioned reasons, Claims 1-4, 6, 8-17, and 19-23 are rejected under 35 U.S.C. 103.
Conclusion
Applicant's amendment necessitated the new ground(s) 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.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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