DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant's election with traverse of Group II: Claims 10 - 18 in the reply filed on 28 JANUARY 2026 is acknowledged.
Claims 1-9 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected group, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on January 28, 2026.
The traversal is on the following grounds:
The applicant argues at [Page 2, Paragraph 1 – Paragraph 5, and Page 4 2nd Paragraph] that the Cichosz reference cited does not destroy the unity of invention between the claims of Groups I and II because Cichosz et. al. uses a combination of CGM and HRV measurements to detect glycemic events, and the Applicant’s invention uses only HRV to determine glucose levels. As recited in the instant claims, there is nothing particular that indicates that only HRV is used to determine glucose levels. Further, as recited with “calculating an equivalent to HbA1C and plasma glucose levels”, the calculation limitation is not connected to any of the previously-recited HRV parameters or ECG information. The argument is not persuasive.
The applicant argues at [Page 2, Paragraph 6] that Cichosz refers to a binary classifier of an event, whereas the invention refers to a numerically measured blood glucose level and a 2 -3 month average level. There is nothing particular recited in the claim that the plasma glucose level is specifically a numerically measured blood glucose level. Rather, the limitation can be interpreted broadly as a relative blood glucose level, which would include hypoglycemia, which is a “low plasma glucose level”. Further, each of the participants in the study from which the hypoglycemia is determined have a calculated HbA1c level (the 2 – 3 month average level), the mean of which is calculated at [Page 732, Left Column, 6th Full Paragraph]. There is nothing particular claimed about the context of the inputs to the calculation. The argument is not persuasive.
The applicant argues at [Page 2, Last Paragraph] and [Page 5, Bottom] – [Page 6, 1st Full Paragraph] that Cichosz uses fixed-sized windows, and the Applicant defines different window sizes using a sliding window approach. The limitation in Applicant’s claims recites “with predefined window sizes and offsets”. There is nothing particular recited in the claim limitation that precludes those window sizes from being predefined as a fixed-size window. The argument is not persuasive.
Applicant argues at Page 4, 2nd Full Paragraph] that Cichosz specifies a machine learning algorithm to detect hypoglycemic events, which is different from the claimed invention that detects glucose levels from HRV. As recited in the instant claims, there is nothing particular that indicates that the HRV is used to determine glucose levels. The limitations are recited with HRV elements separate of the calculating an equivalent to HbA1C and plasma glucose levels limitation. These is nothing tying HRV into the recited calculation. The argument is not persuasive.
Applicant argues at [Page 4, bottom] – [Page 5, Top] that extracting a clean ECG interval is performed in Cichosz by excluding complete ECG signals, and the instant invention excludes specific beats. As recited with a clean ECG interval, there is nothing particular that limits the interval to being individual beats or “marking dirty beats”. The argument is not persuasive.
Applicant argues at [Page 5, Paragraph 1 – 2] that Cichosz only measures standard HRV parameters and not an “extended set” that are annotated by A, C, and S, and with short, medium, long, and extra long terms. As recited, extended set of HRV parameters is very broad, as there is no clear recitation of what constitutes a “parameter” or what “extended” indicates. The term can be broadly interpreted as calculating HRV-associated parameters (like “peaks” and “RR intervals”) extended along a period of time (like Cichosz’s 5 minutes). The argument is not persuasive.
Applicant argues at [Page 6, 2nd Full Paragraph] – [Page 7, 1st Full Paragraph] that Cichosz does not disclose applying a set of threshold decisions to detect ability to regulate a predefined glucose level with selecting features that are “particularly distinguishing or informative for classification”, and that the claimed invention analyzes a different set of features (particularly HRV) and specifies their features, not implying using a machine learning algorithm to develop a model. As stated above, there is nothing particularly recited in the claim that actually ties HRV to glucose level determinations. Further, the broad term applying a set of threshold decisions does not indicate a particular numeric threshold. Rather, it can broadly include, the presence of a feature at all (like “normal” vs “hypoglycemic”), a numeric threshold of determining if the level is below 3.9 mmol/L, or a binary logistic regression classifier threshold value. There is nothing particular in the claim that precludes the decisions from involving machine learning. The argument is not persuasive.
