DETAILED ACTION
This action is in reference to the communication filed on 7 APRIL 2026.
Amendments to claims 1, 10 are entered and considered.
Claims 1-20 are present and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-20 the independent claim(s) 1, 10 recite(s) an apparatus and a system, each of which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-20 the independent claim(s) (claims 1, 10) is/are directed, in part, to:
An apparatus for glycemic control of a user, comprising:
receive biological data of the user
select an activity mode of a plurality of activity modes of a wearable injection device based on at least one of the user input and the biological data, the activity mode indicative of a temporary condition affecting a blood glucose level of the user;
categorize the selected activity mode to an aggressiveness category;
modify a set of operational parameters of a medication delivery algorithm for calculating medication dosages based on the aggressiveness category of the selected activity mode; and
calculate an amount of medication to deliver to the user based on the modified set of operational parameters.
These claim elements are considered to be abstract ideas because they are directed to mental processes including concepts performed in the human mind such as observation, judgement, evaluation, and opinion. Receiving data (observation), selecting a mode regarding the temporary condition of the blood glucose (evaluation), categorizing the mode to a category, modifying parameters and calculating an amount of medication (evaluation) are all examples of such concepts.
The claimed elements further are directed to a mathematical concept such as relationships, formulas, equations, and calculations. Calculating an amount of medication based on the received data is categorically an example of a mathematical concept.
Accordingly, the claims recite abstract ideas.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional element – claim 1 recites a “display device,” a processor in electronic communication with the display, a memory communicatively coupled with the processor and storing instructions, generating a user interface through the display device, receiving input at the interface, and wherein the data is received via a biological sensor, while claim 10 recites similar elements, with the addition of a wearable medical device comprising a reservoir storing medication, and an injector to administer the medication, as well as a computing device in communication with the wearable medical device.
Examiner finds that the “communications” between the devices as claimed, as well as the display generation/input are examples of adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). As per the processors, the displays themselves, as well as the memory elements, Examiner finds recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). With regard to the wearable medical device comprising a liquid reservoir/pump, Examiner specifically notes this to be a general link of the judicial exception(s) to a particular technological environment/field of use – i.e. medication dispensation. (See MPEP 2106.05h). Examiner again finds no improvement to the functioning of the device itself, nor to any other related technology or technical field (see MPEP 2106.05a). These additional elements are found to amount to no more than mere instructions to implement or apply the exception using the identified elements.
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: claim 1 recites a “display device,” a processor in electronic communication with the display, a memory communicatively coupled with the processor and storing instructions, generating a user interface through the display device, receiving input at the interface, and wherein the data is received via a biological sensor, while claim 10 recites similar elements, with the addition of a wearable medical device comprising a reservoir storing medication, and an injector to administer the medication, as well as a computing device in communication with the wearable medical device. When considered individually, the identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0020] Still referring to FIG. 1, in some embodiments, the apparatus 100 may be configured to receive biological data 132 from biological sensor 128. Apparatus 100 and/or processor 104 may be in electronic communication with biological sensor 128. “Electronic communication” as used in this disclosure is a form of connection between two objects where data is transferred. Electronic communication between biological sensor 128 and apparatus 100 may include, but is not limited to, wired, wireless, and/or other connections. A “biological sensor” as used in this disclosure is a device that detects biological data. Biological sensor 128 may include, without limitation, heart rate monitors, blood pressure sensors, blood oxygen sensors, thermometers, blood glucose monitors, continuous blood glucose monitors (CGM), ketone sensors, blood alcohol sensors and the like. Blood glucose monitor and continuous blood glucose monitors (CGM) may also be referred to as blood glucose meters. Biological sensor 128 may detect and/or generate biological data 132.
[0037]… A wearable injection device may include a liquid reservoir, a drug reservoir, a biological sensor, a pump, and a needle and/or cannula injector, without limitation. An injector may include, without limitation, a needle, cannula, syringe, and/or other piercing element in mechanical connection with a spring, pump, and/or other moving element. The apparatus 100 may communicate the amount of medication 136 to a wearable injection device, to which the wearable injection device may administer the amount of medication 136 to a user through an injector. In some embodiments, the apparatus 100 and/or the processor 104 may communicate instructions related to the selected activity mode 124 to a wearable medical device. Instructions may include administering of medication, raising or lowering of blood glucose threshold or target values, raising or lowering of insulin-on-board threshold or target values, and/or any other operation related to the selected activity mode 124 as described throughout this disclosure.
