DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/28/26 has been entered. Claim 1 has been amended. Claims 1-27 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-27 remains rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-28 of U.S. Patent No. 11,696,728 and claims 1-39 of U.S. Patent No. 12, 144,658. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application is anticipated by the claims of the previously patented claims.
Claim 1 of the instant application recites receiving CGM and insulin data; generating a replay analysis; quantifying an amount of glycemic dysfunctions and outputting the amount of glycemic dysfunction. Claim 1 of the previous patents recite the same features plus further limitations. Therefore, patent claim 1 in the previous patents is in essence a "species" of the generic invention of instant claim 1. It has been held that a generic invention is "anticipated" by a "species" within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: analyzer and quantifier in claim 1.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-27 are rejected under 35 U.S.C. 103 as being unpatentable over Sloan et al., herein after Sloan (U.S. 2010/0295686 A1) in view of Kovatchev (US 2016/0331310 A1) and Agrawal (US 2013/0338629 A1).
As per claim 1 Sloan teaches a system for determining and reducing an amount of glycemic dysfunction (Figure 3; paragraph 14 discloses a model-based calculation of the present and near-future values of best estimate and upper/lower bounds of glucose to account for predicted hypoglycemic and hyperglycemic events), the system comprising:
a data processor that receives continuous glucose monitoring (CGM) and insulin data pertaining to a subject (Figure 2 discloses a glucose profile showing glucose levels measured using a CGM sensor as a function of time), wherein the insulin data comprises an insulin bolus amount and a timing for the insulin bolus amount (paragraph 5 discloses the output of sensors can be communicated to a hand held device or controller that is used to calculate an appropriate dosage of insulin to be delivered to the user of the CGM in view of several factors including present glucose level, insulin usage rate, carbohydrates consumed or to be consumed and exercise; paragraph 37 discloses requiring user input of some amount of information from which the system determines insulin bolus administration, including insulin bolus quantity or quantities, bolus type, and insulin bolus delivery time, times or intervals);
a replay analyzer that determines an impact of a previously delivered inefficient or suboptimal bolus by generating a replay analysis using the CGM and insulin data; (Figure 3; paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level);
a quantifier that quantifies an amount of glycemic dysfunction using the replay analysis; (Figure 3; paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level; paragraph 14 discloses a model-based calculation of the present and near-future values of best estimate and upper/lower bounds of glucose to account for predicted hypoglycemic and hyperglycemic events);
and an output device that provides an output representative of the amount of glycemic dysfunction for use in improving diabetes management, including (Figure 2; paragraph 30 discloses such a display may be a conventional display device including for example, but not limited to, a light emitting diode (LED) display, a liquid crystal display (LCD), a cathode ray tube (CRT) display, or the like; paragraph 49 discloses a typical glucose absorption profile for a user measured using a CGM sensor where the graph plots the measured glucose level as a function of time. It is further noted that using the output for use in improving diabetes management by causing an adjustment to insulin therapy delivered by an insulin delivery device based on the amount of glycemic dysfunction to reduce the glycemic dysfunction is a statement of intended use and non-functional descriptive material and given little to no patentable weight.
Sloan does not expressly teach
wherein the quantifier determines at least one glycemic risk index comprising at least one total blood glucose (BG) risk score
and further providing recommendations for pre-meal bolus timing adjustments based on the quantified glycemic risk index
However, these features are old and well-known in the art as evidenced by Kovatchev and Agrawal. Kovatchev teaches glycemic risk indices derived from blood glucose values, including measures corresponding to blood glucose risk scoring (Kovatchev, e.g., paras. [0010] - [0015], [0020]–[0023] describing computation of glycemic risk metrics based on glucose values and transformation of glucose data into risk scores). This reads on “a glycemic risk index comprising at least one total blood glucose (BG) risk score.” Agrawal teaches analysis of meal timing and insulin administration timing and providing guidance and recommendations for insulin delivery relative to meals to improve glycemic control (Agrawal, e.g., paras. [0025], [00146] discussing timing of insulin delivery relative to meals and providing user guidance). It would have been obvious to one of ordinary skill in the art to incorporate Kovatchev’s glycemic risk index into Sloan to provide improved quantification of glycemic risk and control and incorporate Agrawal’s meal timing guidance to provide actionable recommendations for insulin administration timing because all references are directed to improving diabetes management using CGM and insulin data.