Applicant argues at [Page 7, 4th Full Paragraph] – [Page 8, Paragraph 1] that the claimed invention provides means or calculating and presenting the AGP (ambulatory glucose profile) derived from HRV. There is nothing particularly recited that indicates that AGP and glucose statistics are calculated from HRV. The limitation form reports containing AGP and achieved glucose statistics broadly includes statistics that would be in an AGP report (average glucose level over a period of time, where the period of time is reported) and statistics of achieved glucose (average blood glucose levels, hypoglycemia events, etc.). As described above, there is nothing particularly recited in the claim that indicates that this is a the full standardized, one-page glucose report or that it is derived from HRV and not CGM, just that reports are made, which broadly includes reporting statistics in figures and text in a published journal. The argument is not persuasive.
The requirement is still deemed proper and is therefore made FINAL.
Information Disclosure Statement
There are references listed in the specification on [Page 1, Paragraphs 3 – 5] and [Page 2, Top – 1st Full Paragraph], [Page 4, Line 14], and [Page 6, line 17]. The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The use of the term BLUETOOTHTM, which is a trade name or a mark used in commerce, has been noted in this application at [Page 7, Line 18]. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 10 is objected to because of the following informalities: · The expanded form of acronym “AGP” is not defined in the claim set. For the first recitation of “AGP” in the claims (Claim 1), an accompanying expanded form of the acronym, “ambulatory glucose profile”, is necessary.
Claim 11 and Claim 12 are objected to because of the following informalities: the term “wherein using marking of dirty beats comprising” in line 2 of each claim is suggested to be revised to “wherein using marking of dirty beats comprises:” for readability. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: there is a period after the term “before the P wave of the second beat” in line 11 instead of a semicolon. Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
the term “wherein the step of applying set of threshold decisions” in line 1 is suggested to be revised to “wherein the step of applying the set of threshold decisions” for readability. Appropriate correction is required.
the term “to regulate the glucose level comprising” in line 2 is suggested to be revised to “to
regulate the glucose level comprising” in line 2 is suggested to be revised to “to regulate the glucose level comprises:” for readability. Appropriate correction is required.
the term “plurality thresholds” in lines 5, 7, and 9 is suggested to be revised to “the plurality of thresholds” for readability. Appropriate correction is required.
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 10 - 18 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.
Claim 10 recites the term “A method of detecting the glucose level”. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited glucose level. For the purposes of examination, the term “A method of detecting the glucose level“ is deemed to claim “A method of detecting a glucose level.“ It is noted that within the limitations of the remainder of the claim, there is no particular “glucose level” that is detected. Claims 11 – 18 are similarly rejected due to their dependence on Claim 10.
Claim 10 recites the term “applying a set of threshold decisions to detect ability to regulate a predefined glucose level”. It is unclear to what parameter the “set of threshold decisions” is being applied, or if it is to a multitude of parameters. For the purposes of examination, the term “applying a set of threshold decisions to detect ability to regulate a predefined glucose level” is broadly deemed to claim “applying a set of threshold decisions to collected glucose level data to detect an ability to regulate a predefined glucose level”.
Claim 11 recites the term “wherein using marking of dirty beats” in line 2. It is unclear if this is intended to be the same or different than the previously-recited marking of dirty beats. For the purposes of examination, the term “wherein using marking of dirty beats” is deemed to claim “wherein the using marking of dirty beats.”
Claim 11 recites the term “fourth beats that succeed second beats” in line 6. It is unclear if these second beats are intended to be the same or different than the previously-recited second beats. For the purposes of examination, the term “fourth beats that succeed second beats” is deemed to claim “fourth beats that succeed the second beats.”
Claim 11 recites the term “smaller than a predefined threshold”. It is unclear if this predefined threshold is intended to be the same or different than the previously-recited predefined threshold. For the purposes of examination, the term “smaller than a predefined threshold” is deemed to claim “smaller than a second predefined threshold”.
Claim 12 recites the term “wherein marking of dirty intervals” in line 2. It is unclear if this is intended to be the same or different than the previously-recited marking of dirty intervals. For the purposes of examination, the term “wherein marking of dirty intervals” is deemed to claim “wherein the marking of dirty intervals.”
