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 1-5, 7-9, 11-15, 17-21, 23-29, 31, 33, and 35-41 are pending.
Claims 1, 4, 14, 27, 31, and 33 are amended.
Claims 6, 10, 16, 22, 30, 32, and 34 are canceled.
Claims 39-41 are new.
Claims 1-5, 7-9, 11-15, 17-21, 23-29, 31, 33, and 35-41 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 1-5, 7-9, 11-15, 17-21, 23-29, 31, 33, and 35-41 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 1-5, 7-9, 11-13, 27-29, 31, 33, 35 and 36-41 are directed toward a computer-implemented method (i.e. a process) and claims 14-15, 17-21, 23-26 are directed toward a system (i.e., machine). 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, 14, 27, 31, and 33 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 computer-implemented method for automated provisioning of clinical information to a provider caring for a patient with diabetes (PWD), comprising: detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of the provider, wherein the insulin therapy data comprises glucose data and insulin data; identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern; prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a recommended therapy setting change; responsive to selecting the therapy insight, automatically sending, to a provider-dashboard associated with the provider caring for the PWD, the selected therapy insight; receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes the recommended therapy setting change; responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended therapy setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended therapy setting change causes the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended therapy setting change; and administering insulin to the PWD with the closed loop delivery system in accordance with the recommended therapy setting change”.
The limitations of detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of the provider, wherein the insulin therapy data comprises glucose data and insulin data; identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern; prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a recommended therapy setting change; responsive to selecting the therapy insight, automatically sending, the selected therapy insight; receiving, a selection of the one or more predefined clinical advice that includes the recommended therapy setting change; responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended therapy setting change, and wherein the recommended therapy setting change… to automatically adjust delivery of insulin in accordance with the recommended therapy setting change; and administering insulin to the PWD… in accordance with the recommended therapy setting change, 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 detecting, identifying, prioritizing, selecting, sending, and administering, which is properly interpreted as a “personal behavior”), 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.
Further, the abstract idea of claim 14 is identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Additionally, claim 27 recites: “A computer-implemented method for managing insulin therapy settings for a patient with diabetes (PWD), comprising: detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of a provider, wherein the insulin therapy data comprises glucose data and insulin data; identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern; prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a behavior recommendation; responsive to selecting the therapy insight, automatically sending the one or more predefined clinical advice to a provider-dashboard associated with the provider caring for the PWD and to a user-dashboard associated with the PWD; receiving an acceptance of the behavior recommendation associated with the one or more predefined clinical advice; identifying a recommended therapy setting change associated with the accepted behavior recommendation; adjusting a therapy setting based on the recommended therapy setting change; and sending the adjusted therapy setting to the user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the adjusted therapy setting is further configured to cause the closed loop delivery system to automatically administer insulin in accordance with the adjusted therapy setting”.
The limitations of detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of a provider, wherein the insulin therapy data comprises glucose data and insulin data identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern; prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a behavior recommendation; responsive to selecting the therapy insight, automatically sending the one or more predefined clinical advice; receiving an acceptance of the behavior recommendation associated with the one or more predefined clinical advice; identifying a recommended therapy setting change associated with the accepted behavior recommendation; adjusting a therapy setting based on the recommended therapy setting change; and sending the adjusted therapy setting, and wherein the adjusted therapy setting is further configured to… automatically administer insulin in accordance with the adjusted therapy setting, 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 detecting, identifying, prioritizing, selecting, sending, receiving, adjusting, and administering, which is properly interpreted as a “personal behavior”), 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.
Further, the abstract idea of claim 31 is identical as the abstract idea of claim 27. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Additionally, claim 33 recites: “A computer-implemented method for automated provisioning of one or more predefined clinical advice to a provider caring for a patient with diabetes (PWD), comprising: detecting a pattern in insulin therapy data associated with the PWD, wherein the insulin therapy data comprises glucose data and insulin data; responsive to detecting the pattern in the insulin therapy data, identifying, via an insights engine associated with a first computing system, a predefined behavior of a PWD; prioritizing, by the insights engine associated with the first computing system, therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting, by the insights engine, a therapy insight associated with the identified predefined behavior; responsive to the selecting the therapy insight, automatically sending over a network, to a health care provider engine associated with a second computing system separate and distinct from the first computing system, the selected therapy insight, wherein the health care provider engine is configured to automatically send the therapy insight to a provider-dashboard associated with the provider caring for the PWD; receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes a recommended therapy setting change, wherein the recommended therapy setting change comprises increasing or decreasing an insulin dose; and responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended therapy setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended therapy setting change causes the closed loop delivery system to automatically administer insulin in accordance with the recommended therapy setting change”.