[0064] In an example, the wearable injection device 802 may include a processor 814. The processor 814 may be implemented in hardware, software, or any combination thereof. The processor 814 may, for example, be a microprocessor, a logic circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC) or a microprocessor coupled to a memory. The processor 814 may maintain a date and time as well as be operable to perform other functions (e.g., calculations or the like). The processor 814 may be operable to execute a control application 826 and a voice control application 888 stored in the memory 812 that enables the processor 814 to direct operation of the wearable drug delivery device 802.
[0065] Still referring to FIG. 8, the input/output device(s) 845 may one or more of a microphone, a speaker, a vibration device, a display, a push button, a touchscreen display, a tactile input surface, or the like. The input/output device(s) 845 may be coupled to the processor 814 and may include circuitry operable to generate signals based on received inputs and provide the generated signals to the processor 814. In addition, the input/output device(s) 845 may be operable to receive signals from the processor 814 and, based on the received signals, generate outputs via a respective output device.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The wearable device(s) is/are also described in functional terms only, rather than providing any sort of improvement therein in the claim language. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-9, 11-19:
Dependent claims 2-8, 11-18, are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as elements of the mathematical calculations to determine an activity/dose, description as to the calculations themselves, description of the medication to be calculated, and description of the type(s) of modes considered. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Dependent claims 9, 20, 19 are not directed to any additional abstract ideas, but do recite additional non-abstract elements. Claims 9, 20 recite specific examples of the types of sensors being glucose and blood alcohol, while claim 19 provides for actual dispensation of insulin. Examiner finds that the specific types of sensors are insufficient to amount to either a practical application nor significantly more, as they are at best applied to the abstract idea(s) rather than providing some form of improvement or other meaningful limitation. Examiner further refers to the portions of the specification cited above with regard to the sensors in the independent claims in support of the position – it is clear the invention relies upon the existing technology/capabilities therein. With regard to the insulin dispensation in claim 19, in the interest of compact prosecution Examiner further notes this is not sufficient to amount to a practical application or significantly more. Examiner again makes reference to the portions of the specification cited above with regard to the wearable medical device in support of the position that the actual dispensing is at best a general link between the technology and the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vleugels (US 20220378659 A1) in view Constantin et al (US 20190246914 A1, hereinafter Constantin).
In reference to claim 1:
Vleugels teaches: An apparatus for glycemic control of a user (at least [080] “The patient management system can be used in studies of the impact of real-time bolus reminders on medication adherence and glycemic control.”), comprising:
a display device (at least [fig 2, 3, and related text] “Electronic device 218 may for example be a wearable device 321 that is worn around the wrist, arm or finger. Electronic device 218 may also be implemented as a wearable patch that may be attached to the body or may be embedded in clothing. Electronic device 218 may also be a module or add-on device that can for example be attached to another wearable device, to jewelry, or to clothing. Electronic device 219 may for example be a mobile device 322 such as a mobile phone, a tablet or a smart watch.” See also [082] app operating on an interface on a mobile device);
a processor in electronic communication with the display device (at least [fig 2, 3, and related text] electronic device 218) ; and
a memory communicatively connected to the processor, the memory containing instructions configuring the processor (at least [fig 2, 6 and related text] “Electronic device 218 typically includes, in part, one or more sensor units 627, a processing unit 628, memory 629 that can include or be realized as computer-readable storage media having program code instructions, a clock or crystal 630, radio circuitry 634, and a power management unit (“PMU”) 631.”) to:
generate a user interface through the display device, wherein the user interface is configured to receive user input (at least [081, 0118, 0267] “One part of the patient management system is the app, which might provide a user interface to be used by the patient. “);
receive biological data of the user from a biological sensor in communication with the processor (at least [0092-93] “For example, the insulin delivery system may use current or prior glucose level readings, fluctuations in glucose level readings, parameters derived from glucose level readings or insulin-on-board (i.e., the insulin that was administered at an earlier time but is still active in the patient's body)…” The insulin delivery system may also include parameters related to the meal activity itself, such as the duration of the meal, the pace of eating, the amounts consumed. The insulin delivery system may also use other sensor inputs such as heart rate, blood pressure, body temperature, hydration level, fatigue level, etc. and can obtain these from its own sensors or obtain some of them from other devices the patient might be using for this or for other purposes.”); and
