Detailed Notice
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-18 are directed toward a method (i.e. a process), claim 19 is directed toward an early warning system (i.e. machine), and claim 20 is directed toward a non-transitory, tangible computer-readable medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 19, and 20 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A method for operating an early warning system based on continuous analyte data, the method comprising: receiving, by a processor implemented in the early warning system, continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient; determining, by the processor in communication with the continuous analyte sensor, a use-case deployment based on at least one of a user preference and a monitored condition of the patient; determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; providing, by the processor, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model; receiving, from the patient prediction model, a predicted patient outcome associated with the first patient; identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the first predicted patient outcome, to the predetermined recipient device”.
The limitations of receiving, continuous analyte data, wherein the continuous analyte data is associated with a patient; determining, a use-case deployment based on at least one of a user preference and a monitored condition of the patient; determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; providing, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs; receiving, a predicted patient outcome associated with the first patient; identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the first predicted patient outcome, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, determining, providing, identifying, and transmitting, which is properly interpreted as a “personal behavior”), and/or a mental process that a doctor, nurse, etc. would performing a prediction of patient outcomes, but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 19 recites: “An early warning system, comprising: a first continuous analyte sensor configured to continuously collect first continuous analyte data of a first patient and a second continuous analyte sensor configured to continuously collect second continuous analyte data of a second patient; one or more processors in communication with the first continuous analyte sensor and the second continuous analyte sensor; and a memory coupled to the one or more processors and storing a prediction model and instructions that when executed by the one or more processors cause the one or more processors to: receive continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient; determine a use-case deployment based on at least one of a user preference and a monitored condition of the first patient; determine a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; provide the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model; receive, from the patient prediction model, a predicted patient outcome associated with the first patient; identify a predetermined recipient device based on the predicted patient outcome; and transmit a notification, generated based on the first predicted patient outcome, to the predetermined recipient device”.
The limitations of receive continuous analyte data, wherein the continuous analyte data is associated with a patient; determine a use-case deployment based on at least one of a user preference and a monitored condition of the first patient; determine a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; provide the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs; receive, fa predicted patient outcome associated with the first patient; identify a predetermined recipient device based on the predicted patient outcome; and transmit a notification, generated based on the first predicted patient outcome, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, determining, providing, identifying, and transmitting, which is properly interpreted as a “personal behavior”), and/or a mental process that a doctor, nurse, etc. would performing a prediction of patient outcomes, but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 20 recites: “A non-transitory, tangible computer-readable medium having instructions stored thereon that, when executed by at least one computing device in an early warning system, cause a processor of the at least one computing device to perform operations comprising: receiving, by the processor, continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient; determining, by the processor in communication with the continuous analyte sensor, a use-case deployment based on at least one of a user preference and a monitored condition of the patient; determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; providing, by the processor, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model; receiving, from the patient prediction model, a predicted patient outcome associated with the first patient; identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the first predicted patient outcome, to the predetermined recipient device”.
The limitations of receiving, continuous analyte data, wherein the continuous analyte data is associated with a patient; determining, a use-case deployment based on at least one of a user preference and a monitored condition of the patient; determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data; providing, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs; receiving, a predicted patient outcome associated with the first patient; identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the first predicted patient outcome, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, determining, providing, identifying, and transmitting, which is properly interpreted as a “personal behavior”), and/or a mental process that a doctor, nurse, etc. would performing a prediction of patient outcomes, but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Dependent claims 2-18 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 19, and 20. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “processor”, “early warning system”, “continuous analyte sensor”, “patient prediction model”, “recipient device”, “memory”, “non-transitory, tangible computer-readable medium”, and “computing device”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0007], [0033], and [0037]-[0040], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “by a processor implemented in the early warning system”, “wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient”, “by the processor in communication with the continuous analyte sensor”, “by the processor”, “to a patient prediction model”, “from the patient prediction model”, “a predetermined recipient device”, “to the predetermined recipient device”, “a first continuous analyte sensor configured to continuously collect first continuous analyte data of a first patient and a second continuous analyte sensor configured to continuously collect second continuous analyte data of a second patient; one or more processors in communication with the first continuous analyte sensor and the second continuous analyte sensor; and a memory coupled to the one or more processors and storing a prediction model and instructions that when executed by the one or more processors cause the one or more processors to”, and “non-transitory, tangible computer-readable medium having instructions stored thereon that, when executed by at least one computing device in an early warning system, cause a processor of the at least one computing device to perform operations comprising” , which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving, by a processor implemented in the early warning system, continuous analyte data” and “receiving, from the patient prediction model, a predicted patient outcome associated with the first patient”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-18 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 19, and 20, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-18 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 19, and 20, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, by a processor implemented in the early warning system, continuous analyte data” and “receiving, from the patient prediction model, a predicted patient outcome associated with the first patient”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.”
