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 .
Election/Restrictions
Applicant’s election of claims 24-35 in the reply filed on 04/24/2026 is acknowledged. The election has been treated as an election without traverse (MPEP § 818.01(a)). Claims 1-23 and 36 have been withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention.
Status of Claims
This reply is in response to the application filed 28 February 2025.
Claims 24-35 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 08/26/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 304. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 24-35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of 18/822,170. Although the claims at issue are not identical, they are not patentably distinct from each other because recite substantially similar limitations.
This is a provisional nonstatutory double patenting rejection.
The table/chart below exhibits the similarity* between the independent claims where claims 24-25 of the current application are a broader variation of the claims of the reference application.
* Similarities highlighted in BOLD
App# 19/066,786
App# 18/822,170
Claim 24:
a continuous analyte sensor configured to continuously collect continuous analyte data of a patient; one or more processors in communication with the 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 the continuous analyte data; determine a use-case deployment based on at least one of a user preference and a monitored condition of the patient; determine a trend in the continuous analyte data and an analyte value based on the continuous analyte data; provide the use-case deployment, the determined trend, the 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 patient; identify a predetermined recipient device based on the predicted patient outcome; and transmit a notification, generated based on the predicted patient outcome, to the predetermined recipient device.
Claim 19:
a processor configured to be in communication with a continuous lactate monitoring system at a first location associated with the home setting and a plurality of recipient devices and a memory coupled to the processor and storing a patient prediction model and instructions that when executed by the processor cause the processor to: receive continuous lactate data from the continuous lactate monitoring system, provide (a) the continuous lactate data and (b) patient health information as inputs to a patient prediction model to generate a predicted heart failure decompensation event, wherein the patient health information comprises one or more of patient procedure history, patient medical history, or current vital sign information; receive, from the patient prediction model, the predicted heart failure decompensation event, wherein the predicted heart failure decompensation event is determined based on the inputs identify a recipient device of the plurality of recipient devices to receive a notification based on the predicted heart failure decompensation event, wherein the recipient device is located at a second location and transmit the notification based on the predicted heart failure decompensation event wherein content of the notification is specific to the recipient device.
Claim 25:
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 analyte value based on the continuous analyte data; providing, by the processor, the use-case deployment, the determined trend, the 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 patient; identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the predicted patient outcome, to the predetermined recipient device.
Claim 1:
receiving, by a processor implemented in the early warning system, continuous lactate data from a continuous lactate monitoring system associated with the patient, wherein the early warning system is configured to communicate with the continuous lactate monitoring system located at a first location associated with the home setting and with a plurality of recipient devices providing, by the processor, (a) the continuous lactate data and (b) patient health information associated with the patient as inputs to a patient prediction model to generate a predicted heart failure decompensation event, wherein the patient health information comprises one or more of patient procedure history, patient medical history, or current vital sign information; receiving, by the processor, the predicted heart failure decompensation event from the patient prediction model, wherein the predicted heart failure decompensation event is generated based on the inputs identifying, by the processor and based on the predicted heart failure decompensation event, a recipient device from the plurality of recipient devices to receive a notification, wherein the recipient device is located at a second location and transmitting, by the processor, the notification to the recipient device, wherein content of the notification is specific to the recipient device.
The subject matter of the present application amounts to claims which
are broader in scope. It has been held that a generic invention is “anticipated” by an invention within the scope of the generic claims. Dependent claims 26 is anticipated by claim 2 of 18/822,170 for reciting wherein the notification comprises a recommendation for treating the predicted patient outcome. Dependent claim 27 is anticipated by claim 3 of 18/822,170 for reciting outputting, by the patient prediction model, the predicted patient outcome. Dependent claim 28 is anticipated by claim 1 of 18/822,170 for reciting 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. Dependent claim 29 is anticipated by claim 8 of 18/822,170 for reciting wherein the notification includes an instruction for adjusting or maintaining a dosage of a substance. Dependent claim 30 is anticipated by claim 9 of 18/822,170 for reciting 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. Dependent claim 33 is anticipated by claim 13 of 18/822,170 for reciting wherein the determined trend is a rate of change of the analyte value over a given time period. The remaining dependent claims are anticipated by 18/822,170 for reciting substantially similar limitations such as administering an adjusted dosage, determining recommended actions based on a proximity, and determining the content of the notification.