select an activity mode of a plurality of activity modes of a wearable injection device based on at least one of the user input and the biological data, the activity mode indicative of a temporary condition affecting a blood glucose level of the user (at least [091] “The detection of an actual, probable or imminent start of a food intake event as described herein can be used to inform an insulin delivery system. Upon receiving a signal indicating an actual, probable or imminent start of a food intake event, the insulin delivery system may calculate or estimate the adequate dosage of insulin to be administered and the schedule for delivery of the insulin.” At [0113-0117] “In one example, the food intake detection system 101 may monitor the outputs of accelerometer and/or gyroscope sensors to detect a possible bite gesture or a possible sip gesture.” See also [0125-0127] for discussion of food intake event);
modify a set of operational parameters for calculating medication dosages based on the selected activity mode (at least [071] “For example, the wearable device might determine that the patient has started eating and from the pace of eating and a determined likely duration of the event, could signal to a dosing and delivery device some information about the eating event, which the delivery device could use to start a delivery of insulin to the patient. In addition, or instead, the wearable device could send a message relating to the eating event and the parameters measured.” See also [091-097] for discussion of delivery parameters for the insulin device);
calculate an amount of medication to deliver to the user based on the [collected information] (at least [091-097] “The insulin delivery system may use other parameters and inputs in calculating or estimating the dosing and frequency. For example, the insulin delivery system may use current or prior glucose level readings, fluctuations in glucose level readings, parameters derived from glucose level readings or insulin-on-board (i.e., the insulin that was administered at an earlier time but is still active in the patient's body)…Additional parameters related to the food intake event can also be used to inform an insulin delivery system. An insulin delivery system may use such parameters to calculate or estimate an adequate dosage of insulin to be delivered and/or the schedule for the insulin delivery. Such parameters may include, but are not limited to duration of eating or drinking, amounts of food or drinks consumed, pace of eating, amount of carbohydrates consumed, eating method or type of utensils or containers used. Some of these additional parameters (e.g., duration or pace) may be computed by the food intake tracking and feedback system without requiring any user intervention. In other cases, a user intervention, input or confirmation by the user may be necessary.”) Vleugals as cited teaches all the limitations above, but does not specifically disclose an “aggressiveness category” being assigned or considered in the medication dosing. Constantin however does teach:
select an activity mode of a plurality of activity modes of a wearable injection device based on at least one of the user input and the biological data, the activity mode indicative of a temporary condition affecting a blood glucose level of the user (at least [figs 5a/5b and related text] “The state may be determined by applying the real-time datum to the patient physiology model and the behavioral model. The patient physiology model may, for example, be a state model that includes one or more of a glucose concentration state, an insulin-on-board state, an insulin sensitivity state, an energy absorption state, or an energy exertion state. A particular state may be determined by features of available input (e.g. time of day, glucose level, prior activity.)” at [0507] “Where user-entered data includes exercise, systems and methods according to present principles may allow users to save specific workouts or events to allow patterns in post workout glycemic fluctuations to be analyzed and potentially found, resulting in appropriate automatic or manual therapy modifications to be made by the decision-support application/functionality.”)
categorize the selected activity mode into an aggressiveness category (at least [0404, 0441-0442] “Exercise can affect blood sugar level quickly (e.g., almost immediately), and for up to 48 hours after exercise is completed. A decision support system may provide recommendations on what to do prior to exercising, during exercise, or after exercise. For example, a decision support system may provide guidance with respect to treatment parameters to account for the effect of exercise. A decision support system may also retrospectively learn and improve exercise recommendations. In an example, the system may receive from a user (e.g., patient) an exercise plan that may include the type of exercise that will be performed, the intensity of exercise, and the duration of exercise, and the system may develop and deliver guidance based on the received exercise plan….at [0507] “ For example, users may save workouts such as “Cardio 1”, “Cardio 2”, “Get Swoll 1”, “Get Swoll 2”, and so on. Other gradations of exercise may include those pertaining to intensity, e.g., light, medium, hard, duration, e.g., 5 min., 10 min., 15 min., 20 min., 25 min., 30 min., one hour, and so on.”) – i.e. intensity level is the “aggressiveness” category);
modify a set of operational parameters of a medication delivery algorithm for calculating medication dosages based on the aggressiveness category of the selected activity mode (at least [0492] “[0492] The identification of the correlative parameter and the lifestyle/situational parameter, as well as subsequent processing steps, can be performed in a number of ways. For example, the steps can include factors from various locations, including social networks, user-entered data, sensor data, data from user devices, population or big data, and so on. The processing and identification may include Bayesian analysis or the like, e.g., to identify strong connectors which are parameters or variables that bear a strong relationship to each other. Where multiple parameters are employed, the same may include multiple parameters relating to the same event, e.g., both intensity and duration of exercise. The processing and identification may include multiple steps, e.g., a first step of processing “internal data” for that patient, and a subsequent step of performing some processing in the cloud, with “big” data, e.g., using prepackaged subroutines and case-based reasoning. For example, case-based reasoning may be employed to determine what happened to the user in other like situations, or what happened to other like users in other like situations.” At [0499] “ Where multiple types of data are employed as inputs in a given algorithm, the multiple types may be related, e.g., may be a type, timing, and intensity of a variable, e.g., illness, exercise, meals, sleep, and so on, e.g., a duration and intensity of exercise.”); and