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 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ray et al. (US 20230240589 A1), hereinafter Ray.
Regarding claims 1-20, Ray teaches:
1. A method for operating an early warning system based on continuous analyte data, the method comprising (Ray, FIG. 2):
receiving, by a processor implemented in the early warning system, continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient (Ray, FIG. 2, [0010], [0034]-[0040], and [0043]-[0052]);
determining, by the processor in communication with the continuous analyte sensor, a use-case deployment (Ray, [0071]: “As illustrated in FIG. 1 , training server system 140 deploys these trained model(s) to decision support engine 114 for use during runtime. For example, decision support engine 114 may obtain user profile 118 associated with a user and stored in user database 110, use information in user profile 118 as input into the trained model(s), and output a prediction indicative of the presence and/or severity of liver disease for the user (e.g., shown as output 144 in FIG. 1 ). Output 144 generated by decision support engine 114 may also provide one or more recommendations for treatment based on the prediction. Output 144 may be provided to the user (e.g., through application 106), to a user's caretaker (e.g., a parent, a relative, a guardian, a teacher, a nurse, etc.), to a user's physician, or any other individual that has an interest in the wellbeing of the user for purposes of improving the user's health, such as, in some cases by effectuating the recommended treatment”, [0095], [0195], [0201], and [0213]) based on at least one of a user preference and a monitored condition of the patient (Ray, [0064], [0127], [0213]: “For example, a model trained using historical patient records that is deployed for a particular user, may be further re-trained after deployment. For example, the model may be re-trained after the model is deployed for a specific patient to create a more personalized model for the patient. The more personalized model may be able to more accurately make liver disease-related predictions for the patient based on the patient's own data (as opposed to only historical patient record data), including the patient's own inputs 128 and metrics 130”);
determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data (Ray, [0082], [0095], [0108], and [0125]);
providing, by the processor, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model (Ray, [0034], [0044], [0053], [0078], and [0095]);
receiving, from the patient prediction model, a predicted patient outcome associated with the first patient (Ray, [0069], [0071], [0099], [0155], and [0190]-[0191]: “Different methods for generating a disease prediction may be used by decision support engine 114”);
identifying a predetermined recipient device based on the predicted patient outcome (Ray, [0043], [0064], [0074]-[0077], and [0199]: “In particular, decision support engine 114 makes liver disease treatment decisions or recommendations for the user. Treatment recommendations may include recommendations for lifestyle modification and/or one or more drugs to prescribe, titrate, or avoid use by the user. Decision support engine 114 may output such recommendations for treatment to the user (e.g., through application 106)”);
and transmitting a notification, generated based on the first predicted patient outcome, to the predetermined recipient device (Ray, [0074]-[0077], [0199]: “In particular, decision support engine 114 makes liver disease treatment decisions or recommendations for the user. Treatment recommendations may include recommendations for lifestyle modification and/or one or more drugs to prescribe, titrate, or avoid use by the user. Decision support engine 114 may output such recommendations for treatment to the user (e.g., through application 106)”, and [0200]).