The limitations of detecting a pattern in insulin therapy data associated with the PWD, wherein the insulin therapy data comprises glucose data and insulin data; responsive to detecting the pattern in the insulin therapy data, identifying, a predefined behavior of a PWD; prioritizing, therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting, a therapy insight associated with the identified predefined behavior; responsive to the selecting the therapy insight, automatically sending, the selected therapy insight, automatically send the therapy insight associated with the provider caring for the PWD; receiving, a selection of the one or more predefined clinical advice that includes a recommended therapy setting change, wherein the recommended therapy setting change comprises increasing or decreasing an insulin dose; and responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended therapy setting change, and wherein the recommended therapy setting change… to automatically administer insulin in accordance with the recommended therapy setting change, 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 detecting, identifying, prioritizing, selecting, sending, receiving, and administering, which is properly interpreted as a “personal behavior”), 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 2-5, 7-9, 11-13, 15, 17-21, 23-26, 28-29, and 35-41 include other limitations (i.e., sending, identifying, correlating, prioritizing, determining, forgoing, receiving, display, change, transmitting, corresponding, and selecting), 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, 14, 27, 31, and 33. 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 1-5, 7-9, 11-13, 15, 17-21, 23-26, 28-29, 31, 33, and 35-41 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 “provider-dashboard”, “user-dashboard”, “insights engine”, “computing system”, “network”, and “healthcare provider engine”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014]-[0015], and [0023], 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, “to a provider-dashboard associated with the provider caring for the PWD”, “via the provider-dashboard”, “to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system”, “causes the closed loop delivery system”, “with the closed loop delivery system, “to a provider-dashboard associated with the provider caring for the PWD and to a user-dashboard associated with the PWD’, “to the user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system”, “cause the closed loop delivery system to”, “via an insights engine associated with a first computing system”, “by the insights engine associated with the first computing system”, “by the insights engine”, “over a network, to a health care provider engine associated with a second computing system separate and distinct from the first computing system”, “wherein the health care provider engine is configured to”, “to a provider-dashboard”, “via the provider-dashboard”, “to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system”, and “causes the closed loop delivery system”, 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 an acceptance of the behavior recommendation associated with the one or more predefined clinical advice” and “receiving, a selection of the one or more predefined clinical advice that includes a recommended therapy setting change”, 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 2-5, 7-9, 11-13, 15, 17-21, 23-26, 28-29, and 35-41 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, 14, 27, 31, and 33, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “provider-dashboard”, “user-dashboard”, “insights engine”, “computing system”, “network”, and “healthcare provider engine”) 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 2-5, 7-9, 11-13, 15, 17-21, 23-26, 28-29, and 35-41 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, 14, 27, 31, and 33, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving an acceptance of the behavior recommendation associated with the one or more predefined clinical advice” and “receiving, a selection of the one or more predefined clinical advice that includes a recommended therapy setting change”), 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)):
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.” (emphasis added)).
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 1-5, 7-9, 11-15, 17-21, 23-29, 31, 33, and 35-41 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 1-5, 7-9, 11-15, 17-21, 23-29, 31, 33, and 35-41 are rejected under 35 U.S.C. 103 as being unpatentable over Budiman et al. (US 20140350369 A1), hereinafter Budiman, in view of Bernstein et al. (US 20170147769 A1), hereinafter Bernstein, Kanderian et al. (US 20140303552 A1), hereinafter Kanderian, and Palerm (US 20150306312 A1).
Regarding claim 1 Budiman teaches a computer-implemented method for automated provisioning of clinical information to a provider caring for a patient with diabetes (PWD) (Budiman, Abstract), comprising: detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of the provider, (Budiman, Abstract, FIG. 12, [0023], [0134]: "In order to provide further insight into the timing and potential patterns of the self-care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain", and [0245]: "Pattern analysis using temporal relationships opens up new analysis results that would have been missed when only looking at the collected data in aggregated form”), wherein the insulin therapy data comprises glucose data and insulin data (Budiman, [0016]: "From the standpoint of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self-care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia", [0127], [0131], and [0132]: "One or more episode chains are associated with a clinically- meaningful self-care behavior. These behaviors would be selected because of the risk they pose to the patient and/or the possible interventions (medications, education, etc.) ""); identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern (Budiman, [0116], [0118], and [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes"); selecting a therapy insight associated with the identified predefined behavior (Budiman, FIG. 20, [0048], and [0155]: "An augmentation of the treatment recommendation described above using the Control Grid algorithm is to provide second-stage logic to further narrow the possible recommendations that can be made. For instance, there are many different recommendations for reducing glucose variability, such as "stop snacking", "don't forget to take your medication", "don't miss meals", "adjust correction dose of insulin". A glucose control zone may be associated with a number of these recommendations. A second stage of logic may be used to narrow down the list of recommendations. Detection of episodic patterns, as described elsewhere, can be used in this second stage to narrow the list of recommendations"), wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a recommended therapy setting change (Budiman, [0118], [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short-and long-term complications associated with diabetes ", and [0155]); responsive to selecting the therapy insight, automatically sending, to a provider-dashboard associated with the provider caring for the PWD, the selected therapy insight (Budiman, FIG. 56, [0023], [0151], and [0290]).
Budiman does not teach prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame and receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes a recommended setting change.
However, Bernstein teaches prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame (Bernstein, [0085], [0126], and [0235]) and receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes a recommended setting change (Bernstein, FIG. 30, [0156], [0190], [0268], and [0274 ]-[0277]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for a way to prioritize insights of various therapies based on behavior within a period of time and recommended setting change (Bernstein, Abstract, [0126] and [0235]).
Bernstein teaches responsive to selecting of the one or more predefined clinical advice, automatically sending the recommended setting change (Bernstein, [0268]).
The combination of Budiman and Bernstein do not teach automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change.
However, Kanderian teaches automatically sending the recommended therapy setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended therapy setting change causes the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0006 ]-[0008], [0010], [0015], [0105], and [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman and Bernstein to incorporate the teachings of Kanderian and account for sending the recommended setting change to a user-dashboard and adjusting delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0001]-[0005]).
Budiman, Bernstein, and Kanderian do not teach administering insulin to the PWD with the closed loop delivery system in accordance with the recommended therapy setting change.
However, Palerm teaches administering insulin to the PWD with the closed loop delivery system in accordance with the recommended therapy setting change (Palerm, [0043]: “In exemplary embodiments, the infused fluid is insulin, although many other fluids may be administered through infusion such as, but not limited to, HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatments, medications, vitamins, hormones, or the like”, [0047]: “In other embodiments, the CCD 106 may provide information to the infusion device 102 to autonomously control the rate or dose of medication administered into the body of the user”, and [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value. the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 2 Budiman further teaches sending the selected therapy insight comprises: sending one or more of a behavior recommendation to adjust an undesirable behavior or a behavior recommendation to continue a desirable behavior (Budiman, [0023] and [0016]: "From the standpoint of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self-care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia").
Regarding claim 3 Budiman further teaches sending the selected insight comprises: sending one or more of a long-acting dosing recommendation and a rapid acting dosing recommendation (Budiman,[0023], [0016], [0142]: "A specific recommendation related to increasing glucose median would be to reduce the dose or dose rate of glucose-lowering medication", and [0238]).
Regarding claim 4 Budiman further teaches identifying a predefined behavior of a PWD further comprises: identifying whether one or more of a scheduled doses of insulin was administered to the PWD or an incorrect dose of insulin was administered to the PWD (Budiman, [0053], [0054], [0056], [0057], [0063], and [0088]).