Sloan further does not expressly teach wherein the output comprises a visualization showing historical CGM and insulin data vs. replay simulated CGM with numerically optimal boluses at historical bolus times, or historical CGM and insulin data vs. replay simulated CGM with numerically optimal boluses at estimated meal times. However, this is an obvious variant of the Sloan teachings. Sloan teaches: model-based computation of glucose levels based on insulin and CGM data (para. [0014]); display of glucose data over time (Fig. 2; para. [0049]). Presenting modeled glucose data together with historical CGM data in a comparative format constitutes a presentation of analysis results. Displaying historical CGM data and
modeled/simulated CGM data together for comparison would have been an obvious matter of graphical presentation to improve user understanding of glycemic control. It would have been an obvious variant of the Sloan teachings to present modeled and historical data together in order to improve interpretation and usability of the system.
As per claim 2, Sloan teaches the system of claim 1, wherein a first portion of the CGM and insulin data is estimated and a second portion of the CGM and insulin data is reported (paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level; paragraph 14 discloses a model-based calculation of the present and near-future values of best estimate and upper/lower bounds of glucose to account for predicted hypoglycemic and hyperglycemic events; paragraph 15 discloses determining a bolus volume to be administered to make up for a cessation of basal delivery of insulin by determining an amount of insulin remaining is a user’s body and calculating a bolus delivery to equal the amount of basal delivery lost).
As per claim 3, Sloan teaches the system of claim 1, wherein the CGM and insulin data comprise at least one of an estimated timing of a meal of the subject or an estimated composition of the meal of the subject (paragraph 37 discloses user supplied information may include user glucose concentration, interstitial glucose level information, information relating to a meal or snack that has been ingested or is to be ingested (estimated timing of the meal); paragraph 49 discloses a typical glucose absorption profile for a user measured using a CGM sensor where the graph plots the measured glucose level as a function of time and shows the effect of glucose level of various actions such as carbohydrate intake (meal of the subject) and the delivery of rapid acting insulin and long acting insulin; paragraph 60 discloses due to various events that affect the metabolism of a user, such as eating a meal or engaging in exercise, a bolus deliver of insulin is required to raise the blood concentration of insulin to an effective level to counteract the effects of the ingestion of carbohydrates during a meal).
As per claim 4, Sloan does not expressly teach system of claim 1, wherein the CGM and insulin data comprise misinformation about at least one of a timing of a meal of the subject or a composition of the meal of the subject.
However, this is old and well-known in the art as evidenced by Agrawal. In particular Agrawal paragraph 155 discloses distracted users who forget to treat their diabetes by skipping boluses, eating high sugar food (composition of the meal), forgetting to turn on the insulin pump; paragraph 163 discloses the user is prompted to estimate a carbohydrate value for each of the plurality of representative foods presented to the user such as large, small, two egg omelet, 3 egg omelet which is interpreted as composition of the meal).
Sloan discloses systems and methods for management of a user’s glucose level when insulin delivery is to be restarted after an unexpected stop. The user’s insulin history and glucose level history are used for calculating a bolus volume of insulin to bring a user’s insulin on board up to the level it would have been had the insulin delivery not been stopped. Agrawal discloses systems and methods for diabetes therapy management where average glucose level information for a time period over a plurality of days is determined. A current event occurrence is determined and may be breakfast lunch, or dinner and a notification event may be initiated indicating hyperglycemia, hypoglycemia, a sharp glucose spike, or a sharp glucose level drop.
It would have been obvious to one of ordinary skill at the time of the invention to expand the method of Sloan to further include diabetes therapy management system for recommending basal patter adjustments by analyzing patient information to generate reports to assist in diabetes management as disclosed by Agrawal. One of ordinary skill in the art at the time of the invention would have been motivated to expand the method of Sloan in this way to improve glycemic control in insulin pump users by optimizing the insulin pump therapy parameters (Agrawal: paragraph 173).
As per claim 5, Sloan teaches the system of claim 1, wherein a portion of the CGM and insulin data is received from a computing device of the subject (paragraph 37 discloses requiring user input of some amount of information from which the system determines insulin bolus administration, including insulin bolus quantity or quantities, bolus type, and insulin bolus delivery time, times or intervals).
As per claim 6, Sloan teaches the system of claim 1, wherein the CGM and insulin data comprise estimated metabolic states in time series form, a reconciled meal history, and a delivered insulin history (paragraph 19 discloses a method of adjusting glucose level alarm thresholds and alarm enunciation delay times using CGM and insulin delivery information using a model based state estimation and determining a predicted future glucose level; paragraph 50 discloses information relating to meal intake information supplied by the user should contain an estimate of the carbohydrate content of the meal or snack, corresponding to the amount of carbs the user is about to ingest, is ingested, or has ingested; paragraph 66 discloses the controller and/or pump has a memory that stores information related to the history of the user’s glucose levels and various actions or events that have been taken to adjust those levels, such as the rate of basal delivery of insulin, the amount of the last insulin delivery, and the time between various events or user actions).