2. The method of claim 1, wherein the notification comprises a recommendation for treating the first predicted patient outcome (Ray. [0036] and [0050]-[0051]: “As discussed in more detail herein, decision support engine 114 may provide decision support recommendations to the user via application 106. Decision support engine 114 provides decision support recommendations based on information included in user profile 118”).
3. The method of claim 1, wherein the predicted patient outcome is generated by the patient prediction model, wherein the method further comprises: inputting, to the patient prediction model, the continuous analyte data (Ray, [0042] and [0069]-[0071]); and outputting, by the patient prediction model, the predicted patient outcome (Ray, [0040]-[0043]).
4. The method of claim 1, wherein the patient prediction model is a machine learning model (Ray, [0042] and [0069]-[0071]).
5. The method of claim 1, wherein the predicted patient outcome is further based on patient medical information, and wherein the method further comprises: inputting, to the patient prediction model, patient medical information in combination with the continuous analyte data, wherein the patient medical information includes one or more of patient procedure history, patient medical history, or current vital signs associated with the first patient (Ray, [0040]-[0043] and [0069]-[0071]).
6. The method of claim 1, wherein the use-case deployment includes one of a hospital setting, a home setting, a sepsis condition, a heart failure condition, or a high-risk surgery condition (Ray, [0043], [0054], and [0056]).
7. The method of claim 1, wherein the continuous analyte sensor is a dual-analyte sensor and the continuous analyte data comprises lactate and any combination of glucose, creatinine, and ketone (Ray, [0012]-[0013], [0037], and [0046]).
8. The method of claim 1, wherein the notification includes an instruction for adjusting or maintaining a dosage of a substance to be administered to the patient, and the predetermined recipient device is configured to administer the substance to the patient based at the dosage specified in the instruction (Ray, [0074]-[0075], [0077], [0101], [0130], and [0200]).
9. The method of claim 1, wherein content of the notification is determined based on one or more of a proximity of a recipient of the predetermined recipient device to the patient, a time of day, and level of severity of the predicted patient outcome and identifying the predetermined recipient device is further based on one or more of the proximity of the recipient to the patient, the time of day, and the level of severity of the predicted patient outcome (Ray, [0240], [0285]-[0286], [0294], and [0344]).
10. The method of claim 1, further comprising:
receiving, by the processor, second continuous analyte data, wherein the second continuous analyte data is associated with a second patient, and wherein the second continuous analyte data is provided by a second continuous analyte sensor associated with the second patient (Ray, [0069], [0071], [0099], [0155], and [0190]-[0191]: “Different methods for generating a disease prediction may be used by decision support engine 114”);
determining, by the processor in communication with the second continuous analyte sensor, a second use-case deployment based on at least one a second user preference and a monitored condition of the second patient (Ray, [0064], [0127], [0213]: “For example, a model trained using historical patient records that is deployed for a particular user, may be further re-trained after deployment. For example, the model may be re-trained after the model is deployed for a specific patient to create a more personalized model for the patient. The more personalized model may be able to more accurately make liver disease-related predictions for the patient based on the patient's own data (as opposed to only historical patient record data), including the patient's own inputs 128 and metrics 130”);
providing, by the processor, the second use-case deployment and the second continuous analyte data as a second input to the patient prediction model, wherein the use-case deployment differs from the second use-case deployment (Ray, [0034], [0044], [0053], [0078], and [0095]);
receiving, from the patient prediction model, a second predicted patient outcome associated with the second patient (Ray, FIG. 2, [0010], [0034]-[0040], and [0043]-[0052]);
identifying a second predetermined recipient device based on the second predicted patient outcome (Ray, [0043], [0064], and [0074]-[0077]);
and transmitting a second notification, generated based on the second predicted patient outcome, to the second predetermined recipient device (Ray, [0074]-[0077] and [0373]).