calculate an amount of medication to deliver to the user based on the modified set of operational parameters (at least [0404] “ For example, the system may track exercise intensity and duration, glucose levels, and insulin dosages, and learn how exercise impacts glucose sensitivity. A determined glucose sensitivity may be provided as guidance or may be used to determine other states or as an input to an algorithm to determine guidance.” At [0425-0426] calculating dosing rates based on algorithm). Vleugals and Constantin are analogous references as both disclose managing insulin dosing based on collected or input data regarding a user’s behavior. One of ordinary skill in the art at the time of the invention would have found the detected intensity data as taught by Constantin obvious to include in the dosing of Vluegals, as both references disclose the importance of considering activities of a user in order to appropriately modify or track insulin dosing. Further, Constantin teaches that intensity of exercise can affect the production of one or more hormones that can affect insulin sensitivity: “For example, the system may track exercise intensity and duration, glucose levels, and insulin dosages, and learn how exercise impacts glucose sensitivity. A determined glucose sensitivity may be provided as guidance or may be used to determine other states or as an input to an algorithm to determine guidance.” (see 0404). As such, consideration of these types of exercise events would have been an obvious inclusion in the dosing of insulin via a pump (see 0404).
In reference to claim 2, 11:
Vleugels further teaches wherein the processor is further configured to:
correlate the biological data with an activity mode of the plurality of activity modes (at least [0116-0119] using the sensor data and the habit/prior consumption data, a correlation is made between detected and predicted events); and
prompt the user, through the user interface, to select the correlated activity mode (at least [0116] “ For example, training data obtained from the user and/or from other users at an earlier time may be used to train a classifier. Training data may be obtained by asking for user confirmation when a possible bite or sip gesture has been detected.” – i.e. a food/drinking event occurs).
In reference to claim 3, 12:
Vleugels further teaches wherein the processor is further configured to:
receive training data correlating biological data to activity modes (at least [0116] “The food intake event detection system may use machine learning or other data analytics techniques to improve the accuracy and reliability of its detection capabilities. For example, training data obtained from the user and/or from other users at an earlier time may be used to train a classifier. Training data may be obtained by asking for user confirmation when a possible bite or sip gesture has been detected. A labeled data record can then be created and stored in memory readable by the gesture processor that includes the features related to the gesture, along with other contextual features, such as time of day or location.”);
train an activity mode machine learning model with the training data, wherein the activity mode machine learning model is configured to input biological data and output an activity mode (at least [0116] “A classifier can then be trained on a labeled dataset comprised of multiple labeled data records set of labeled data records….”); and
determine an activity mode based on the output of the activity mode machine learning model (at least [0116] “and the trained classifier model can then be used in a food intake event detection system to more accurately detect the start of a food intake event”).
In reference to claim 4, 13:
Vleugels further teaches: wherein the processor is further configured to:
receive data from an external computing device of a plurality of biological data associated with a plurality of activity modes (at least [0122-123] “A variety of sensors may be used for such monitoring. The monitored signals may be generated by the dietary tracking and feedback system. For example, over a period of time, food intake events and characteristics related to those food intake events are recorded, such as eating pace, quantity of food consumption, food content, etc., while also tracking other parameters that are not directly, or perhaps not obviously, linked to the food intake event. This could be, for example, location information, time of day a person wakes up, stress level, certain patterns in a person's sleeping behavior, calendar event details including time,…” - i.e. patterns from the sensor events;
calculate an average selected activity mode based on the plurality of biological data (at least [0172, 0117-0119] stress and assorted pattern data are used to determine the habits of when a user eats, sleeps, and reacts) ; and
select an activity mode for the user based on the average selected activity mode (at least [117-0119] “As a result, the system can process correlations in the historical data to predict the time or time window that the user is most likely to have breakfast based on the current day of week and at what time the user woke up. Other trigger signals or trigger events may also be used by the non-real-time analysis and learning subsystem 105 to predict the time that a user will eat breakfast.”).
