DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-7 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 5-6 are directed to a method (i.e., a process) and claims 1-4 and 7 are directed to a system (i.e., a machine). Accordingly, claims 1-7 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Independent claims 1, 4, 5, and 7 include limitations that recite at least one abstract idea. Specifically, independent claims 1, 4, 5, and 7 recite:
1. A computing system for providing a titration dose guidance recommendation for a query subject to treat diabetes mellitus, wherein the system comprises one or more processors and a memory, the memory comprising: A) instructions for executing by the one or more processors: - a for the subject representative prediction algorithm adapted to calculate the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject as a function of the size of an injected dose of insulin, the prediction algorithm being adaptive in response to obtained FBG and corresponding insulin dose size data for the subject, - a probability algorithm adapted to, based on predicted mean and variance FBG values and a pre-defined FBG target range, calculate as a function of insulin dose size the probability of hypoglycaemia respectively hyperglycaemia for the subject, and - a policy algorithm adapted to, based on calculated hypoglycaemia respectively hyperglycaemia probabilities, calculate the probability for a corresponding policy target as a function of insulin dose size, B) instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR), the method comprising:- obtaining for the subject an FBG target to be used as the pre-defined FBG target range for the probability algorithm, - obtaining from the subject an update data set comprising a most recent FBG value and a corresponding insulin dose size, - using the prediction algorithm: calculating for the subject, based on the update data sat, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the probability algorithm: calculating as a function of insulin dose size the probability of hypoglycaemia respectively hyperglycaemia for the subject, - using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation.
4. A computing system for providing a titration dose guidance recommendation for a query subject to treat diabetes mellitus, wherein the system comprises one or more processors and a memory, the memory comprising: A) instructions for executing by the one or more processors: - a for the subject representative prediction algorithm adapted to calculate the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject as a function of the size of an injected dose of insulin, the prediction algorithm being adaptive in response to obtained FBG and corresponding insulin dose size data for the subject, - a policy algorithm adapted to, based on predicted mean and variance FBG values, calculate the probability for a corresponding policy target as a function of insulin dose size, B) instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR), the method comprising: - obtaining for the subject an FBG target to be used as the policy target for the probability algorithm, - obtaining from the subject an update data set comprising a most recent FBG value and a corresponding insulin dose size, - using the prediction algorithm: calculating for the subject, based on the update data sat, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation.
5. A method for providing a titration dose guidance recommendation for a query subject to treat diabetes mellitus, the method comprising the steps of: - obtaining for the subject an FBG target to be used as a pre-defined FBG target range for a probability algorithm, - obtaining from the subject an update data set comprising a most recent FBG value and a corresponding insulin dose size, - using a for the subject representative adaptive prediction algorithm: calculating for the subject, based on the update data set, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using a probability algorithm: calculating as a function of insulin dose size the probability of hypoglycaemia respectively hyperglycaemia for the subject based on the predicted mean and variance FBG values and the pre-defined FBG target range, - using a policy algorithm: calculating, based on the calculated hypoglycaemia respectively hyperglycaemia probabilities, the probability for a corresponding policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing a dose size recommendation.
7. A computing system for providing a titration dose guidance recommendation for a query subject to treat diabetes mellitus, wherein the system comprises one or more processors and a memory, the memory comprising: A) instructions for executing by the one or more processors: - a for the subject representative prediction algorithm adapted to calculate the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject as a function of the size of an injected dose of insulin, the prediction algorithm being adaptive in response to obtained FBG and corresponding insulin dose size data for the subject, - a probability algorithm adapted to, based on predicted mean and variance FBG values and a hypoglycaemia target value, calculate as a function of insulin dose size the probability of hypoglycaemia for the subject, B) instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR), the method comprising: - obtaining from the subject an update data set comprising a most recent FBG value and a suggested insulin dose size, - obtaining for the subject a threshold acceptable probability for hypoglycaemia, - using the prediction algorithm: calculating for the subject, based on the update data set, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the probability algorithm: calculating for the suggested insulin dose size the probability of hypoglycaemia for the subject,- determining whether the suggested insulin dose size is above or below the threshold acceptable probability, and - communicating to the subject whether the suggested insulin dose size is acceptable or should be lowered.