11. The method of claim 1, wherein: when the determined absolute value of an analyte value in the continuous analyte data is below a first threshold and the determined trend is above a second threshold, the method comprises sending, by the processor, an alert to the predetermined recipient device (Ray, [0077], [0079], [0088], [0151], [0186], and [0370]); and when the determined absolute value of the analyte value is below the first threshold and the determined trend is below the second threshold, preventing transmission of the alert (Ray, [0077], [0079], [0088], [0151], [0155], [0186], and [0370]).
12. The method of claim 11, wherein:
when the determined absolute value of the analyte value is below the first threshold, the determined trend is above the second threshold, and the use-case deployment is a home setting, the method comprises sending a second alert to a device associated with another predetermined recipient device (Ray, [0077], [0079], [0088], [0151], [0186], and [0370]); when the determined absolute value of the analyte value is below the first threshold, the determined trend is above the second threshold but below a third threshold higher than the second threshold, and the use-case deployment is a hospital setting, preventing transmission of the second alert (Ray, [0077], [0079], [0088], [0151], [0155], [0186], and [0370]);
and when the determined absolute value of the analyte value is below the first threshold, the determined trend is above the third threshold, and the use-case deployment is the hospital setting, the method comprises sending an alert to a device associated with a different predetermined recipient device (Ray, [0077], [0079], [0088], [0151], [0155], [0186], and [0370]).
13. The method of claim 11, wherein the determined trend is a rate of change of the analyte value over a given time period (Ray, [0136], [0141], [0145], and [0152]).
14. The method of claim 1, further comprising determining content of the notification based on the predicted patient outcome and the use-case deployment (Ray. [0036] and [0050]-[0051]: “As discussed in more detail herein, decision support engine 114 may provide decision support recommendations to the user via application 106. Decision support engine 114 provides decision support recommendations based on information included in user profile 118”).
15. The method of claim 1, wherein a content of the notification is determined based on one or more of a proximity of a recipient associated with the predetermined recipient device to the patient, time of day, and a level of severity of the predicted patient outcome (Ray, [0240], [0285]-[0286], [0294], and [0344]).
16. The method of claim 15, wherein the content of the notification includes an instruction to administer an intervention to the patient based on the predicted patient outcome, and the predetermined recipient device is configured to automatically administer the intervention in response to the instruction (Ray, [0074]-[0075], [0077], [0090], [0101], [0130], and [0200]).
17. The method of claim 16, wherein the intervention is one or more of fluid resuscitation, administration of antibiotics, provision of breathing oxygen gas (Ray, [0056], [0109], and [0369]-[0370]).
18. The method of claim 16, wherein the notification includes an instruction for adjusting or maintaining a dosage of a substance to be administered, and the predetermined recipient device is configured to administer the substance to the patient based at the dosage specified in the instruction (Ray, [0074]-[0075], [0077], [0101], [0130], and [0200]).
19. An early warning system, comprising (Ray, FIG. 2):
a first continuous analyte sensor configured to continuously collect first continuous analyte data of a first patient and a second continuous analyte sensor configured to continuously collect second continuous analyte data of a second patient (Ray, FIG. 2 and FIG. 4);
one or more processors in communication with the first continuous analyte sensor and the second continuous analyte sensor (Ray, FIG. 2 and FIG. 4);
and a memory coupled to the one or more processors and storing a prediction model and instructions that when executed by the one or more processors cause the one or more processors to (Ray, FIG. 2 and FIG. 4):
receive continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient (Ray, FIG. 2, [0010], [0034]-[0040], and [0043]-[0052]);
determine a use-case deployment (Ray, [0071], [0095], [0195], [0201], and [0213]) based on at least one of a user preference and a monitored condition of the first patient (Ray, [0064], [0127], [0213]: “For example, a model trained using historical patient records that is deployed for a particular user, may be further re-trained after deployment. For example, the model may be re-trained after the model is deployed for a specific patient to create a more personalized model for the patient. The more personalized model may be able to more accurately make liver disease-related predictions for the patient based on the patient's own data (as opposed to only historical patient record data), including the patient's own inputs 128 and metrics 130”);
determine a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data (Ray, [0082], [0095], [0108], and [0125]);
provide the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model (Ray, [0034], [0044], [0053], [0078], and [0095]);
receive, from the patient prediction model, a predicted patient outcome associated with the first patient (Ray, [0069], [0071], [0099], [0155], and [0190]-[0191]: “Different methods for generating a disease prediction may be used by decision support engine 114”);
identify a predetermined recipient device based on the predicted patient outcome (Ray, [0043], [0064], and [0074]-[0077]);
and transmit a notification, generated based on the first predicted patient outcome, to the predetermined recipient device (Ray, [0074]-[0077] and [0373]).