Claim 12 recites the term “marking a start of a dirty interval” in line 3. It is unclear if this is intended to be the same or different than the previously-recited dirty intervals. For the purposes of examination, the term “marking a start of a dirty interval” is deemed to claim “marking a start of the dirty interval”
Claim 12 (lines 5 – 9) recites the term “marking the start of the dirty interval in a location after a T wave of the first beat and before a P wave of the second beat…where the first beat is marked as the dirty beat, and the second beat belongs to the normal N class”. In light of the other term previously-recited in Claim 12, the following is unclear:
The claimed mark of the end of the dirty interval and previously-recited start of the dirty interval are both made in a middle of a beat-to-beat interval. From this, it appears that the marks are different beat-to-beat intervals, since otherwise a mark in the middle of the beat-to-beat interval is simultaneously the start and end of the dirty interval.
However, as recited as “the first beat” and “the second beat” in lines 3 and 4, it is then unclear if the assumption is made that the marks are in different beat-to-beat intervals, then the first beat and second beat are on either side of the original beat-to-beat interval and not the second beat-to-beat interval. Further, they were originally such that the first beat “belongs to a normal N class” and the second beat “is marked as a dirty beat”. The second-recited first and second beats are conversely recited as first beat = dirty beat and second beat = normal N class.
Looking to Page 11 of Applicant’s specification, with “marking a dirty beat if it does not belong to the N class…and also marks a dirty beat to be the normal beat that succeeds the identified dirty beat”, it is unclear if the intended scope of the claim is to re-identify the previously normal first beat as a dirty beat, since it is beside a dirty beat. Alternatively, if the intended scope is for describing a sliding window of multiple intervals of beat marking along the length of the ECG signal output, with two separate “first” and “second” beats intended to be described. The scope of the claim cannot be ascertained.
Claim 12 recites the term “extracting the clean ECG intervals, wherein the clean ECG intervals are intervals that are not marked as dirty intervals” in lines 12 - 13. It is unclear how many intervals are intended to be extracted, as in line on of the claim “wherein the extracting step is accomplished by using marking dirty intervals”, and the previously-recited extracting step recites “extracting a clean ECG interval”. The metes and bounds of the claim are unclear, such that is unknown if multiple intervals are intended to be extracted, or just one. Looking to the remainder of the limitations in Claim 12, it appears to otherwise be recited in the singular “interval”. Further, for the term “are intervals”, it is unclear if this is intended to be the same or different than the previously-recited “beat-to-beat interval”. For the purposes of examination, the term “extracting the clean ECG interval, wherein the clean ECG interval is an interval that is not marked as the dirty interval” is deemed to claim “extracting the clean ECG interval, wherein the clean ECG interval is the beat-to-beat interval that is not marked as the dirty interval.”
Claim 13 recites the term “of clean ECG intervals” in line 2. It is unclear if these are intended to be the same or different than the “clean ECG interval” previously recited in claim 1. For the purposes of examination, the term “of clean EG intervals” is deemed to claim “of a plurality of the clean ECG interval”.
Claim 13 recites the term “calculating a set of HRV parameters” in line 4. It is unclear if these are intended to be the same or different than the previously-recited set of HRV parameters in the “extended set”. For the purposes of examination, the term “calculating a set of HRV parameters” is deemed to claim “calculating the set of HRV parameters of the extended set of HRV parameters.”
Claim 13 recites the term “on clean ECG intervals” in line 4. It is unclear if this is intended to be the same or different than the previously-recited clean ECG interval. For the purposes of examination, the term “on clean ECG intervals” is deemed to claim “on a plurality of the ECG interval”.
Claim 13 recites the term “calculated on clean ECG intervals” in lines 6 – 7. It is unclear if these are intended to be the same or different than the previously-recited “clean ECG intervals”. Based on the interpretation above, the term “calculated on clean ECG intervals” is deemed to claim “calculated on the plurality of the ECG interval.”
Claim 13 recites the term “concatenating clean ECG intervals” in line 8. It is unclear if these are intended to be the same or different than the previously-recited “clean ECG intervals”. Based the interpretation above, the term “calculated on clean ECG intervals” is deemed to claim “concatenating the plurality of the ECG interval.”