Regarding claim 5 Budiman further teaches correlating therapy insights to associated health risks (Budiman, [0094]); and prioritizing the therapy insights based on a severity of the health risks (Budiman, [0139]).
Regarding claim 7 Budiman further teaches responsive to determining that the insulin therapy data does not correspond to a clinically relevant pattern, forgo identifying the predefined behavior (Budiman, [0128] and [0130]).
Regarding claim 8 Budiman further teaches the insulin therapy data is selected from a group consisting of: glucose data, therapy settings and insulin dosing data (Budiman, Abstract, [0023], [0088], [0090], and [0108]).
Regarding claim 9 Budiman further teaches the insulin therapy data comprises glucose measurements continuously received (Budiman, [0116] and [0260]), wherein at least some of the glucose measurements includes date, time and glucose value (Budiman, [0087] and [0109]).
Regarding claim 10 Budiman further teaches the insulin therapy data is dosing information that includes date, time and amount of a dose of insulin (Budiman, TABLE 3, [0087], and [0109]).
Regarding claim 11 Budiman further teaches receiving selection of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard (Budiman, [0023] and [0016]).
Regarding claim 12 Budiman further teaches receiving an indication of cancelation of the one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard (Budiman, [0118], [0155], and [0157]).
Regarding claim 13 Budiman further teaches receiving adjustments of the therapy insight sent to the provider-dashboard (Budiman, [0157]).
Regarding claim 14 Budiman teaches a system for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD) comprising: an insights engine (Budiman, [0023] and [0290]) configured to: detect a clinically relevant pattern in insulin therapy data of a patient under the care of a provider (Budiman, Abstract, FIG. 12, [0023], [0134]: "In order to provide further insight into the timing and potential patterns of the self-care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain', and [0245]: "Pattern analysis using temporal relationships opens up new analysis results that would have been missed when only looking at the collected data in aggregated form"), wherein the insulin therapy data comprises glucose data and insulin data (Budiman, [0116]: "From the stand point of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self-care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia", [0127], [0131] and [0132]; "One or more episode chains are associated with a clinically-meaningful self-care behavior. These behaviors would be selected because of the risk they pose to the patient and/or the possible interventions (medications, education, etc.)""); identify a predefined behavior of the PWD responsive to the detected clinically relevant pattern (Budiman, [0016], [0118], and [0125]: "The current invention leverages clinically- informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes ); and select a therapy insight associated with the identified predefined behavior (Budiman, FIG. 20, [0048], and [0155]: "An augmentation of the treatment recommendation described above using the Control Grid algorithm is to provide second-stage logic to further narrow the possible recommendations that can be made. For instance, there are many different recommendations for reducing glucose variability, such as "stop snacking", "don't forget to take your medication", "don't miss meals", "adjust correction dose of insulin". A glucose control zone may be associated with a number of these recommendations. A second stage of logic may be used to narrow down the list of recommendations. Detection of episodic patterns, as described elsewhere, can be used in this second stage to narrow the list of recommendations "), wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a recommended therapy setting change (Budiman, [0118], [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes", and [0155]), and a health care provider (HCP) engine configured to automatically send, to a provider- dashboard associated with the provider caring for the PWD, the selected therapy insight (Budiman, FIG. 56, [0023], [0151], and [0290]).
` Budiman does not teach prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame and receive a selection of the one or more predefined clinical advice that includes a recommended setting change.
However, Bernstein teaches prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame (Bernstein, [0085], [0126], and [0235]) and receive a selection of the one or more predefined clinical advice that includes a recommended setting change (Bernstein, FIG. 30, [0156], [0190], [0268], and [0274]-[0277]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for a way to prioritize insights of various therapies based on behavior within a period of time (Bernstein, Abstract, [0126] and [0235]).
Bernstein teaches responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended setting change (Bernstein, [0268]).
The combination of Budiman and Bernstein do not teach automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change.
However, Kanderian teaches automatically sending the recommended therapy setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended therapy setting change is further configured to cause the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0006]-[0008], [0010], [0015], [0105], and [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman and Bernstein to incorporate the teachings of Kanderian and account for sending the recommended setting change to a user-dashboard and adjusting delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0001]-[0005]).
Budiman, Bernstein, and Kanderian do not teach administering insulin to the PWD with the closed loop delivery system in accordance with the recommended therapy setting change.
However, Palerm teaches administering insulin to the PWD with the closed loop delivery system in accordance with the recommended therapy setting change (Palerm, [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 15 Budiman further teaches the HCP engine is further configured to send one or more of a behavior recommendation to adjust an undesirable behavior or a behavior recommendation to continue a desirable behavior (Budiman, [0023] and [0016]: "From the standpoint of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self-care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia').
Regarding claim 17 Budiman further teaches the HCP engine is further configured to receive selection of one or more behavior recommendations associated with the therapy insight sent to the provider- dashboard (Budiman, [0023] and [0016]).
Regarding claim 18 Budiman further teaches the HCP engine is further configured to receive an indication of cancelation of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard (Budiman, [0118], [0155], and [0157]).
Regarding claim 19 Budiman further teaches the HCP engine is further configured to receive adjustments of the therapy insight sent to the provider-dashboard (Budiman, [0157]).
Regarding claim 20 Budiman further teaches the insights engine is further configured to identify whether one or more of a scheduled doses of insulin was administered to the PWD or an incorrect dose of insulin was administered to the PWD (Budiman, [0088]).
Regarding claim 21 Budiman further teaches the insights engine is further configured to: correlate therapy insights to associated health risks (Budiman, [0094]); and prioritize the therapy insights based on a severity of the health risks (Budiman, [0139]).
Regarding claim 23 Budiman further teaches the insights engine is further configured to prioritize therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame (Budiman, [0139], [0155], and [0203]).
Regarding claim 24 Budiman further teaches the insulin therapy data is selected from a group consisting of: glucose data, therapy settings and insulin dosing data (Budiman, Abstract, [0023], [0088], [0090], and [0108]).