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 24-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claim 24) and process (claim 25-35).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 24 recites steps of
a continuous analyte sensor configured to continuously collect continuous analyte data of a patient;
one or more processors in communication with the 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 the continuous analyte data;
determine a use-case deployment based on at least one of a user preference and a monitored condition of the patient;
determine a trend in the continuous analyte data and an analyte value based on the continuous analyte data;
provide the use-case deployment, the determined trend, the 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 patient;
identify a predetermined recipient device based on the predicted patient outcome; and
transmit a notification, generated based on the predicted patient outcome, to the predetermined recipient device.
Claim 25 recites similar limitations as claim 24 but for the recitation of generic computer components.
These steps for an early warning system for continuous health monitoring, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through performing determinations, including observations and evaluations, on processing information associated with patient data and data trends. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. The italicized portion containing the recitation of providing the use-case deployment, the determined trend, the analyte value, and the continuous analyte data as inputs to a patient prediction model has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance in the mind and mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 24-25, 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 continuous analyte sensor configured to continuously collect continuous analyte data of a patient; one or more processors in communication with the 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 amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receive the continuous analyte data; receive […] a predicted patient outcome associated with the patient; and, transmit a notification […] the predetermined recipient device amounts to mere data gathering and output since it does not add meaningful limitations to the receiving and transmitting actions performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity; and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) 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; and, add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as recitation of a continuous analyte sensor configured to continuously collect continuous analyte data of a patient; one or more processors in communication with the 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, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f);
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as receive the continuous analyte data; receive […] a predicted patient outcome associated with the patient; and, transmit a notification […] the predetermined recipient device, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
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 generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 26-35 reciting particular aspects for an early warning system for continuous health monitoring such as
[Claims 26] wherein the notification comprises a recommendation for treating the predicted patient outcome;
[Claims 27] 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; and outputting, by the patient prediction model, the predicted patient outcome;
[Claims 28] 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;
[Claims 29] 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;
[Claims 30] 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;
[Claims 31] when the 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; and when the analyte value is below the first threshold and the determined trend is below the second threshold, preventing transmission of the alert;
[Claims 32] when 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; when 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; and when 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;
[Claims 33] wherein the determined trend is a rate of change of the analyte value over a given time period;
[Claims 34] determining content of the notification based on the predicted patient outcome and the use-case deployment;
[Claims 35] 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;
these italicized portions includes performance of the limitations in the mind since they merely describe types of data and determinations that can be performed by humans. Additionally, providing an input and generating an output by the patient prediction model has been treated as mathematical calculations which falls under the Mathematical Concepts abstract idea in light of the 2024 USPTO AI Guidance.
Step 2A Prong 2
Dependent claims 29 and 31-32 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 29 (the predetermined recipient device); and, claim 31 (by the processor) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)) and, add insignificant extra-solution activity to the abstract idea claim 31 (sending […] an alert to the predetermined recipient device); and, claims 32 (the method comprises sending a second alert to a device associated with another predetermined recipient device; and, the method comprises sending an alert to a device associated with a different predetermined recipient device) amounts to mere data output since it does not add meaningful limitations to the sending actions performed, see MPEP 2106.05(g))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 29 and 31 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, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Dependent claims 31-32 recite additional subject matter which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving the reference image set amounts; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, this is a textbook USC 101 where the present invention is clearly not a patent-eligible invention. Additionally, it is evident that the present claims monopolize the fundamental concept of using off-the-shelf predictive model (which the applicant did not invent) to notify a caregiver based on patient data, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates. Using AI tools is generally not enough to transform an abstract idea into patent-eligible subject matter if the core of the invention is still a method of determination; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 24-29, 31, and 33-34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ray et al. (US20230240589A1).