In reference to claim 5, 14:
Vleugels further teaches: wherein the processor is further configured to:
receive an acuteness factor through the user interface (at least [0418, 0500] “For example, if the measurement sensor unit 1904 is a CGM unit, the measurement sensor processing unit 1909 may include logic that analyzes signals received from the measurement sensor unit 1904 and looks for certain patterns, such as a rise in CGM readings, or a change in first or higher order derivative of CGM readings to determine if a food intake event is likely occurring, optionally with an associated probability or confidence level. “); and
modify the activity mode based on the acuteness factor (at least 0418, 0500] the determination/dosage is modified based on the severity of the CGM reading).
In reference to claim 6:
Vleugels further teaches: wherein the processor is further configured to:
determine a pattern of the user based the biological data (at least [0117-0119] patterns in the user’s activities, including sleep, monitoring, eating, stress); and
present an activity mode to the user through the user interface based on the pattern (at least [0116, 0135-013] “For example, when medication needs to be taken at certain times of day, a medication adherence system can monitor the time and, when it is time to take medication, issue an alert. It may then also activate the event detection subsystem (if it is not yet already active) and start monitoring the outputs of the event detection subsystem. It waits for a notification confirmation from user that he/she has taken the medication.” – when a predicted event is detected, an interface populates and waits for confirmation).
In reference to claim 7, 16:
Vleugels further teaches: wherein the processor is further configured to:
generate a list of activity modes (at least [096, 0116, 0138-0141, 0399, 0529] interfaces generated to confirm an activity as detected or predicted); and
present the list of activity modes to the user through the user interface (at least [096, 0116] user confirmation of the detected event/activity, at ]0138-141] user confirmation of the medication activity and/or in combination with a detected eating/drinking event, see also [0399, 0524, fig 19 and related text] for additional examples of confirmation of event/activity.
In reference to claim 8, 19:
Vleugels further teaches: wherein the amount of medication includes insulin (at least [071-077] insulin dispensed/dosed, including “whether and how much insulin”).
In reference to claim 9, 20:
Vleugels further teaches: wherein the biological sensor includes a blood glucose meter and a blood alcohol sensor (at least [070] CGM worn by patient, at [0368] “Cross-correlated analytics sub-system 1604 may combine the predicted drinking gestures with other sensor data to predict more accurately if someone is consuming a drink that contains alcohol and estimate the amount of alcohol consumed. Examples of other sensor data may include but are not limited to measuring hand vibration, heart rate, voice analysis, skin temperature, measuring blood, breath chemistry or body chemistry.”)
In reference to claim 10:
Vleugels teaches A system for glycemic control (at least [080] “The patient management system can be used in studies of the impact of real-time bolus reminders on medication adherence and glycemic control.”), comprising:
a biological sensor configured to generate biological data (at least [0410, 508-509] sensor maybe CGM and/or sensor units 2302);
a wearable medical device comprising: a liquid reservoir, wherein the liquid reservoir stores medication (at least [070] “The insulin could be given in the form of injectable insulin using for example a pen or syringe, or via an insulin pump or a similar insulin dosing and dispensing device that is attached to the patient's body to introduce measured amounts of insulin into the patient's body.“ see also [0410, 508-509]); and
an injector, wherein the injector is configured to administer the medication from the liquid reservoir to a user (at least [070] “The insulin could be given in the form of injectable insulin using for example a pen or syringe, or via an insulin pump or a similar insulin dosing and dispensing device that is attached to the patient's body to introduce measured amounts of insulin into the patient's body.“ see also [0410, 508-509], see also [0411] “For example, medication dispensing unit 1908 might be an embedded hardware system that injects doses or micro-doses of medication in response to instructions given by a software system and thus would only require minimal processing on-board. It is also possible that medication dosing calculation unit 1906 and medication dispensing unit 1908 are housed in the same hardware. “) ; and
a computing device in electronic communication with the wearable medical device, (at least [fig 19, 3 and related text] “ For example, medication dispensing unit 1908 might be an embedded hardware system that injects doses or micro-doses of medication in response to instructions given by a software system and thus would only require minimal processing on-board. It is also possible that medication dosing calculation unit 1906 and medication dispensing unit 1908 are housed in the same hardware. Certain elements of FIG. 19 may be distributed across multiple software and or hardware elements. As an example, a portion of medication dosing calculation unit 1906 may be implemented on one electronic device that is separate from the electronic device that houses medication dispensing unit 1908, whereas another portion of medication dosing calculation unit 1906 may be embedded within the same housing as medication dispensing unit 1908.”) wherein the computing device is configured to:
receive the biological data from the biological sensor (at least [0092-93] “For example, the insulin delivery system may use current or prior glucose level readings, fluctuations in glucose level readings, parameters derived from glucose level readings or insulin-on-board (i.e., the insulin that was administered at an earlier time but is still active in the patient's body)…” The insulin delivery system may also include parameters related to the meal activity itself, such as the duration of the meal, the pace of eating, the amounts consumed. The insulin delivery system may also use other sensor inputs such as heart rate, blood pressure, body temperature, hydration level, fatigue level, etc. and can obtain these from its own sensors or obtain some of them from other devices the patient might be using for this or for other purposes.”);
generate a user interface through a display device in electronic communication with the computing device (at least [081, 096, 0118, 0267] “One part of the patient management system is the app, which might provide a user interface to be used by the patient.“);
receive user input from the user interface (at least [081, 096, 0118, 0267] “One part of the patient management system is the app, which might provide a user interface to be used by the patient”; “Those records might be determined by the system, possibly with feedback from the user about their accuracy or those records might be determined by the user and input via a user interface of the system;”)
select an activity mode of a plurality of activity modes of the wearable medical device based on at least one of the biological data and the user input, the activity mode indicative of a temporary condition affecting blood glucose levels of the user (at least [091] “The detection of an actual, probable or imminent start of a food intake event as described herein can be used to inform an insulin delivery system. Upon receiving a signal indicating an actual, probable or imminent start of a food intake event, the insulin delivery system may calculate or estimate the adequate dosage of insulin to be administered and the schedule for delivery of the insulin.” At [0113-0117] “In one example, the food intake detection system 101 may monitor the outputs of accelerometer and/or gyroscope sensors to detect a possible bite gesture or a possible sip gesture.” See also [0125-0127] for discussion of food intake event);
modify a set of operational parameters for calculating medication dosages based on the selected activity mode at least [071] “For example, the wearable device might determine that the patient has started eating and from the pace of eating and a determined likely duration of the event, could signal to a dosing and delivery device some information about the eating event, which the delivery device could use to start a delivery of insulin to the patient. In addition, or instead, the wearable device could send a message relating to the eating event and the parameters measured.” See also [091-097] for discussion of delivery parameters for the insulin device); and
communicate the selected activity mode with the wearable medical device, wherein the wearable medical device administers the medication to the user based on the selected activity mode (at least [091-097] “The insulin delivery system may use other parameters and inputs in calculating or estimating the dosing and frequency. For example, the insulin delivery system may use current or prior glucose level readings, fluctuations in glucose level readings, parameters derived from glucose level readings or insulin-on-board (i.e., the insulin that was administered at an earlier time but is still active in the patient's body)…Additional parameters related to the food intake event can also be used to inform an insulin delivery system. An insulin delivery system may use such parameters to calculate or estimate an adequate dosage of insulin to be delivered and/or the schedule for the insulin delivery. Such parameters may include, but are not limited to duration of eating or drinking, amounts of food or drinks consumed, pace of eating, amount of carbohydrates consumed, eating method or type of utensils or containers used. Some of these additional parameters (e.g., duration or pace) may be computed by the food intake tracking and feedback system without requiring any user intervention. In other cases, a user intervention, input or confirmation by the user may be necessary.” At [0410] “In one embodiment of the current disclosure, the medication to be administered is insulin, the measurement sensor unit 1904 is a continuous glucose monitor sensor that measures interstitial glucose levels, medication dispensing unit 1908 is an insulin pump and medication dosing calculation unit 1906 is the insulin dosage computation unit of an automated insulin delivery system (a.k.a., artificial pancreas).” See also [0508, 0509] for more discussion about pump).
Vleugals as cited teaches all the limitations above, but does not specifically disclose an “aggressiveness category” being assigned or considered in the medication dosing. Constantin however does teach:
select an activity mode of a plurality of activity modes of a wearable injection device based on at least one of the user input and the biological data, the activity mode indicative of a temporary condition affecting a blood glucose level of the user (at least [figs 5a/5b and related text] “The state may be determined by applying the real-time datum to the patient physiology model and the behavioral model. The patient physiology model may, for example, be a state model that includes one or more of a glucose concentration state, an insulin-on-board state, an insulin sensitivity state, an energy absorption state, or an energy exertion state. A particular state may be determined by features of available input (e.g. time of day, glucose level, prior activity.)” at [0507] “Where user-entered data includes exercise, systems and methods according to present principles may allow users to save specific workouts or events to allow patterns in post workout glycemic fluctuations to be analyzed and potentially found, resulting in appropriate automatic or manual therapy modifications to be made by the decision-support application/functionality.”)