As per claim 7, Sloan teaches the system of claim 1, wherein the CGM and insulin data comprise estimated metabolic states, reconciled estimated metabolic inputs, and known metabolic inputs (paragraph 50 discloses information relating to meal intake information supplied by the user should contain an estimate of the carbohydrate content of the meal or snack, corresponding to the amount of carbs the user is about to ingest, is ingested, or has ingested; paragraph 66 discloses the controller and/or pump has a memory that stores information related to the history of the user’s glucose levels and various actions or events that have been taken to adjust those levels, such as the rate of basal delivery of insulin, the amount of the last insulin delivery, and the time between various events or user actions).
As per claim 8, Sloan teaches the system of claim 1, wherein the CGM and insulin data is discretized in time (Figure 2 discloses a glucose profile showing glucose levels measured using a CGM sensor as a function of time; paragraph 37 discloses insulin bolus administration information may include quantity or quantities, bolus type, and insulin bolus delivery time, times or intervals).
As per claim 9, Sloan teaches the system of claim 1, wherein the replay analyzer isolates an impact of timing, carbohydrate counting, and carbohydrate ratio of an estimated bolusing with respect to a meal (paragraph 54 discloses an exemplary measure of total glycemic index includes the ratio of carbohydrates absorbed from the meal and a reference value derived from pure sugar or white bread over a specified time period; paragraph 124 discloses a carbohydrate ration with include 1 unit of insulin for 15 grams of carbs; paragraph 129 – 130 discloses once the processor and controller know that the user will be in a carbohydrate deficient state, the controller will display instructions which instruct the user to eat X grams of carbs now or later to avoid low glucose levels).
As per claim 10, Sloan does not expressly teach the system of claim 1, wherein the replay analyzer uses replay simulation analysis to assess an impact of numerically optimal boluses at historical bolus times by removing inaccurate carbohydrate counts and inappropriate carbohydrate ratios.
However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal Figure 22; paragraph 11 discloses the bolus dosage recommendation is increased if the user’s response to estimate the carbohydrate value for the at least one of the representative foods corresponding to the food to be consumed is lower than the true carbohydrate value for that particular food. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
As per claim 11, Sloan teaches the system of claim 1, wherein the replay analyzer assesses an impact of numerically optimal boluses at estimated historical meal times (paragraph 104 discloses present and projected glucose of the user in a selected near future horizon falling within a specified nominal target, or one that optimizes the present and projected glucose with respect to optimality criteria).
As per claim 12, Sloan teaches the system of claim 1, wherein the replay analyzer performs replay simulations at times of historical boluses or at times in advance of estimated meals (paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level).
As per claim 13, Sloan teaches the system of claim 1, wherein the replay analyzer performs replay simulations with at least one of historical CGM and insulin data, numerically optimal boluses at historical bolus times, or numerically optimal boluses at estimated meal times (paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level).
As per claim 14, Sloan teaches system of claim 1, wherein the replay analyzer is configured to: generate a set of candidate boluses (paragraph 10 discloses the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin (candidate bolus);
pick and implement the best bolus based on the score of each candidate bolus paragraph 10 discloses the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin (candidate bolus);
and continue to replay simulate until the time of the next bolus (Figure 3; paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level; paragraph 16 discloses analyzing the future glucose level using the latest bolus value plus a future basal rate to determine if future glucose level is acceptable, and if so, waiting for a selected period of time and repeating).
Sloan fails to expressly teach the replay analyzer is configured to:
simulate future blood glucose (BG) values associated with each candidate bolus; score a simulated BG trajectory for each candidate bolus using a risk analysis.
However, these features are old and well-known in the art as evidenced by Kovatchev. In particular, Kovatchev teaches: simulating future blood glucose (BG) values associated with each candidate bolus (paragraph 53 discloses an advisory module with a bolus calculator suggesting pre-meal insulin doses); and
scoring a simulated BG trajectory for each candidate bolus using a risk analysis (paragraph 50 discloses patient’s risk status for hypo- or hyperglycemia; paragraph 73 discloses risk analysis).