Regarding claim 24, Ray discloses a continuous analyte sensor configured to continuously collect continuous analyte data of a patient ([0082] “In certain embodiments, a continuous analyte sensor 202 may comprise a sensor for detecting and/or measuring analyte(s).” [Claim 1] “a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements”)
one or more processors in communication with the continuous analyte sensor ([Claim 4] “one or more processors in data communication with the sensor electronics module” [Claim 1] “a sensor electronics module coupled to the 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 ([0214] “In the illustrated embodiment, processor 705 retrieves and executes programming instructions stored in memory 710”)
receive the continuous analyte data;
([0052] “For example, application 106 provides a set of inputs 128, including the analyte measurements received from continuous analyte monitoring system 104”)
determine a use-case deployment based on at least one of a user preference and a monitored condition of the patient ([0035] “Continuous measurements as proposed herein, provide a more accurate indication of liver metabolic function and liver health as compared to a single point in time reading. A single point in time reading may be influenced by a patient's activity, such as exercise or diet changes near or during the point in time. […] Measuring analytes (e.g., lactate) in a continuous readout as proposed herein may increase understanding of metabolic function of the liver to confirm good liver performance, or determine the presence and/or magnitude of liver metabolic dysfunction.”)
determine a trend in the continuous analyte data and an analyte value based on the continuous analyte data ([0034] “Such continuous monitoring of analytes is advantageous in diagnosing and staging a disease of a patient given the continuous measurements provide continuously up to date measurements as well as information on the trend and rate of analyte change over a continuous period.”)
provide the use-case deployment, the determined trend, the analyte value, and the continuous analyte data as inputs to a patient prediction model ([0040] “For example, certain aspects are directed to algorithms and/or machine-learning models designed to assess the presence and severity of liver disease in a patient.” [0198] “Analyte data, including lactate and glucose data, lactate and ketone data, lactate and potassium data, or lactate, glucose, potassium, and ketone data (e.g., from measurements by continuous analyte monitoring system 104), may be used as input into such machine learning models.” [0041] “The algorithms and/or machine-learning models may take into consideration […] personalized patient-specific data.” [0053] “Metrics 130, […] such as one or more of the user's physiological state, trends associated with the health or state of a user, etc. In certain embodiments, metrics 130 may then be used by decision support engine 114 as input for providing guidance to a user.” [0082] “In certain embodiments, a continuous analyte sensor 202 may comprise a sensor for detecting and/or measuring analyte(s). […] to provide estimated analyte value(s)”)
receive, from the patient prediction model, a predicted patient outcome associated with the patient ([0042] “By iteratively processing features associated with each data record corresponding to each historical patient, the models may be iteratively refined to generate accurate predictions of liver disease presence and severity in a patient.”)
identify a predetermined recipient device based on the predicted patient outcome; and transmit a notification, generated based on the predicted patient outcome, to the predetermined recipient device.
([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).”)
Regarding claim 25, Ray discloses receiving, by a processor implemented in the early warning system, continuous analyte data, wherein the continuous analyte data is associated with a patient ([0214] “In the illustrated embodiment, processor 705 retrieves and executes programming instructions” [0052] “For example, application 106 provides a set of inputs 128, including the analyte measurements received from continuous analyte monitoring system 104” [Claim 1] “a continuous analyte sensor configured generate analyte measurements associated with analyte levels of a patient”)
and wherein the continuous analyte data is provided by a continuous analyte sensor associated with the patient ([0082] “In certain embodiments, a continuous analyte sensor 202 may comprise a sensor for detecting and/or measuring analyte(s).” [Claim 1] “a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements”)
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 ([Claim 4] “one or more processors in data communication with the sensor electronics module” [0035] “Continuous measurements as proposed herein, provide a more accurate indication of liver metabolic function and liver health as compared to a single point in time reading. A single point in time reading may be influenced by a patient's activity, such as exercise or diet changes near or during the point in time. […] Measuring analytes (e.g., lactate) in a continuous readout as proposed herein may increase understanding of metabolic function of the liver to confirm good liver performance, or determine the presence and/or magnitude of liver metabolic dysfunction.”)