Claim 13 recites the term “calculating the HRV on the long ECG interval” in line 10. There is insufficient antecedent basis for this limitation. It is unclear what HRV is being calculated. For the purposes of examination, the term “calculating the HRV on the long ECG interval” is deemed to claim “calculating a HRV on the long ECG interval”.
Claim 13 recites the term “by using individual or concatenated approaches of clean ECG intervals”. There is no particular “individual” (approach) recited in this claim or Claim 10 from which this claim depends. There is a “concatenated” limitation of “concatenating clean ECG intervals…into a long ECG interval.” With the “or” terminology, it is unclear how to satisfy the metes and bounds of the claim, as the “individual” approach is unclear, and which limitations are associated with the optional “concatenated approach” are also unclear. The metes and bounds of the claim cannot be ascertained.
Claim 14 recites the term “the coverage factor condition” in line 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited coverage factor. For the purposes of examination, the term “the coverage factor condition” is deemed to claim “a coverage factor condition”.
Claim 14 recites the term “including the clean ECG intervals” in line 3. It is unclear if this plurality is intended to be the same or different than the previously-recited clean ECG interval. For the purposes of examination, the term “including the clean ECG intervals” is deemed to claim “including the clean ECG interval.”
Claim 14 recites the term “within the analyzed ECG measurement” in line 3. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited analyzed ECG measurement. The analyzed ECG-associated component in Claim 10, from which this claim depends, is “ECG annotations”. For the purposes of examination, the term “within the analyzed ECG measurement” is deemed to claim “within the analyzed ECG annotations”.
Claim 14 recites the term “specifying a repetitive procedure” in line 7. It is unclear if this is intended to be the same or different than the previously-recited repetitive procedure. For the purposes of examination, the term “specifying a repetitive procedure” is deemed to claim “specifying a second repetitive procedure.”
Claim 14 recites the term “calculates a collection of HRVs” in line 7. It is unclear if the is the same or different than the previously-recited collection of HRVs. For the purposes of examination, the term “calculates a collection of HRVs” is deemed to claim “calculates a second collection of HRVs.”
Claim 14 recites the term “that includes ECG window sizes” in line 8. It is unclear if the is the same or different than the previously-recited predefined ECG window sizes. For the purposes of examination, the term “that includes ECG window sizes” is deemed to claim “that includes the predefined ECG window sizes.”
Claim 14 recites the term “with short and medium term ECG measurements” in lines 8 - 9. It is unclear if the is the same or different than the previously-recited short and medium term ECG measurements. For the purposes of examination, the term “with short and medium term ECG measurements” is deemed to claim “with the short and the medium term ECG measurements”
Claim 14 recites the term “calculating a minor coverage factor as the number of ECG parts that contain at least one clear ECG interval within an analyzed ECG part” in lines 11 - 12. It is unclear with constitutes a “part”. Is this a section of the overall ECG that encompasses multiple intervals? Are they beats? It is unknown if a “part” is intended to be a time segment on an ECG measurement or a label that it put on the annotations such as “Start”. The metes and bounds are unclear.
Claim 14 recites the term “specifying a coverage factor condition by identifying a relevant HRV collection if the minor coverage factor of the major coverage factor are more than half of the number of ECG parts” in lines 16 – 19. This limitation in unclear due to the following:
It is unclear where the “relevant HRV collection” originates, if it is based on either the “collection of HRVs for predefined ECG window sizes” or the “collection of HRVs for predefined offset” (interpreted as “a second collection of HRVs”), or both.
There are two “ifs” for defining how a “relevant HRV collection” is determined. The first “if” is if the minor coverage factor or the major coverage factor include a majority of ECG parts”. It is unclear how a “part” is defined, so determining when more than one part is present is also unclear. Further, there appears to be a word missing between “ECG parts” and “if the minor coverage factor” in lines 17 – 18. It is unknown if this is intended to be an “and” or an “or”. As is, the metes and bounds of the claim are unclear.
For the portion of “if the minor coverage factor of the major coverage factor are more than half of the number of ECG parts” is unclear because the metes and bounds of what constitutes a “part” or a plurality of “parts” of ECG.