Regarding claim 25 Budiman further teaches the insulin therapy data comprises glucose measurements continuously received (Budiman, [0116] and [0260]), wherein at least some of the glucose measurements include date, time and glucose value (Budiman, [0087] and [0109]).
Regarding claim 26 Budiman further teaches the insulin therapy data comprises dosing information that includes date, time and amount of a dose of insulin (Budiman, TABLE 3, [0087], and [0109]).
Regarding claim 27 Budiman teaches a computer-implemented method for managing insulin therapy settings for a patient with diabetes (PWD), comprising: detecting a clinically relevant pattern in insulin therapy data of a patient under the care of a provider (Budiman, Abstract, FIG. 12, [0023], [0134]: "In order to provide further insight into the timing and potential patterns of the self- care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain", and [0245]: "Pattern analysis using temporal relationships opens up new analysis results that would have been missed when only looking at the collected data in aggregated form"), wherein the insulin therapy data comprises glucose data and insulin data (Budiman, [0116]: "From the standpoint of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self- care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia", [0127], [0131], and [0132]: "One or more episode chains are associated with a clinically-meaningful self-care behavior. These behaviors would be selected because of the risk they pose to the patient and/or the possible interventions (medications, education, etc.)''); identifying a predefined behavior of a PWD responsive to the detected clinically relevant pattern (Budiman, [0116], [0118], and [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self- care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes"); selecting a therapy insight of the prioritized therapy insights associated with the identified predefined behavior (Budiman, FIG. 20, [0048], and [0155]: "An augmentation of the treatment recommendation described above using the Control Grid algorithm is to provide second-stage logic to further narrow the possible recommendations that can be made. For instance, there are many different recommendations for reducing glucose variability, such as "stop snacking", "don't forget to take your medication", "don't miss meals", "adjust correction dose of insulin". A glucose control zone may be associated with a number of these recommendations. A second stage of logic may be used to narrow down the list of recommendations. Detection of episodic patterns, as described elsewhere, can be used in this second stage to narrow the list of recommendations"), wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation (Budiman, [0118], [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes". and [0155]); responsive to the selecting the therapy insight, automatically sending the one or more predefined clinical advice to a user-dashboard associated with a PWD and to a provider-dashboard associated with the provider caring for the PWD (Budiman, FIG. 56, [0023], [0151], and [0290]); receiving an acceptance of a behavior recommendation associated with a therapy insight (Budiman, [0118], [0125 ]-[0127], [0133], and [0235]); identifying a recommended insulin therapy setting associated with the accepted behavior recommendation (Budiman, [0118], [0125]- [0127], [0133], and [0235]); adjusting a therapy setting based on the recommended therapy setting change (Budiman, [0125]-[0127], [0133], [0235], and [0246]); and sending the adjusted therapy setting to the user-dashboard (Budiman, [0133]-[0134]).
Budiman does not teach prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight of the prioritized therapy insights associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation.
However, Bernstein teaches prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight of the prioritized therapy insights associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation (Bernstein, [0085], [0126], and [0235]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for a way to prioritize insights of various therapies based on behavior within a period of time (Bernstein, Abstract, [0126] and [0235]).
Bernstein teaches responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended setting change (Bernstein, [0268]).
The combination of Budiman and Bernstein do not teach automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop system to automatically adjust delivery of insulin in accordance with the recommended setting change.
However, Kanderian teaches automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system (Kanderian, Abstract, [0006 ]-[0008], [0010], [0015], [0105], and [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman and Bernstein to incorporate the teachings of Kanderian and account for sending the recommended setting change to a user-dashboard and adjusting delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0001]-[0005]).
Budiman, Bernstein, and Kanderian do not teach automatically administer of insulin in accordance with the adjusted therapy setting.
However, Palerm teaches automatically administer of insulin in accordance with the adjusted therapy setting (Palerm, [0043]: “In exemplary embodiments, the infused fluid is insulin, although many other fluids may be administered through infusion such as, but not limited to, HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatments, medications, vitamins, hormones, or the like”, [0047]: “In other embodiments, the CCD 106 may provide information to the infusion device 102 to autonomously control the rate or dose of medication administered into the body of the user”, and [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value. the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 28 Budiman further teaches sending the adjusted therapy setting to the user-dashboard automatically adjust a therapy setting based on the recommended therapy setting change (Budiman, [0254]: "In the opposite case, where it is found that there is much more interday variability than intraday variability, treatment recommendations may be focused on lifestyle changes such as meal timing and exercise ").
Regarding claim 29 Budiman further teaches the adjusted therapy setting is displayed on the user-dashboard (Budiman, [0133 ]-[0134] and [0290]).
Regarding claim 30 Budiman further teaches operating an insulin delivery system with the adjusted therapy setting (Budiman, [0247]-[0249]).