determining a trend in the continuous analyte data and an analyte value based on the continuous analyte data ([0034] “Such continuous monitoring of analytes is advantageous in diagnosing and staging a disease of a patient given the continuous measurements provide continuously up to date measurements as well as information on the trend and rate of analyte change over a continuous period.”)
providing, by the processor, the use-case deployment, the determined trend, the analyte value, and the continuous analyte data as inputs to a patient prediction model ([0204] “includes a processor 705” [0040] “For example, certain aspects are directed to algorithms and/or machine-learning models designed to assess the presence and severity of liver disease in a patient. The algorithms and/or machine-learning models may be used in combination with one or more continuous analyte sensors, including at least a continuous lactate sensor, to provide liver disease assessment and staging, for example, at a regular intervals (e.g., daily, weekly, etc.).” [0041] “The algorithms and/or machine-learning models may take into consideration […] personalized patient-specific data.” [0053] “Metrics 130, […] such as one or more of the user's physiological state, trends associated with the health or state of a user, etc. In certain embodiments, metrics 130 may then be used by decision support engine 114 as input for providing guidance to a user.” [0082] “In certain embodiments, a continuous analyte sensor 202 may comprise a sensor for detecting and/or measuring analyte(s). […] to provide estimated analyte value(s)”)
receiving, from the patient prediction model, a predicted patient outcome associated with the patient ([0042] “By iteratively processing features associated with each data record corresponding to each historical patient, the models may be iteratively refined to generate accurate predictions of liver disease presence and severity in a patient.”)
identifying a predetermined recipient device based on the predicted patient outcome; and transmitting a notification, generated based on the predicted patient outcome, to the predetermined recipient device ([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).”)
Regarding claim 26, Ray discloses wherein the notification comprises a recommendation for treating the predicted patient outcome ([0051] “As discussed in more detail herein, decision support engine 114 may provide decision support recommendations to the user via application 106.” [0200] “Accordingly, in certain embodiments, decision support engine 114, at block 416, may recommend the user stop taking the previously prescribed medication, and in some cases, recommend an alternative medication for consumption by the user.”)
Regarding claim 27, Ray discloses wherein the predicted patient outcome is generated by the patient prediction model ([0070] “By iteratively processing each data record corresponding to each historical patient, the model(s) may be iteratively refined to generate accurate predictions of liver disease risk, presence, progression, improvement, and severity in a patient.”)
wherein the method further comprises: inputting, to the patient prediction model, the continuous analyte data ([0043] “In addition, baseline lactate levels and changes in lactate levels provided by the continuous analyte monitoring system may be used as input into the machine learning models and/or algorithms”)
and outputting, by the patient prediction model, the predicted patient outcome ([0071] “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 )”)
Regarding claim 28, Ray discloses wherein the predicted patient outcome is further based on patient medical information ([0070] “By iteratively processing each data record corresponding to each historical patient, the model(s) may be iteratively refined to generate accurate predictions of liver disease risk, presence, progression, improvement, and severity in a patient.”)
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 ([0198] “Analyte data, including lactate and glucose data, lactate and ketone data, lactate and potassium data, or lactate, glucose, potassium, and ketone data (e.g., from measurements by continuous analyte monitoring system 104), may be used as input into such machine learning models.” [0042] “According to certain embodiments, prior to deployment, the machine learning models are trained with training data, e.g., including population data. As described in more detail herein, the population data may be provided in a form of a dataset including data records of historical patients with varying stages of liver disease.”)