20. A non-transitory, tangible computer-readable medium having instructions stored thereon that, when executed by at least one computing device in an early warning system, cause a processor of the at least one computing device to perform operations comprising (Ray, FIG. 2 and FIG. 4):
receiving, by the processor, continuous analyte data, wherein the continuous analyte data is associated with a patient, and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient (Ray, FIG. 2, [0010], [0034]-[0040], and [0043]-[0052]);
determining, by the processor in communication with the continuous analyte sensor, a use-case deployment (Ray, [0071]: “As illustrated in FIG. 1 , training server system 140 deploys these trained model(s) to decision support engine 114 for use during runtime. For example, decision support engine 114 may obtain user profile 118 associated with a user and stored in user database 110, use information in user profile 118 as input into the trained model(s), and output a prediction indicative of the presence and/or severity of liver disease for the user (e.g., shown as output 144 in FIG. 1 ). Output 144 generated by decision support engine 114 may also provide one or more recommendations for treatment based on the prediction. Output 144 may be provided to the user (e.g., through application 106), to a user's caretaker (e.g., a parent, a relative, a guardian, a teacher, a nurse, etc.), to a user's physician, or any other individual that has an interest in the wellbeing of the user for purposes of improving the user's health, such as, in some cases by effectuating the recommended treatment”, [0095], [0195], [0201], and [0213]) based on at least one of a user preference and a monitored condition of the patient (Ray, [0064], [0127], [0213]: “For example, a model trained using historical patient records that is deployed for a particular user, may be further re-trained after deployment. For example, the model may be re-trained after the model is deployed for a specific patient to create a more personalized model for the patient. The more personalized model may be able to more accurately make liver disease-related predictions for the patient based on the patient's own data (as opposed to only historical patient record data), including the patient's own inputs 128 and metrics 130”);
determining a trend in the continuous analyte data and an absolute value of an analyte value based on the continuous analyte data (Ray, [0082], [0095], [0108], and [0125]);
providing, by the processor, the use-case deployment, the determined trend, the absolute value of an analyte value, and the continuous analyte data as inputs to a patient prediction model (Ray, [0034], [0044], [0053], [0078], and [0095]);
receiving, from the patient prediction model, a predicted patient outcome associated with the first patient (Ray, [0069], [0071], [0099], [0155], and [0190]-[0191]: “Different methods for generating a disease prediction may be used by decision support engine 114”);
identifying a predetermined recipient device based on the predicted patient outcome (Ray, [0043], [0064], [0074]-[0077], and [0199]: “In particular, decision support engine 114 makes liver disease treatment decisions or recommendations for the user. Treatment recommendations may include recommendations for lifestyle modification and/or one or more drugs to prescribe, titrate, or avoid use by the user. Decision support engine 114 may output such recommendations for treatment to the user (e.g., through application 106)”);
and transmitting a notification, generated based on the first predicted patient outcome, to the predetermined recipient device (Ray, [0074]-[0077], [0199]: “In particular, decision support engine 114 makes liver disease treatment decisions or recommendations for the user. Treatment recommendations may include recommendations for lifestyle modification and/or one or more drugs to prescribe, titrate, or avoid use by the user. Decision support engine 114 may output such recommendations for treatment to the user (e.g., through application 106)”, and [0200]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 9 AM - 5 PM (EST).
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, Marc Jimenez can be reached at 571-272-4530. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.S.S./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681