Regarding claim 31 Budiman teaches a computer-implemented method for managing insulin therapy settings for a patient with diabetes (PWD), comprising: detecting a clinically relevant pattern in insulin therapy data of the PWD under the care of a provider (Budiman, Abstract, FIG. 12, [0023], [0134]: "In order to provide further insight into the timing and potential patterns of the self-care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain", and [0245]: "Pattern analysis using temporal relationships opens up new analysis results that would have been missed when only looking at the collected data in aggregated form"), wherein the insulin therapy data comprises glucose data and insulin data (Budiman, [0116]: "From the standpoint of insulin-based treatments, this method provides standardized guidance for medication adjustment (increase, decrease or maintain), and highlights the necessity to address self- care behaviors in order to reduce glucose variability that is elevated to the point that it limits managing both hyperglycemia and hypoglycemia", [0127], [0131], and [0132]: "One or more episode chains are associated with a clinically-meaningful self-care behavior. These behaviors would be selected because of the risk they pose to the patient and/or the possible interventions (medications, education, etc.”) identifying a predefined behavior of the PWD responsive to the detected clinically relevant pattern (Budiman, [0116], [0118], and [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes "); selecting a therapy insight associated with the identified predefined behavior (Budiman, FIG. 20, [0048], and [0155]: "An augmentation of the treatment recommendation described above using the Control Grid algorithm is to provide second- stage logic to further narrow the possible recommendations that can be made. For instance, there are many different recommendations for reducing glucose variability, such as "stop snacking", "don't forget to take your medication", "don't miss meals", "adjust correction dose of insulin'. A glucose control zone may be associated with a number of these recommendations. A second stage of logic may be used to narrow down the list of recommendations. Detection of episodic patterns, as described elsewhere, can be used in this second stage to narrow the list of recommendations”), wherein the therapy insight comprises one or more predefined clinical advice associated with the PWD that includes a recommended therapy setting change (Budiman, [0118], [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes" and [0155]); responsive to selecting the therapy insight, automatically sending the one or more predefined clinical advice to a user- dashboard associated with the PWD (Budiman, FIG. 56, [0023], [0151], and [0290]); receiving an acceptance to adjust a therapy setting displayed on the user-dashboard (Budiman, [0118], [0125]-[0127], [0133], and [0235]), wherein the adjusted therapy setting is based on the recommended therapy setting change and is associated with the behavior recommendation for the PWD based at least partially on the selected therapy insight (Budiman, [0133], [0134], and [0159]); automatically adjusting the insulin therapy setting corresponding to the accepted adjusted insulin therapy setting (Budiman, [0125]-[0127], [0133], [0235], and [0246]); and sending the adjusted insulin therapy setting to the user-dashboard (Budiman, [0133]-[0134]).
Budiman does not teach prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight of the prioritized therapy insights associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation.
However, Bernstein teaches prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame; selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation (Bernstein, [0085], [0126], and [0235]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for a way to prioritize insights of various therapies based on behavior within a period of time (Bernstein, Abstract, [0126] and [0235]).
Bernstein teaches responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended setting change (Bernstein, [0268]).
The combination of Budiman and Bernstein do not teach automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change.
However, Kanderian teaches automatically sending the adjusted therapy setting to a user- dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system (Kanderian, Abstract, [0006 ]-[0008], [0010], [0015], [0105], and [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman and Bernstein to incorporate the teachings of Kanderian and account for sending the recommended setting change to a user-dashboard and adjusting delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0001]-[0005]).
Budiman, Bernstein, and Kanderian do not teach automatically administer of insulin in accordance with the adjusted therapy setting.
However, Palerm teaches automatically administer of insulin in accordance with the adjusted therapy setting (Palerm, [0043]: “In exemplary embodiments, the infused fluid is insulin, although many other fluids may be administered through infusion such as, but not limited to, HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatments, medications, vitamins, hormones, or the like”, [0047]: “In other embodiments, the CCD 106 may provide information to the infusion device 102 to autonomously control the rate or dose of medication administered into the body of the user”, and [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value. the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 33 Budiman teaches a computer-implemented method for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD), comprising: detecting a pattern in insulin therapy data associated with the PWD, wherein the detected pattern is indicative of at least one of desirable behaviors or undesirable behaviors exhibited by the PWD related to insulin-based management of the PWD's diabetes (Budiman, Abstract, FIG. 12, [0023], [0134]: "In order to provide further insight into the timing and potential patterns of the self-care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain", and [0245]: "Pattern analysis using temporal relationships opens up new analysis results that would have been missed when only looking at the collected data in aggregated form' responsive to detecting the pattern in insulin therapy data”), identifying, via an insights engine associated with a first computing system, a predefined behavior of a PWD (Budiman, [0116], [0118], and [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes"), selecting, by the insights engine, a therapy insight of the prioritized therapy insights associated with the identified predefined behavior (Budiman, FIG. 20, [0048], and [0155]: "An augmentation of the treatment recommendation described above using the Control Grid algorithm is to provide second-stage logic to further narrow the possible recommendations that can be made. For instance, there are many different recommendations for reducing glucose variability, such as "stop snacking', "don't forget to take your medication, "don't miss meals", "adjust correction dose of insulin". A glucose control zone may be associated with a number of these recommendations. A second stage of logic may be used to narrow down the list of recommendations. Detection of episodic patterns, as described elsewhere, can be used in this second stage to narrow the list of recommendations "); responsive to the selecting the therapy insight, automatically sending over a network, to a health care provider engine associated with a second computing system separate and distinct from the first computing system, the selected therapy insight, wherein the health care provider engine is configured to automatically send the therapy insight to a provider-dashboard associated with a provider caring for the PWD (Budiman, FIG. 56, [0023], [0151], and [0290]).
Budiman does not teach prioritizing, by the insights engine associated with the first computing system, therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame and receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes a recommended setting change.
However, Bernstein teaches prioritizing, by the insights engine associated with the first computing system, therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame (Bernstein, [0085], [0126], and [0235]) and receiving, via the provider-dashboard, a selection of the one or more predefined clinical advice that includes a recommended setting change, (Bernstein, FIG. 30, [0156], [0190], [0268], and [0274]-[0277]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for a way to prioritize insights of various therapies based on behavior within a period of time and recommend a setting change (Bernstein, Abstract, [0126] and [0235]).
Bernstein teaches responsive to the selection of the one or more predefined clinical advice, automatically sending the recommended setting change (Bernstein, [0268]).
The combination of Budiman and Bernstein do not teach automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system to automatically adjust delivery of insulin in accordance with the recommended setting change.
However, Kanderian teaches automatically sending the recommended setting change to a user-dashboard, wherein the user-dashboard is associated with a closed loop delivery system, and wherein the recommended setting change is further configured to cause the closed loop delivery system (Kanderian, Abstract, [0006]-[0008], [0010], [0015], [0105], and [0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman and Bernstein to incorporate the teachings of Kanderian and account for sending the recommended setting change to a user-dashboard and adjusting delivery of insulin in accordance with the recommended setting change (Kanderian, Abstract, [0001]-[0005]).
Budiman, Bernstein, and Kanderian do not teach automatically adjust delivery of insulin in accordance with the recommended setting change and wherein the recommended therapy setting change comprises increasing or decreasing an insulin dose.