Regarding claim 29, Ray discloses wherein the notification includes an instruction for adjusting or maintaining a dosage of a substance to be administered to the patient ([0077] “In certain embodiments, the model may be trained by training server system 140 based on historical glucose and lactate to provide medication type and dosage recommendations. For example, the model may be trained to provide recommendations for medication type and dosage.”)
and the predetermined recipient device is configured to administer the substance to the patient based at the dosage specified in the instruction ([0090] “For example, medical device 208 may be an insulin pump for administering insulin to a user.”)
Regarding claim 31, Ray discloses when the 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 ([0079] “In certain embodiments where rule-based models are used for providing decision support, historical glucose and/or lactate measurements may be utilized to determine “unhealthy” thresholds or ranges for glucose and/or lactate levels post-consumption of a meal. Thereafter, the unhealthy thresholds or ranges for glucose and/or lactate may be utilized to notify the user about whether a meal was unhealthy for the user based on real-time measurements of glucose and/or lactate.” [0186] “For example, one concept for measuring lactate levels is to have the user exercise at an intensity such that the user's lactate level increases to a certain level, e.g., between 4-10 mmol/L, or reaches the user's lactate threshold, for example. Once this level is achieved, exercise may be stopped.”)
and when the analyte value is below the first threshold and the determined trend is below the second threshold, preventing transmission of the alert ([0165] “As mentioned with respect to FIG. 3, a baseline lactate level may be indicative of the user's normal lactate values while the user is at rest (e.g., sedentary). Assuming a baseline lactate level of the user is 2 mmol/L, decision support system 100 may determine an amount of time it takes measured lactate levels to reach 2 mmol/L after peak lactate concentrations of 8 mmol/L and 5 mmol/L.”)
Regarding claim 33, Ray discloses wherein the determined trend is a rate of change of the analyte value over a given time period ([0109] “In certain embodiments, a lactate production rate may be determined by assessing an increase in lactate levels over a specified amount of time.”)
Regarding claim 34, Ray discloses determining content of the notification based on the predicted patient outcome and the use-case deployment ([0077] “Generally, monitoring lactate levels of the user over time may demonstrate the effect of a medication type or dosage on the user's liver health. Lactate levels over time may demonstrate, for example, that the user is sensitive to a particular type of statin and the user may be recommended to use an alternative statin.”)
Regarding claim 35, Ray discloses 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 ([0145] “In another example, at block 402, continuous analyte monitoring system 104 may continuously monitor ketone levels of the user, during a first time period. […] ketone specific metrics may aid in the recommendation of a specific diet for the user diagnosed with liver disease, and further provide real-time feedback on the improvement of liver dysfunction”)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 30 is rejected under 35 U.S.C. 103 as being unpatentable over Ray et al. (US20230240589A1) in view of Jain et al. (US11342051B1).
Regarding claim 30, Ray discloses 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 the level of severity of the predicted patient outcome ([0145] “In another example, at block 402, continuous analyte monitoring system 104 may continuously monitor ketone levels of the user, during a first time period. […] ketone specific metrics may aid in the recommendation of a specific diet for the user diagnosed with liver disease, and further provide real-time feedback on the improvement of liver dysfunction”)
Ray does not explicitly disclose however Jain teaches 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 ([pg. 9] “Individual user devices, such as mobile phones, can detect wireless signals from any of a variety of sources, e.g., other user devices, GPS, Wi-Fi, cellular towers, location beacons, and so on. This data can indicate the locations of, for example, a user's phone over time and/or the proximity of the phone to devices of other users.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the monitoring system of Ray 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 as taught by Jain since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
No prior Art Rejection
Regarding claim 32, no prior art rejection is being presented at this time.
Prior Art Cited but Not Relied Upon
Wang, X., Wang, X., Zhao, L., & Song, L. (2026). Machine learning-based identification of lactate metabolism-associated biomarkers in non-alcoholic fatty liver disease. Clinical and Experimental Medicine.
This reference is relevant because it discloses analyzing lactate data with a machine learning model to determine liver disease similar to the present invention.
Conclusion
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/WINSTON R FURTADO/Primary Examiner, Art Unit 3687
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