Sloan discloses systems and methods for management of a user’s glucose level when insulin delivery is to be restarted after an unexpected stop. The user’s insulin history and glucose level history are used for calculating a bolus volume of insulin to bring a user’s insulin on board up to the level it would have been had the insulin delivery not been stopped. Kovatchev discloses a flexible system capable of utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long term trends and prognosis to real-time automated closed-loop control. It would have been obvious to one of ordinary skill at the time of the invention to expand the method of Sloan to further the ability to utilize data from different diabetes monitoring techniques such as self-monitoring of blood glucose (SMBG), multiple daily insulin injections (MDI), Continuous Glucose Monitoring, and an artificial pancreas as disclosed by Kovatchev.
One of ordinary skill in the art at the time of the invention would have been motivated to expand the method of Sloan in this way to improve glycemic control of a patient comprising an input module configured to accept input data from one or more of a plurality of diverse insulin delivery devices (Kovatchev: paragraph 28).
As per claim 15, Sloan does not expressly teach the system of claim 14, wherein the risk analysis is a velocity dependent risk analysis. However, these features are old and well-known in the art as evidenced by Kovatchev. In particular, Kovatchev paragraph 50 discloses patient’s risk status for hypo- or hyperglycemia; paragraph 73 discloses risk analysis. The motivation to combine the teachings of Sloan and Kovatchev is discussed in the rejection of claim 14, and incorporated herein.
As per claim 16, Sloan teaches the system of claim 1, wherein the quantifier quantifies the amount of dysfunction of estimated bolusing compared to an optimally timed bolusing (paragraph 14 discloses a model-based calculation of the present and near-future values of best estimate and upper/lower bounds of glucose to account for predicted hypoglycemic and hyperglycemic events).
As per claim 17, Sloan teaches the system of claim 1, wherein the quantifier quantifies the amount of dysfunction of historical bolusing compared to an optimally timed bolusing (paragraph 10 discloses using the insulin history and glucoses level history for calculating and recommending a bolus volume of insulin to be delivered to bring a user’s insulin level on board).
As per claim 18, Sloan teaches the system of claim 1, wherein the quantifier quantifies a compliance of the patient with an ideal pre-meal bolus timing (paragraph 37 discloses user supplied information may include user glucose concentration, interstitial glucose level information, information relating to a meal or snack that has been ingested or is to be ingested (estimated timing of the meal); paragraph 51 discloses an estimate of the amount of carbs the patient is about to ingest is provided by the user).
As per claim 19, Sloan teaches the system of claim 1, wherein the output comprises at least one of a plot or a visualization (Figure 2; paragraph 49 discloses a typical glucose absorption profile for a user measured using a CGM sensor where the graph plots the measured glucose level as a function of time).
As per claim 20, Sloan does not expressly teach the system of claim 1, wherein the output comprises a risk index comprising at least one of high blood glucose risk, low blood glucose risk, or total glycemic risk. However, these features are old and well-known in the art as evidenced by Kovatchev. In particular, Kovatchev teaches wherein the output comprises a risk index comprising at least one of high blood glucose risk, low blood glucose risk, or total glycemic risk (paragraph 14). The motivation to combine the teachings of Sloan and Kovatchev is discussed in the rejection of claim 14, and incorporated herein.
As per claim 21, Sloan does not expressly teach the system of claim 1, wherein the output comprises a number and an extent of excursions into an out-of-range blood glucose level. However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal Figure 7B; paragraph 169 discloses hypoglycemic patterns and time periods of occurrence as well as hyperglycemic and time periods of occurrence. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
As per claim 22, Sloan teaches the system of claim 1, wherein the output shows glycemic dysfunction at historical times (paragraph 10 discloses a system and method for use when insulin delivery is to be restarted after an unexpected stop in delivery, where the insulin history and glucose level history are used for calculating and recommending a bolus volume of insulin to be delivered to stabilize a user’s insulin level).
As per claim 23, Sloan teaches the system of claim 1, wherein the output device provides a visualization showing at least one of a behavioral impact of the glycemic dysfunction, historical CGM and insulin data vs. replay simulated CGM with optimal boluses at historical bolus times, or historical CGM and insulin data vs. replay simulated CGM with optimal boluses at estimated meal times (paragraph 49 discloses a typical glucose absorption profile for a user measured using a CGM sensor where the graph plots the measured glucose level as a function of time).