For the term “a majority of ECG parts”, it is unclear if they are intended to be the same or different than the “first ECG part” or the ‘number of ECG parts” previously-recited in the claim. Further, as previously-stated, it is unknown if a “part” is intended to be a time segment on an ECG measurement or a label that it put on the annotations.
Claim 14 recites the term “from a collection set” in line 20. It is unclear if a collection set is intended to be the same or different than the previously-recited the “collection of HRVs for predefined ECG window sizes” or the “collection of HRVs for predefined offset” (interpreted as “a second collection of HRVs”), or both, or if it is some other “collection set”.
The term “if the HRV differs from an average behavior” in claim 14 is a relative term which renders the claim indefinite. The term “an average behavior” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, the term “if the HRV differs from an average behavior” is deemed to claim “if the HRV differs from a calculated HRV average.”
Claim 14 recites the term “eliminating a HRV from a collection set if the HRV differs from an average behavior and a range of possible values that are outside a region formed around an average value and a standard deviation of an analyzed HRV”. It is unclear what constitutes “a region formed around an average value”, if it is a time interval on the ECG signal output itself, or if it is a standard deviation of the average HRV. The metes and bounds of the claim are unclear.
Claim 14 recites the term “eliminating the HRV from the collection set” in line 23. It is unclear if a collection set is intended to be the same or different than the previously-recited the “collection of HRVs for predefined ECG window sizes” or the “collection of HRVs for predefined offset” (interpreted as “a second collection of HRVs”), or both, or if it is some other “collection set”. The metes and bounds of the claim are unclear.
Claim 15 recites the term “the step of applying set of threshold decisions for detecting the ability to regulate the glucose level” in lines 1 - 2. There is no previously-recited step matching that recitation in the claims, as there is no previously-recited “ability to regulate the glucose level.” It is unclear if this is intended to be the same or different than the previously-recited predefined glucose level. For the purposes of examination, the term “the step of applying set of threshold decisions for detecting the ability to regulate the glucose level” is deemed to claim “the step of applying the set of threshold decisions for detecting the ability to regulate the predefined glucose level.”
The term “selecting a specific set of HRV parameters from an aggregation of HRV collections” in line 3 of claim 15 is a relative term which renders the claim indefinite. The term “specific” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of what make a set of HRV parameters be specific versus non-specific is unclear. For the purposes of examination, the term “selecting a specific set of HRV parameters from an aggregation of HRV collections” is deemed to claim “selecting a set of HRV parameters from an aggregation of HRV collections.”
Claim 15 recites the term “classifying a bad ability to regulate glucose levels if all HRVs are smaller than a plurality thresholds” in line 4 - 5. It is unclear from where the HRVs come. If they are HRVs of the “set of HRV parameters”, if they are HRVs from the “aggregation of HRV collections” if they are of all of the HRVs measured over some period of time, or what the metes and bounds of “all HRVs”. It is unknown if “all HRVs” would also apply to HRVs measured from multiple subjects. The metes and bounds of the scope of HRVs is unclear.
Claim 15 recites the term “with a predefined confidence” in line 6. It is unclear if this is intended to be the same or different than the previously-recited “predefined confidence”. For the purposes of examination, the term “with a predefined confidence” is deemed to claim “with a second predefined confidence.”
Claim 15 recites the term “if all HRVs are higher than the plurality thresholds”. As above, it is unclear which HRVs are “all HRVs”. Further, it is unclear if this is supposed to be the same or different than the previously recited “all HRVs”. The metes and bounds of the limitation cannot be discerned.
Claim 15 recites the term “determining a number of results if a number of selected HRVs are smaller than the plurality thresholds and classifying the bad ability to regulate glucose levels if the number of results is smaller than the number of selected HRVs”. This limitation is unclear, as it is unclear if the selected HRVs are intended to be part of the “specific set of HRV parameters”, or if they are intended to be different values. Also, there is no previously-recited “number of results”. It is unknown if those are supposed to be HRV parameters or something else. The metes and bounds of the claim cannot be ascertained.