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because a for the subject representative prediction algorithm adapted to calculate the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject as a function of the size of an injected dose of insulin, the prediction algorithm being adaptive in response to obtained FBG and corresponding insulin dose size data for the subject, - a probability algorithm adapted to, based on predicted mean and variance FBG values and a pre-defined FBG target range, calculate as a function of insulin dose size the probability of hypoglycaemia respectively hyperglycaemia for the subject, and - a policy algorithm adapted to, based on calculated hypoglycaemia respectively hyperglycaemia probabilities, calculate the probability for a corresponding policy target as a function of insulin dose size, perform a method responsive to receiving a dose guidance request (DGR), the method comprising:- obtaining for the subject an FBG target to be used as the pre-defined FBG target range for the probability algorithm, - obtaining from the subject an update data set comprising a most recent FBG value and a corresponding insulin dose size, - using the prediction algorithm: calculating for the subject, based on the update data sat, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the probability algorithm: calculating as a function of insulin dose size the probability of hypoglycaemia respectively hyperglycaemia for the subject, - using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation; - a policy algorithm adapted to, based on predicted mean and variance FBG values, calculate the probability for a corresponding policy target as a function of insulin dose size; obtaining for the subject an FBG target to be used as the policy target for the probability algorithm, - obtaining from the subject an update data set comprising a most recent FBG value and a corresponding insulin dose size, - using the prediction algorithm: calculating for the subject, based on the update data sat, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation; - a probability algorithm adapted to, based on predicted mean and variance FBG values and a hypoglycaemia target value, calculate as a function of insulin dose size the probability of hypoglycaemia for the subject, perform a method responsive to receiving a dose guidance request (DGR), the method comprising: - obtaining from the subject an update data set comprising a most recent FBG value and a suggested insulin dose size, - obtaining for the subject a threshold acceptable probability for hypoglycaemia, - using the prediction algorithm: calculating for the subject, based on the update data set, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin, - using the probability algorithm: calculating for the suggested insulin dose size the probability of hypoglycaemia for the subject,- determining whether the suggested insulin dose size is above or below the threshold acceptable probability, and - communicating to the subject whether the suggested insulin dose size is acceptable or should be lowered amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 4, and 7, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a computing system, one or more processors, and a memory to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the computing system, one or more processors, and memory are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of performing calculations, receiving data, obtaining data, determining data, and communicating data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2, 3, and 6 are ultimately dependent from Claim(s) 1 and 5 and include all the limitations of Claim(s) 1 and 5. Therefore, claim(s) 2, 3, and 6 recite the same abstract idea. Claims 2, 3, and 6 describe further limitations regarding a historic data set for the subject, display a determined dose size representing the requested dose size recommendation, and wherein the update data set is obtained using a historic data set for the subject and a most recent FBG value and a corresponding insulin dose size for the subject. These are all just further describing the abstract idea recited in claims 1 and 5, without adding significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, representative independent claims 1, 4, 5, and 7 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to one or more processors calculating, receiving a request, obtaining data, and communicating data, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of performing repetitive calculations, storing and retrieving information in memory, and receiving and transmitting data over a network. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-7 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 5 are rejected for reciting grammatically unclear language. Claim 1 recites “the probability of hypoglycaemia respectively hyperglycaemia for the subject,” “calculated hypoglycaemia respectively hyperglycaemia probabilities,” and “the probability of hypoglycaemia respectively hyperglycaemia for the subject.” Claim 5 recites “the probability of hypoglycaemia respectively hyperglycaemia for the subject“ and “the calculated hypoglycaemia respectively hyperglycaemia probabilities.“ Examiner requests clarifying what exactly is being claimed in these limitations.
Claims 1, 3, and 4 recite the limitation "the requested dose size recommendation" in the last line of the claims. There is insufficient antecedent basis for this limitation in the claims. It is unclear if this requested recommendation is related to the “dose guidance request” recited earlier in the claims, or if it is related to a different request.
Claims 2 and 6 incorporate the deficiencies of claims 1 and 5, through dependency, and are therefore also rejected.
Claim Objections
Claims 1 and 4 are objected to because of the following informalities: change “sat” to “set.” Appropriate correction is required.
Claim 5 is objected to because of the following informalities: change “an FBG target” to “a fasting blood glucose (FBG) target” at line 3. Appropriate correction is required.
Claims 1, 4, 5, and 7 are objected to because of the following informalities: change “the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject” to “a mean and variance of a resulting fasting blood glucose value (FBG) for the subject” within claims 1, 4, and 7. Change “the mean and the variance of the resulting FBG for the subject“ to “a mean and variance of a resulting FBG for the subject“ within claim 5. Appropriate correction is required.
Claims 2 and 3 are objected to because of the following informalities: change “A computing system…” to “The computing system….” Appropriate correction is required.
Claim 6 is objected to because of the following informalities: change “a most recent FBG value and a corresponding insulin dose size” to “the most recent FBG value and the corresponding insulin dose size.” Appropriate correction is required.