categorize the selected activity mode into an aggressiveness category (at least [0404, 0441-0442] “Exercise can affect blood sugar level quickly (e.g., almost immediately), and for up to 48 hours after exercise is completed. A decision support system may provide recommendations on what to do prior to exercising, during exercise, or after exercise. For example, a decision support system may provide guidance with respect to treatment parameters to account for the effect of exercise. A decision support system may also retrospectively learn and improve exercise recommendations. In an example, the system may receive from a user (e.g., patient) an exercise plan that may include the type of exercise that will be performed, the intensity of exercise, and the duration of exercise, and the system may develop and deliver guidance based on the received exercise plan….at [0507] “ For example, users may save workouts such as “Cardio 1”, “Cardio 2”, “Get Swoll 1”, “Get Swoll 2”, and so on. Other gradations of exercise may include those pertaining to intensity, e.g., light, medium, hard, duration, e.g., 5 min., 10 min., 15 min., 20 min., 25 min., 30 min., one hour, and so on.”) – i.e. intensity level is the “aggressiveness” category);
modify a set of operational parameters of a medication delivery algorithm for calculating medication dosages based on the aggressiveness category of the selected activity mode (at least [0492] “[0492] The identification of the correlative parameter and the lifestyle/situational parameter, as well as subsequent processing steps, can be performed in a number of ways. For example, the steps can include factors from various locations, including social networks, user-entered data, sensor data, data from user devices, population or big data, and so on. The processing and identification may include Bayesian analysis or the like, e.g., to identify strong connectors which are parameters or variables that bear a strong relationship to each other. Where multiple parameters are employed, the same may include multiple parameters relating to the same event, e.g., both intensity and duration of exercise. The processing and identification may include multiple steps, e.g., a first step of processing “internal data” for that patient, and a subsequent step of performing some processing in the cloud, with “big” data, e.g., using prepackaged subroutines and case-based reasoning. For example, case-based reasoning may be employed to determine what happened to the user in other like situations, or what happened to other like users in other like situations.” At [0499] “ Where multiple types of data are employed as inputs in a given algorithm, the multiple types may be related, e.g., may be a type, timing, and intensity of a variable, e.g., illness, exercise, meals, sleep, and so on, e.g., a duration and intensity of exercise.”); and
wherein the wearable medical device administers the medication to the user based on the modified set of operational parameters of the selected activity mode (at least [0404] “ For example, the system may track exercise intensity and duration, glucose levels, and insulin dosages, and learn how exercise impacts glucose sensitivity. A determined glucose sensitivity may be provided as guidance or may be used to determine other states or as an input to an algorithm to determine guidance.” At [0425-0426] calculating dosing rates based on algorithm). Vleugals and Constantin are analogous references as both disclose managing insulin dosing based on collected or input data regarding a user’s behavior. One of ordinary skill in the art at the time of the invention would have found the detected intensity data as taught by Constantin obvious to include in the dosing of Vluegals, as both references disclose the importance of considering activities of a user in order to appropriately modify or track insulin dosing. Further, Constantin teaches that intensity of exercise can affect the production of one or more hormones that can affect insulin sensitivity: “For example, the system may track exercise intensity and duration, glucose levels, and insulin dosages, and learn how exercise impacts glucose sensitivity. A determined glucose sensitivity may be provided as guidance or may be used to determine other states or as an input to an algorithm to determine guidance.” (see 0404). As such, consideration of these types of exercise events would have been an obvious inclusion in the dosing of insulin via a pump (see 0404).
In reference to claim 15:
Vleugels further teaches: wherein the computing device is further configured to:
determine an activity pattern of the user based on the selected activity mode and biological data (at least [0117-0119] patterns in the user’s activities, including sleep, monitoring, eating, stress, as well as sensor obtained data); and
present an activity mode to the user through the user interface based on the activity pattern (at least [0116, 0135-013] “For example, when medication needs to be taken at certain times of day, a medication adherence system can monitor the time and, when it is time to take medication, issue an alert. It may then also activate the event detection subsystem (if it is not yet already active) and start monitoring the outputs of the event detection subsystem. It waits for a notification confirmation from user that he/she has taken the medication.” – when a predicted event is detected, an interface populates and waits for confirmation).
In reference to claim 17:
Vleugels further teaches: wherein the activity mode includes a drinking mode, which presumes the user consuming one or more alcoholic beverages (at least [0130] drinking event, at [0368] “Cross-correlated analytics sub-system 1604 may combine the predicted drinking gestures with other sensor data to predict more accurately if someone is consuming a drink that contains alcohol and estimate the amount of alcohol consumed.”).