Claim 16 recites the term “wherein the step of calculating the equivalent to HbA1C as a value that expresses the two-month average ability to regulate glucose is accomplished by”. There is no previously-recited “step” with these specifications. The previously recited limitation in Claim 10, from which this claim depends, is “calculating an equivalent to HbA1C and plasma glucose levels. For the purposes of examination, the term “wherein the step of calculating the equivalent to HbA1C as a value that expresses the two-month average ability to regulate glucose is accomplished by” is deemed to claim ““wherein the step of calculating the equivalent to HbA1C comprises calculating a value that expresses a two-month average ability to regulate the plasma glucose level, which is accomplished by”.
The term “overall ability to regulate glucose level” in lines 5 – 6 of claim 16 is a relative term which renders the claim indefinite. The term “overall” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear if “overall” is intended to indicate over the entire time period, or a qualitatively best or worst ability to regulate glucose level among multiple subjects in the study, or something else. Further, it is unclear if “regulate glucose level” is intended to be the same or different than the previously recited “regulate glucose.” Based on the interpretation above, the term “overall ability to regulate glucose level” is deemed to claim “the two-month average ability to regulate the plasma glucose level.”
Claim 17 recites the term “The method of claim 10 wherein the step of calculating the equivalent to instantaneous plasma glucose level is accomplished by”. There is no previously-recited step of this description. For the purposes of examination, the term “The method of claim 10 wherein the step of calculating the equivalent to instantaneous plasma glucose level is accomplished by” is deemed to claim “The method of claim 10, further comprising calculating an equivalent to an instantaneous plasma glucose level by.”
Claim 18 recites the term “wherein the step of forming the reports containing AGP and glucose statistics” in lines 1 - 2. It is unclear if these are intended to be the same or different than the previously-recited AGP and glucose statistics. For the purposes of examination, the term “wherein the step of forming the reports containing AGP and glucose statistics” is deemed to claim ““wherein the step of forming the reports containing the AGP and the glucose statistics.”
The term “selecting a specific time frame” in line 2 of claim 18 is a relative term which renders the claim indefinite. The term “specific” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of what makes a time frame specific as opposed to non-specific are unclear. For the purposes of examination, the term “selecting a specific time frame” is deemed to claim ““selecting a time frame.”
Claim 18 recites the term “selecting a time frame” in line 4. It is unclear if this is intended to be the same or different than the previously-recited time frame. For the purposes of examination, the term “selecting a time frame” is deemed to claim “selecting the time frame.”
Claim 18 recites the term “a time spent below range, a time spent after range”. It is unclear if this is intended to be “above range”. Also the metes and bounds of what the range are unclear, whether the ranges are intended to be the same or different than the previously-recited “normal range”. For the purposes of examination, the term “a time spent below range, a time spent after range” is deemed to claim “a time spent below the normal range, a time spent above the normal range”.
Claim 18 recites the term “ambulatory glucose profile, including a glycemic control index, a glucose management index”. It is unclear if the glycemic control index and the glucose management index are intended to be the same or different metrics. Looking to the Applicant’s specification, there is no particular “glycemic control index”. The closest disclosure is at [Page 6, Lines 11 - 14] and indicates that a degree of glycemic control is “glycosylated hemoglobin HbA1C (often referred to as AlC) that is an indicator of the degree of glycemic control (usually referred to as a measure of the ability to control the blood sugar over a period of about 2 or 3 months)”, which appears to be the same as the glucose management index (GMI) at Applicant’s Specification [Page 1, Line 29 – 31] as “GMI, also known as eA1c (Johnson et. al., 2019) is calculated from a formula…”). It is unclear if the glycemic control index and glucose management index are intended to be different parameters. For the purposes of examination, the term “ambulatory glucose profile, including a glycemic control index, a glucose management index” is deemed to claim “ambulatory glucose profile, including a glucose management index.”
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 10 - 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 10, the claim recites "an act or step, or series of acts or steps" and is therefore a process, which is a statutory category of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of claims 10 – 18 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 10 – 18 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 10 – 18 recites an abstract idea.
Specifically, Claims 10 – 18 recite (underlined are observations, judgments, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 10.