Claim 6 is objected to because of the following informalities: change “A method…” to “The method….” Appropriate correction is required.
Claims 1, 5, and 7 are objected to because of the following informalities: change “the probability of hypoglycaemia…” to “a probability of hypoglycaemia…“ in lines 10-11 of claim 1, lines 10-11 of claim 5, and lines 10-11 of claim 7. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: change “using a probability algorithm…” to “using the probability algorithm...” Appropriate correction is required.
Claim 4 is objected to because of the following informalities: change “the probability algorithm” to “a probability algorithm.“ Appropriate correction is required.
Claims 1, 4, and 7 are objected to because of the following informalities: change “an injected dose” to “the injected dose” at lines 22-23 of claim 1, lines 18-19 of claim 4, and lines 18-19 of claim 7. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: change “probabilities” to “probability.” Appropriate correction is required.
Claims 1, 4, and 5 are objected to because of the following informalities: change “the probability for a corresponding policy target“ to “a probability for a corresponding policy target.“ Appropriate correction is required.
Claims 1 and 4-7 are objected to because of the following informalities: the claims recite “insulin dose size” throughout the claims. It is unclear if the “insulin dose size” recited in the claims is referring to one insulin dose size, or if they are different sizes since there is a lack of the word “the” prior to “insulin dose size” after the initial mention of an insulin dose size. Appropriate correction is required.
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.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Orden et al. (US 2019/0272912 A1) in view of Raskin et al. (US 2021/0213200 A1).
(A) Referring to claim 7, Van Orden discloses A computing system for providing a titration dose guidance recommendation for a query subject to treat diabetes mellitus, wherein the system comprises one or more processors and a memory, the memory comprising (Fig. 2, para. 1, 2, 18, and 114 of Van Orden; Turning to FIG. 2, an exemplary basal titration adjustment device 250 for obtaining a basal rate titration schedule for a subject comprises one or more processing units (CPU's) 274, a network or other communications interface 284, a memory 192 (e.g., random access memory), one or more magnetic disk storage and/or persistent devices 290 optionally accessed by one or more controllers 288, one or more communication busses 213 for interconnecting the aforementioned components, a user interface 278, the user interface 278 including a display 282 and input 280 (e.g., keyboard, keypad, touch screen), and a power supply 276):
A) instructions for executing by the one or more processors (para. 151 of Van Orden; As illustrated in FIG. 2, a basal titration adjustment device 250 comprises one or more processors 274 and a memory 192/290. The memory stores instructions that, when executed by the one or more processors, perform a method.):
- a for the subject representative prediction algorithm adapted to calculate the mean and the variance of the resulting fasting blood glucose value (FBG) for the subject as a function of the size of an injected dose of insulin, the prediction algorithm being adaptive in response to obtained FBG and corresponding insulin dose size data for the subject (para. 36-38, 75, 76, 88, and 171 of Van Orden; the fasting glucose level is computed as (i) the minimum autonomous glucose measurement in the fasting period, (ii) a measure of central tendency across the autonomous glucose measurements in the fasting period, (iii) a range of the autonomous glucose measurement in the fasting period, (iv) an interquartile range across the autonomous glucose measurements in the fasting period, (v) a variance across the glucose measurements in the fasting period, (vi) an average squared difference across the glucose measurements in the fasting period from the mean (μ) of the glucose measurements in the fasting period. The first glycaemic risk measure comprises the fasting glucose level calculated from the plurality of autonomous glucose measurements, where the fasting glucose level is computed by computing a moving period of variance a across the plurality of autonomous glucose measurements. FIG. 12 illustrates how three different insulin medicament dose sizes are given to the subject during the first time course, where the average glucose values during each period are used to estimate the insulin sensitivity and predict change in glucose concentration. Insulin sensitivity is determined based on the fasting glucose values and the corresponding insulin doses where ISF is the insulin sensitivity FG is fasting glucose and U is insulin medicament dose size. The average glucose values during the fasting period in each dosing period, combined with the insulin medicament dose sizes for each dosing period are used to compute the insulin sensitivity.),
B) instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR), the method comprising (para. 162, 199, 201, 206, and 207 of Van Orden; The fasting blood glucose can be measured daily even less often; perhaps as little as once weekly during the second time course. This enables the subject to be guided during titration in a safer manner than any of the existing methods due to the use of a robust predictive model. The predictive titration model will schedule the recommended daily dose, and it will optimize the dose advice based on the fasting blood glucose measurements during the titration period. The outcome of the predictive insulin dose guidance system can be displayed in a number of ways (health care practitioner' computer, the Internet, a paper printout, short message service (SMS) message, directly in the basal titration adjustment module 204, etc.)):
- obtaining from the subject an update data set comprising a most recent FBG value and a suggested insulin dose size (para. 171 and 172 of Van Orden; if the second dataset is used to determine or confirm ΔU.sub.i,j, then adherence by the subject to the standing insulin regimen does not have to be assumed. Rather, the insulin dose size of the subject may be directly computed from the second dataset. For instance, the insulin medicament records 214 having timestamps 220 in each of the epochs may be used to compute the insulin dose size for each epoch.),
- using the prediction algorithm: calculating for the subject, based on the update data set, the mean and the variance of the resulting FBG for the subject as a function of the size of an injected dose of insulin (para. 36-40 of Van Orden; G is the mean of the autonomous glucose measurements selected from the plurality of autonomous glucose measurements, and k is within the respective contiguous predetermined time span. A fasting period in the first time course is associated with a respective contiguous predetermined time span exhibiting a minimum variance. The standing insulin regimen for the subject over the first time course specifies a plurality of epochs (n) within the first time course, and a different daily total basal insulin medicament dosage for each respective epoch in the plurality of epochs. Successive measurements in the plurality of autonomous glucose measurements in the second dataset are taken at an interval rate of one day, two days, three days, four days, five days, six days, or seven days.),
- using the probability algorithm: calculating for the suggested insulin dose size the probability of hypoglycaemia for the subject (para. 86 and 207 of Van Orden; FIG. 10 illustrates how the four hours prior to the identified minimum running variance of Panel B of FIG. 9 is used as the source of fasting glucose values in accordance with an embodiment of the present disclosure. This period is used to estimate hypoglycaemic risk. In this case, a hypoglycaemic event occurred once during the first time course.),
- communicating to the subject whether the suggested insulin dose size is acceptable or should be lowered (para. 98 & 209 of Van Orden; for a calculated value of >10.0 mmol/L it can be recommended to adjust the basal insulin dose with +8 units, for a calculated value of 9.1-10.0 mmol/L it can be recommended to adjust the basal insulin dose with +6 units, for a calculated value of 8.1-9.0 mmol/L it can be recommended to adjust the basal insulin dose with +4 units, for a calculated value of 7.1-8.0 mmol/L it can be recommended to adjust the basal insulin dose with +2 units, and for a calculated value of 6.1-7.0 mmol/L it can be recommended to adjust the basal insulin dose with +2 units. If one BG measurement is 3.1-4.0 mmol/L it can be recommended to adjust the basal dose with −2 units, and if one BG measurement is <3.1 mmol/L it can be recommended to adjust the basal insulin dose with −4 units. The measurement of the fasting blood glucose before titration as well as the resulting basal insulin dose adjustments may either be performed by the patient him/herself or by a doctor/nurse based on BG values supplied by the patient. The fasting blood glucose profile model predicts the fasting blood glucose level of the subject based upon amounts of insulin medicament injected into the subject. This model can be used from time to time to validate the basal rate titration schedule. That is, if the model cannot adequately predict the subject's glucose levels based upon the amounts of insulin medicament that subject has recently taken, the corresponding basal rate titration schedule is not validated and the intensive first time course is repeated to obtain a new basal rate titration schedule and/or the basal rate titration schedule is switched to a more conservative schedule with a higher glucose target and reduced insulin medicament.).
Van Orden does not disclose a probability algorithm adapted to, based on predicted mean and variance FBG values and a hypoglycaemia target value, calculate as a function of insulin dose size the probability of hypoglycaemia for the subject; obtaining for the subject a threshold acceptable probability for hypoglycaemia; determining whether the suggested insulin dose size is above or below the threshold acceptable probability.