However, Palerm teaches automatically adjust delivery of insulin in accordance with the recommended setting change (Palerm, [0043]: “In exemplary embodiments, the infused fluid is insulin, although many other fluids may be administered through infusion such as, but not limited to, HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatments, medications, vitamins, hormones, or the like”, [0047]: “In other embodiments, the CCD 106 may provide information to the infusion device 102 to autonomously control the rate or dose of medication administered into the body of the user”, and [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value. the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user") wherein the recommended therapy setting change comprises increasing or decreasing an insulin dose (Palerm, [0043]: “In exemplary embodiments, the infused fluid is insulin, although many other fluids may be administered through infusion such as, but not limited to, HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatments, medications, vitamins, hormones, or the like”, [0047]: “In other embodiments, the CCD 106 may provide information to the infusion device 102 to autonomously control the rate or dose of medication administered into the body of the user”, [0067]: “the pump control system 520 may decrease and/or increase the dosage command to attempt to maintain the blood glucose level between the resume delivery threshold value and the suspend delivery threshold value, and thereby improve the likelihood of the blood glucose level being maintained at or near the target value”, and [0122]: "the current delivery mode is a PID closed-loop delivery mode, the command generation application 1114 continues generating delivery commands using the current glucose measurement value and/or the predicted blood glucose value. the delivery command generated by the command generation application 1114 is provided to the motor control module 512, which, in turn, operates the motor 507 to displace the plunger 517 and infuse or otherwise deliver insulin to the body 501 of the user").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 35 Budiman further teaches receiving a selected therapy insight from the healthcare provider engine (Budiman, [0116], [0125], and [0127]); and transmitting the selected therapy insight to a PWD dashboard (Budiman, [0133]-[0134] and [0290]).
Regarding claim 36 Budiman further teaches the recommended therapy setting change comprises a change from an initial insulin sensitivity factor to an adjusted insulin sensitivity factor (Budiman, [0206]: "the first titration amount may be preset to correspond to a conservative value defined by predetermined patient information such as patient weight or known insulin sensitivity, or it may be defined as a conservative value based on a worse case physiological model of a patient (that is, the most insulin sensitive). For a subsequent titration, the insulin titration sensitivity (ITS) may be estimated as the change-in-median-glucose/change-in-insulin).
Regarding claim 37 Budiman does not teach the recommended therapy setting change comprises a change from an initial carbohydrate ratio to an adjusted carbohydrate ratio.
However, Bernstein teaches the recommended therapy setting change comprises a change from an initial carbohydrate ratio to an adjusted carbohydrate ratio (Bernstein, [0267]: "The titration amounts may be preconfigured, or the algorithm could determine titration amounts based on preconfigured parameters, such as maximum titration amount, maximum total dose amount, and parameters common to bolus calculators, such as insulin sensitivity and carbohydrate ratio" and [0301]: "These parameters would not need to be such that they are usable directly by the patient (e.g., insulin-to-carb ratio or insulin-to-glucose ratio) but may be combinations of these").
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman to incorporate the teachings of Bernstein and account for the therapy recommendation based on the change in carbohydrate intake (Bernstein, Abstract, [0267], and [0301]).
Regarding claim 38 Budiman further teaches the recommended therapy setting change comprises a change from an initial basal rate to an adjusted basal rate (Budiman, [0225]: "it was assumed that a change in basal insulin I affects the patient's median glucose Gm, but not glucose variability Gv. As a result, the notion of insulin titration sensitivity relates changes in I to changes in Gm" and [0238]).
Regarding claim 39 Budiman, Bernstein, and Kanderian do not teach the clinically relevant pattern comprises a glucose level for the PWD rising above an upper glucose threshold after a meal.
However, Palerm teaches the clinically relevant pattern comprises a glucose level for the PWD rising above an upper glucose threshold after a meal (Palerm, FIG. 20, [0067]: “if a predicted blood glucose level indicates that the user's blood glucose level is expected to fall below a lower threshold value, the pump control system 520 may reduce the dosage command (e.g., to zero or some other relatively smaller amount) to ensure the blood glucose level is maintained above the lower threshold value, even though the current blood glucose measurement value indicates the blood glucose level is sufficiently above the lower threshold value. Conversely, if the predicted blood glucose level indicates that the user's blood glucose level is expected to exceed an upper threshold value, the pump control system 520 may increase the dosage command to ensure the blood glucose level is maintained below the upper threshold value, even though the current blood glucose measurement value indicates the blood glucose level is sufficiently below the upper threshold value. For example, if a target blood glucose level is between a resume delivery threshold value and a suspend delivery threshold value, the pump control system 520 may decrease and/or increase the dosage command to attempt to maintain the blood glucose level between the resume delivery threshold value and the suspend delivery threshold value, and thereby improve the likelihood of the blood glucose level being maintained at or near the target value” and [0149]: “FIG. 20 depicts an exemplary IOB monitoring process 2000 suitable for implementation by a control system associated with a fluid infusion device, such as the pump control system 520, 1100 in the infusion device 502, to automatically operate the fluid infusion device to deliver fluid in a manner that regulates or otherwise controls the active amount of fluid (e.g., the IOB) in the body of a user (or patient). In this regard, in various situations, when the infusion rate is reduced to zero (e.g., in response to meal-related glucose fluctuations, erroneous and/or invalid glucose sensor measurement values, or the like) or suspended as described above in connection with delivery suspension process 1200, the active amount of fluid in the body of the user becomes depleted as the fluid is metabolized by the user's body. For example, when a current blood glucose measurement for the user that is fed back to the input of a closed-loop control system is below a target blood glucose reference value (or glucose setpoint), the closed-loop control system may output a delivery command of zero (since insulin cannot be removed from the user's body) until the user's current glucose measurement begins increasing towards and/or above the target value. During this time period, any remaining active insulin in the user's body is gradually being metabolized. Depending on how long the insulin delivery rate is equal to zero or infusion is otherwise suspended, the user's IOB may be relatively low (and potentially depleted completely) before insulin infusion is resumed when the user's current glucose measurement exceeds the target glucose value. The delay between when the user's IOB goes low or becomes depleted and when insulin delivery is resumed is further compounded by the delay between when insulin is infused and when the insulin is metabolized and begins to affect the user's glucose levels. As a result, the user's glucose level could be undesirably high before the infused insulin takes effect”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regrading claim 40 Budiman, Bernstein, and Kanderian do not teach the clinically relevant pattern comprises a glucose level for the PWD falling below a lower glucose threshold after a meal.