As per claim 24, Sloan does not expressly teach the system of claim 1, further comprising a compliance engine that is configured to provide a visualization of a compliance of the patient with an ideal pre-meal bolus timing. However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal Figures 6A and 17; paragraph 6 discloses at least one anomalous glucose level is analyzed and is adapted to the pattern to form an adapted glucose level patter; paragraph 91 discloses an assessment of a subject’s compliance to a therapy including diabetes; paragraph 155 discloses distracted users where patterns may be used to identify habitual lapses in compliance. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
As per claim 25, Sloan does not expressly teach the system of claim 1, wherein the output device provides meal management information to the subject based on the amount of glycemic dysfunction. However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal Figure 15A; paragraph 226 discloses providing a recommendation to counsel the patient regarding meal timing and other dietary habits. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
As per claim, 26, Sloan does not expressly teach the system of claim 25, wherein the meal management information comprises pre-bolus timing information for the patient. However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal paragraph 226 discloses providing a recommendation to adjust a bolus dosage for a meal bolus event or recommend to adjust the timing of a bolus. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
As per claim 27, Sloan does not expressly teach the system of claim 26, wherein the pre-bolus timing information comprises a recommendation to the patient regarding bolusing before a type of meal in the future. However this is old and well-known in the art as evidenced by Agrawal. In particular, Agrawal paragraph 226 discloses providing a recommendation to adjust a bolus dosage for a meal bolus event or recommend to adjust the timing of a bolus. The motivation to combine the teachings of Sloan and Agrawal is discussed in the rejection of claim 4, and incorporated herein.
Response to Arguments
Applicant's arguments filed 4/28/26 have been fully considered but they are not persuasive.
On page 7 of the 4/28/26 Remarks, Applicant traverses the Office’s interpretation that the limitations “replay analyzer” and “quantifier” invoke 35 U.S.C. §112(f), asserting that the claim language “speaks for itself.” This argument is not persuasive. The terms “analyzer” and “quantifier” are nonce terms that do not recite sufficiently definite structure and are defined primarily by the functions they perform. Applicant has not provided evidence that these terms convey recognized structure to one of ordinary skill in the art. Accordingly, the Office maintains that these limitations are properly interpreted under §112(f).
Next, Applicant requests that the rejection be held in abeyance. No terminal disclaimer has been filed and no arguments regarding patentable distinctness have been presented. Accordingly, the rejection is maintained.
Applicant’s arguments with respect to application of the prior art to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s particular arguments regarding the 102 application of Sloan is now moot as that rejection has been withdrawn in light of the claim amendments and updated in the body of the rejection above. For thoroughness, Examiner will address any pertinent arguments.
On pg. 7 of the Remarks Applicant argues Sloan does not teach "a visualization showing historical CGM and insulin data vs. replay simulated CGM with numerically optimal boluses at historical bolus times" or showing "historical CGM and insulin data VS. replay simulated CGM with numerically optimal boluses at estimated meal times," as recited in amended claim 1. Notwithstanding the newly applied 103 rejection, this argument is not persuasive. The recited visualization is directed to presentation of information. The claim does not require that the visualization affects the operation of the system or the insulin delivery device. As such, the visualization constitutes non-functional descriptive material. Under MPEP 2111.05, non-functional descriptive material is not entitled to patentable weight. Sloan discloses presenting glucose-related information (e.g., FIG. 2, [0049]), which is sufficient under a broadest reasonable interpretation. Accordingly, the visualization limitation does not distinguish over Sloan.
Applicant further argues on pg. 7 that Sloan does not teach recommendations for pre-meal bolus timing adjustments based on a quantified glycemic risk index, as recited in amended claim 1. This argument is not persuasive. Providing “recommendations” constitutes advisory output and does not require implementation of any specific control action. The claim does not require that insulin delivery be modified in response to the recommendations. Therefore, this limitation is directed to information content, not a technical operation. As such, the recommendation limitation is non-functional and does not distinguish over Sloan.
Next on pg. 8 of the Remarks Applicant argues the proposed combination of Sloan and Agrawal fails to disclose, teach, or suggest at least amended claim 1's specific comparative replay visualization of historical CGM/insulin data versus replay-simulated CGM using numerically optimal boluses at historical bolus times or at estimated meal times. This argument is not persuasive. The claim does not recite any specific algorithm, optimization criteria, or modeling technique for determining “numerically optimal boluses.” Under the broadest reasonable interpretation, this limitation encompasses any computed or adjusted insulin delivery values derived from CGM and insulin data. Sloan teaches using historical insulin delivery and glucose-related data to compute insulin-on-board and determine dosing adjustments. Such computations necessarily model the effect of alternative insulin delivery values and therefore reasonably read on “replay simulation” under BRI. The recitation of “numerically optimal” merely expresses a desired result and does not impose a structural limitation distinguishing over Sloan.
Conclusion
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/ Primary Examiner, Art Unit 3686 5/30/2026
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