Raskin discloses a probability algorithm adapted to, based on predicted mean and variance FBG values and a hypoglycaemia target value, calculate as a function of insulin dose size the probability of hypoglycaemia for the subject (para. 253, 321, and 324 of Raskin; In some cases, the blood glucose control system may determine three values of average blood glucose: the mean value (e.g., computed for the entire G(t) measured during a therapy period or part of a therapy period), a pre-meal mean value (e.g., computed for the time window of 60-120 min after the meal), and post-meal mean value (e.g., computed for the time window of 0-60 min before meal). Computing of pre- and post-meal averages and the difference between the averages can serve as an indication of the overall effectiveness of pre-meal bolus timing and bolus amount. In some examples, deviation from target or normoglycemia may be evaluated by determining percentages of time spent within, below, or above preset target limits (e.g., G.sub.min=70 and G.sub.max=180 mg/dL). In some examples, the percentage of time within each range may be calculated via linear interpolation between consecutive glucose readings. In some other examples, percentage of time within additional ranges can be computed. In some such examples, the probability of occurrence of extreme hypoglycemia and hyperglycemia may be also evaluated. To quantify variability of blood glucose level, in some examples, standard deviation and variance may be used to compute variability of BGL. In some cases, a risk index may be defined that can serve as a measure of overall glucose variability when focusing of the relationship between glucose variability and risks for hypo- and hyperglycemia. The process 1300 may be modified to determine the optimal value of Tmax, or a value of Tmax that provides improved maintenance of the subject's diabetes, by reducing Tmax (increasing the aggressiveness of the therapy) after each therapy period in a series of therapy periods, until a statistical assessment shows that further reduction of the Tmax does not improve the mean glucose level without increasing the probability of hypoglycemia. Improved maintenance of the subject's diabetes may include maintaining a mean glucose level closer to a setpoint glucose level range or reducing fluctuations in mean glucose level over time compared to prior control value (e.g., Tmax) settings. It should be understood that other metrics may be used to measure an improvement of maintenance of the subject's diabetes, such as reduction in hypoglycemia risk events or reduction in administration of insulin without increasing diabetic effects or corresponding risks.); obtaining for the subject a threshold acceptable probability for hypoglycaemia and determining whether the suggested insulin dose size is above or below the threshold acceptable probability (para. 89 and 270 of Raskin; The process 700 may be performed by any system that can track the glucose level of a subject over time and identify hypoglycemic events, or occurrences when a risk of a hypoglycemic event satisfies a threshold (e.g., when the risk of the hypoglycemic event matches or is above a particular probability). For example, the process 700 may be performed by one or more elements of the glucose level control system 510. In some cases, at least certain operations of the process 700 may be performed by a separate computing system that receives indications of blood glucose levels of the subject 512 from the glucose level control system 510 and/or indications of hypoglycemic events (or identified above threshold hypoglycemic risk events). If the controller of the blood glucose level control system determines that there is a significant or statistically significant improvement (e.g., more than a threshold improvement) in the mean glucose level for the subject with little or no increase in hypoglycemia events or risk events, the system can adopt or recommend the lower Tmax value as the preferred Tmax. This process can be repeated using additional reductions in Tmax. In some cases, each reduction in Tmax may be smaller than the previous reduction. Moreover, if it is determined that there is a not an improvement in the mean glucose level for the subject and/or if there is an increase in hypoglycemia or hypoglycemia risk events, the system may use the prior Tmax or may select a Tmax between the new Tmax and the prior Tmax. Thus, using the process 1300, the system can iteratively modify Tmax to find an optimal value for the subject and/or the selected insulin type.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Raskin within Van Orden. The motivation for doing so would have been to determine impending risk (para. 6 of Raskin).
Subject Matter Free of Prior Art
Regarding independent claim 1, the closest prior art , Van Orden et al. (US 2019/0272912 A1) and Raskin et al. (US 2021/0213200 A1), do not teach or fairly suggest: a policy algorithm adapted to, based on calculated hypoglycaemia respectively hyperglycaemia probabilities, calculate the probability for a corresponding policy target as a function of insulin dose size; and using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and - determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation.
Regarding independent claim 4, the closest prior art , Van Orden et al. (US 2019/0272912 A1) and Raskin et al. (US 2021/0213200 A1), do not teach or fairly suggest: a policy algorithm adapted to, based on predicted mean and variance FBG values, calculate the probability for a corresponding policy target as a function of insulin dose size, obtaining for the subject an FBG target to be used as the policy target for the probability algorithm, using the policy algorithm: calculating the probability for the policy target as a function of insulin dose size, and determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing the requested dose size recommendation.
Regarding independent claim 5, the closest prior art , Van Orden et al. (US 2019/0272912 A1) and Raskin et al. (US 2021/0213200 A1), do not teach or fairly suggest: using a policy algorithm: calculating, based on the calculated hypoglycaemia respectively hyperglycaemia probabilities (calculated as a function of insulin dose size and based on the predicted mean and variance FBG values and the pre-defined FBG target range), the probability for a corresponding policy target as a function of insulin dose size, and determining the insulin dose size having the highest calculated probability for meeting the policy target, the determined dose size representing a dose size recommendation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches systems, devices, and/or methods for identifying time periods of insufficient blood glucose testing (US 2016/0038077 A1); a pre-mail insulin dose individualized decision system based on Gaussian process (CN-112133439-A); and MDI dosage suggestion system based on Bayesian optimization (CN-114464291-A).
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
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