In reference to claim 18:
Vleugels further teaches: wherein the drinking mode includes a hardness factor of the one or more alcoholic beverages (at least [0130] drinking events, and at [0368] amount of alcohol consumed would require hardness of consumed beverage in conjunction with food item cameras at [0154-0155].)
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220133226 A1 to Nothacker et al discloses an alcohol consumption management system.
US 20200058386 A1 to Kelly describes a dynamic insulin pump monitoring/dispensing system.
Response to Arguments
Applicant’s remarks as filed on 7 APRIL 2026 have been fully considered.
Applicant begins with a discussion of the 101 rejection on page 7 of the remarks. Applicant’s remarks regarding a mental process are not found persuasive, as the sensor itself is addressed in subsequent steps of the analysis, as the communication between the sensor and any other machine. Similarly, the sensors and additional elements with regard to the activity mode selection are also more appropriately analyzed with the additional elements as identified above. The selection of an activity is certainly a mental process, with the sensor data itself being received for the decision. As per the algorithm/modification of the parameters, the particular limitation does not actually require computation or application of an algorithm.
Applicant’s discussion of the mathematical category on page 8 is noted. Examiner respectfully submits the claim as a whole is directed to preparing and executing the calculation itself.
Applicant turns to a discussion of step 2A prong II, whether a practical application is present, on page 9, with various elements of USPTO policy. Applicant generally refers/recites exemplary claim 1 on page 9/10, however Examiner does not find this persuasive as the conclusion presented (i.e. no monopoly present) is not itself the sole test of eligibility.
Applicant turns to a discussion of the presence of an improvement on page 10. Examiner agrees that managing glycemic control/insulin dosing is an important challenge for diabetic patients, however, glycemic control itself is not a technical problem or field. Applicant has not improved the algorithm itself, and dosing of insulin (while important) is not in and of itself a technical problem for the purposes of MPEP 2106.05a. Again, Applicant’s examples on page 11 provide certainly a description of an improvement embodied in the technology itself, however, again, dosing insulin for high intensity exercise is not technical for the purposes of analysis. Applicant’s remarks regarding McRO are not found to be persuasive, as claim 1 does not automate a task previously only performable by a human. Applicant’s claims do not improve the functioning of the computer, or any other technical realm.
Applicant’s remarks regarding MPEP 2106.05f on page 12 begin with a selection of the section itself. Applicant reproduces claim 1 in support of a position that the claims do not recite a solution or outcome, Examiner respectfully disagrees, as the solution/outcome is the updated dosing. Examiner respectfully disagrees that categorizing an activity as aggressive or not should be considered as an additional element, and instead is more suitably analyzed in the abstract idea. Similarly, the computer itself is used to execute the algorithm once the parameter(s) have been updated. The claim itself clearly relies on the existence of the known closed loop insulin delivery system (including the process, biological sensor, and injection device). As per point 3, Examiner does not find “glycemic control” to be a particular application of the judicial exception as claimed. Applicant again recites elements of exemplary claim 1, however this again lends to the technical/additional elements being merely “applied” to the abstract idea(s) themselves.
Applicant turns to MPEP 2106.05h on page 14. Examiner does not dispute that the elements in claim 1 are integral to the claimed solution, only that the technical/additional elements as identified (sending and receiving data) are generally considered insignificant extra solution activity. Examiner is unclear how the activity mode selection is considered here, and the parameter modification etc., as these have been discussed with respect to the abstract idea. Examiner notes not every additional element is required to be considered under the same factors/sections.
Applicant’s remarks regarding MPEP 2106.05e are noted, beginning on page 15, however, they are not found persuasive as to the other meaningful limitations as discussed in MPEP 2106.05e. Examiner notes preemption or lack thereof is not the sole indicator of eligibility.
Applicant turns to step 2B on page 16. Applicant essentially restates exemplary claim 1 and concludes eligibility, however, it is unclear what if any additional elements or in combination are believed to constitute significantly more.
Applicant’s remarks regarding the prior art begin on page 11. Examiner finds these remarks largely directed to the newly amended limitations, and as such are found to be moot/unpersuasive in view of the new grounds of rejection above. Examiner notes that exemplary claim 1 allows for the selected activity mode to be either collected via user input, or biological sensor data (i.e. with no user input whatsoever). Applicant’s remaining remarks are found moot/unpersuasive in view of the updated rejection of claims 1, 10.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
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