However, Palerm teaches the clinically relevant pattern comprises a glucose level for the PWD falling below a lower glucose threshold after a meal (Palerm, FIG. 20, [0067]: “if a predicted blood glucose level indicates that the user's blood glucose level is expected to fall below a lower threshold value, the pump control system 520 may reduce the dosage command (e.g., to zero or some other relatively smaller amount) to ensure the blood glucose level is maintained above the lower threshold value, even though the current blood glucose measurement value indicates the blood glucose level is sufficiently above the lower threshold value. Conversely, if the predicted blood glucose level indicates that the user's blood glucose level is expected to exceed an upper threshold value, the pump control system 520 may increase the dosage command to ensure the blood glucose level is maintained below the upper threshold value, even though the current blood glucose measurement value indicates the blood glucose level is sufficiently below the upper threshold value. For example, if a target blood glucose level is between a resume delivery threshold value and a suspend delivery threshold value, the pump control system 520 may decrease and/or increase the dosage command to attempt to maintain the blood glucose level between the resume delivery threshold value and the suspend delivery threshold value, and thereby improve the likelihood of the blood glucose level being maintained at or near the target value” and [0149]: “FIG. 20 depicts an exemplary IOB monitoring process 2000 suitable for implementation by a control system associated with a fluid infusion device, such as the pump control system 520, 1100 in the infusion device 502, to automatically operate the fluid infusion device to deliver fluid in a manner that regulates or otherwise controls the active amount of fluid (e.g., the IOB) in the body of a user (or patient). In this regard, in various situations, when the infusion rate is reduced to zero (e.g., in response to meal-related glucose fluctuations, erroneous and/or invalid glucose sensor measurement values, or the like) or suspended as described above in connection with delivery suspension process 1200, the active amount of fluid in the body of the user becomes depleted as the fluid is metabolized by the user's body. For example, when a current blood glucose measurement for the user that is fed back to the input of a closed-loop control system is below a target blood glucose reference value (or glucose setpoint), the closed-loop control system may output a delivery command of zero (since insulin cannot be removed from the user's body) until the user's current glucose measurement begins increasing towards and/or above the target value. During this time period, any remaining active insulin in the user's body is gradually being metabolized. Depending on how long the insulin delivery rate is equal to zero or infusion is otherwise suspended, the user's IOB may be relatively low (and potentially depleted completely) before insulin infusion is resumed when the user's current glucose measurement exceeds the target glucose value. The delay between when the user's IOB goes low or becomes depleted and when insulin delivery is resumed is further compounded by the delay between when insulin is infused and when the insulin is metabolized and begins to affect the user's glucose levels. As a result, the user's glucose level could be undesirably high before the infused insulin takes effect”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Budiman, Bernstein, and Kanderian to incorporate the teachings of Palerm and account for delivering/administering insulin to a patient through a closed-loop delivery system based on a therapy change (Palerm, Abstract and [0122]).
Regarding claim 41 Budiman further teaches the therapy insights corresponding to the predefined behavior are prioritized when the predefined behavior occurs more frequently than another predefined behavior during the predefined time frame (Budiman, [0116], [0118], [0125]: "The current invention leverages clinically-informed algorithms to search the data to reveal insights about the glucose control and self-care behaviors performed by the patient. These insights can then direct the care provider and patient to therapeutic and educational methods to improve diabetes self-care behaviors, improve glycemic control and reduce risks of short- and long-term complications associated with diabetes", [0132]: "One or more episode chains are associated with a clinically- meaningful self-care behavior. These behaviors would be selected because of the risk they pose to the patient and/or the possible interventions (medications, education, etc.) ", and [0134]: "In order to provide further insight into the timing and potential patterns of the self-care behaviors experienced by the patient, the episode chain or chains associated with each behavior may be shown in a time-of-day plot, with each episode indicated within the chain”).
Response to Arguments
Applicant's arguments filed 08/15/2025 have been fully considered. Regarding the 35 U.S.C. 101 Rejection, Applicant argues the amendment of “administering insulin” effects a particular treatment for a person with diabetes. Examiner respectfully disagrees. Although the claims recite “administering”, per MPEP 2106.04(d)(2): “to qualify as a “treatment” or “prophylaxis” limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of “administering amazonic acid to a patient” or a step of “administering a course of plasmapheresis to a patient.” If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the “treatment or prophylaxis” consideration. For example, a step of “prescribing a topical steroid to a patient with eczema” is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a “pharmaceutical composition” or that a “feed dispenser is operable to dispense a mineral supplement” are not affirmative limitations because they are merely indicating how the claimed invention might be used”). However, the treatment must also be “particular”, see MPEP 2106.04(d)(2) states “Conversely, consider a claim that recites the same abstract idea and “administering a suitable medication to a patient.” This administration step is not particular, and is instead merely instructions to “apply” the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application… The treatment or prophylaxis limitation must impose meaningful limits on the judicial exception, and cannot be extra-solution activity or a field-of-use. For example, consider a claim that recites (a) administering rabies and feline leukemia vaccines to a first group of domestic cats in accordance with different vaccination schedules, and (b) analyzing information about the vaccination schedules and whether the cats later developed chronic immune-mediated disorders to determine a lowest-risk vaccination schedule. Step (b) falls within the mental process grouping of abstract ideas enumerated in MPEP § 2106.04(a). While step (a) administers vaccines to the cats, this administration is performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Also, see Example #49, claim 1 of the July 2024 Subject Matter Eligibility Examples, which recites the limitation of “administering an appropriate treatment to the glaucoma patient at high risk of PI”, but is ineligible under 101 because “Although this limitation indicates that a treatment is to be administered, it does not provide any information as to how the patient is to be treated or what the treatment is, but instead covers any possible treatment that a medical professional decides to administer to the patient. As such, there are no meaningful constraints on the administering step such that the particular treatment or prophylaxis consideration would apply because it is not limited to any particular manner or type of treatment. See MPEP 2106.04(d)(2). Moreover, like the claims in Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 78 (2012), the claim merely provides the identification made in limitation (b) to the relevant audience (such as a physician or another medical professional) and at most adds a suggestion to take that identification into account when treating patients. Limitation (c) may thus be understood as no more than an attempt to generally link the judicial exception to a field of use. See MPEP 2106.05(h). Therefore, limitation (c) fails to meaningfully limit the claim because it does not require any particular application of the abstract idea and therefore amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed”.
Also see, Example #43, claim 1 of the October 2019 Update: Subject Matter Eligibility, which recites the limitation “administering a treatment to the patient having a non-responder phenotype”, but is ineligible under 101 because “Although this limitation indicates that a treatment is to be administered, it does not provide any information as to how the patient is to be treated, or what the treatment is, but instead covers any possible treatment that a doctor decides to administer to the patient. In fact, this limitation is recited at such a high level of generality that it does not even require a doctor to take the calculation step’s outcome (the patient’s phenotype) into account when deciding which treatment to administer, making the limitation’s inclusion in this claim at best nominal. Like the claims in Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 78 (2012), claim 1 here tells the relevant audience (doctors) about the mathematical concepts and at most adds a suggestion that the doctors take those laws into account when treating their patients. Limitation (b) thus fails to meaningfully limit the claim because it does not require any particular application of the recited calculation, and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Accordingly, limitation (b) does not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception”.
Regarding the 35 U.S.C. 103 Rejection, Applicant argues the prior art does not teach “prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame”. Examiner respectfully disagrees. Bernstein teaches [0154] “FIGS. 27-29 show an example of three iterations of the method. FIG. 27 shows an example of “original” data, that is, no replacement of glucose values by the high glucose setting has occurred. This data may or may not have had the high glucose alarm activated. If it were, then the actual experience of glucose alarm activation may be summarized. FIG. 28 shows an example of the hourly glucose distribution after estimating the impact of setting the high glucose alarm to 200 mg/dL. The estimated overall average glucose is seen to move closer to the target average glucose. Also, the hourly average glucose is seen to be impacted during parts of the day when the glucose was elevated above the high glucose alarm setting. FIG. 29 extends the example for the setting of the high glucose alarm to 160 mg/dL”, [0156]: “FIG. 30 shows a summary of estimated impact of high glucose alarm setting on high glucose alarms per day and average glucose. More specifically, FIG. 30 shows an example summary of iterations of estimating the impact on average glucose and number of high glucose alarms as the high glucose alarm setting is changed. It may be assumed that the right-most data point is either “actual” (experienced by the patient while wearing the device) or “simulated,” for example if the patient has not activated the high glucose alarm during the monitoring period that is being reviewed. As the high glucose alarm setting is reduced, the overall average glucose is reduced, and the number of glucose alarms increases. However, the invention here proposes estimating these values from individual monitoring (e.g., CGM) data, and therefore is more personalized than population-based methods or other methods that summarize other patients high glucose alarm frequency”, [0174]-[0175] “In some aspects of the present disclosure, the control grid based algorithm is used to process data for specific time periods of the day or relative time periods related to key events. For example, four key time periods may be defined as overnight/fasting (12 am-8 am), post breakfast (8 am-12 pm), post lunch (12 pm-6 pm), and post dinner (6 pm-12 am). Glucose data collected for multiple days can be grouped into these time periods and the control grid algorithm run for each group. In this way, recommendations that are specific to time periods may be generated. For instance, variability recommendations may be generated specific to meals or overnight. For patient's whose treatment is multiple daily injections (MDI) of insulin, the time-period targeted recommendations may be specific to insulin needs during these times of day. For instance, the control grid for the over-night/fasting period may indicate that medication dosage should be increased; the recommendation may indicate that the patient's long-acting insulin dose should be increased… In some instances, multiple control grids may be used. The treatment recommendation logic may in some cases be more complicated when multiple control grids are used. An example of this logic is shown in the attached FIG. 35. In the embodiment shown in FIG. 35, the method is repeated for different time-of-day (TOD) periods—e.g., post breakfast (PB), post lunch (PL), post dinner/early sleep (PD) and late sleep/walking (W). The results from each time period analysis may be used to determine an appropriate treatment recommendation”, and [0177] “In some instances, multiple control grids may be used. The treatment recommendation logic may in some cases be more complicated when multiple control grids are used. An example of this logic is shown in the attached FIG. 35. In the embodiment shown in FIG. 35, the method is repeated for different time-of-day (TOD) periods—e.g., post breakfast (PB), post lunch (PL), post dinner/early sleep (PD) and late sleep/walking (W). The results from each time period analysis may be used to determine an appropriate treatment recommendation” (e.g., “prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame”). Therefore, the 35 U.S.C. 101 Rejection is maintained.
Applicant also argues the combination Budiman, Bernstein, and Kanderian are not obvious. Examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art would know to combine Budiman, Bernstein, and Kanderian because Budiman teaches “system and method provides a glucose report for determining glycemic risk based on an ambulatory glucose profile of glucose data over a time period, a glucose control assessment based on median and variability of glucose, and indicators of high glucose variability”, Bernstein teaches “one or more software applications to help a user manager their diabetes”, and Kanderian teaches “an infusion system, which may be a closed loop infusion system or “semi-closed-loop” system, uses state variable feedback to control the rate that fluid is infused into the body of a user… The system may use three state variables, subcutaneous insulin concentration, plasma insulin concentration, and insulin effect, and corresponding gains, to calculate an additional amount of fluid to be infused as a bolus and to be removed from the basal delivery of the fluid”. All three prior arts makes multiple references to glucose, insulin, and/or treating a person with diabetes. Therefore, the 35 U.S.C. 103 Rejection is maintained.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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