A method of detecting the glucose level by non-invasive method comprising:
analyzing ECG annotations;
extracting a clean ECG interval;
calculating an extended set of HRV parameters;
calculating an aggregation of HRV collections with predefined window sizes and offsets;
applying a set of threshold decisions to detect ability to regulate a predefined glucose level;
calculating an equivalent to HbA 1C and plasma glucose levels;
forming reports containing AGP and achieved glucose statistics.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claim 10, these limitations include:
Observation and judgment of ECG annotations;
Observation and judgment to evaluate a clean ECG signal;
Evaluate an extended set of HRV parameters;
Evaluate an aggregation of HRV collections with predefined window sizes and offsets;
Observation and judgment to apply a set of threshold decisions to detect ability to regulate a predefined glucose level;
Evaluate an equivalent to HbA1C and plasma glucose levels;
all of which are grouped as mental processes or mathematical calculations under the 2019 PEG.
Similarly, Dependent Claims 11 - 18 include the following abstract limitations, in addition the aforementioned limitations in Independent Claim 10 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
identify first beats that do not belong to a normal N class and second beats that succeed said first beats;
Observation and judgment of first beats that do not belong to a normal N class and second beats that succeed said first beats;
identify third beats, wherein said third beats are identified as artifacts or segments with identified noise;
Observation and judgment of third beats, wherein said third beats are identified as artifacts or segments with identified noise
identify fourth beats that succeed third beats;
Observation and judgment of fourth beats that succeed third beats;
identifying fifth beats where beat-to-beat interval differs from a previous beat-to-beat interval for a factor larger than a predefined threshold;
Observation and judgment of fifth beats where beat-to-beat interval differs from a previous beat-to-beat interval for a factor larger than a predefined threshold;
identifying sixth beats that succeed the fifth beats;
Observation and judgment of sixth beats that succeed the fifth beats;
identifying seventh beats that are not marked as dirty, wherein a longest continuous sequence is smaller than a predefined threshold.
Observation and judgment of seventh beats that are not marked as dirty, wherein a longest continuous sequence is smaller than a predefined threshold.
mark a start of a dirty interval in a middle of a beat-to-beat interval, wherein a first beat belongs to a normal N class, wherein a second beat is marked as a dirty beat;
Observation and judgment to mark a start of a dirty interval in a middle of a beat-to-beat interval, wherein a first beat belongs to a normal N class, wherein a second beat is marked as a dirty beat;
mark the start of the dirty interval in a location after a T wave of a first beat and before a P wave of a second beat;
Observation and judgment to mark the start of the dirty interval in a location after a T wave of a first beat and before a P wave of a second beat;
mark an end of the dirty interval in the middle of the beat-to-beat interval, where the first beat is marked as the dirty beat, and the second beat belongs to the normal N class;
Observation and judgment to mark an end of the dirty interval in the middle of the beat-to-beat interval, where the first beat is marked as the dirty beat, and the second beat belongs to the normal N class;
mark the end of the dirty interval in the location after the T wave of the first beat and before the P wave of the second beat;
Observation and judgment to mark the end of the dirty interval in the location after the T wave of the first beat and before the P wave of the second beat;
extract the clean ECG intervals, wherein the clean ECG intervals are intervals that are not marked as dirty intervals.
Observation and judgment to evaluate the clean ECG intervals, wherein the clean ECG intervals are intervals that are not marked as dirty intervals.
calculate the extended set of HRV parameters using individual or concatenated approaches of clean ECG intervals
evaluate the extended set of HRV parameters using individual or concatenated approaches of clean ECG intervals
calculate a set of HRV parameters on the clean ECG intervals within an analyzed ECG measurement window;
evaluate a set of HRV parameters on the clean ECG intervals within an analyzed ECG measurement window;
calculate statistical operations on the set of HRV parameters calculated on clean ECG intervals;
evaluate statistical operations on the set of HRV parameters calculated on clean ECG intervals;
concatenate clean ECG intervals from the analyzed ECG measurement window into a long ECG interval;
Observation and judgment to concatenate clean ECG intervals from the analyzed ECG measurement window into a long ECG interval;
calculate the HRV on the long ECG interval.
evaluate the HRV on the long ECG interval.
calculate the aggregation of HRV collections using a sliding window approach satisfying the coverage factor condition of including the clean ECG intervals within the analyzed ECG measurement,
evaluate the aggregation of HRV collections using a sliding window approach satisfying the coverage factor condition of including the clean ECG intervals within the analyzed ECG measurement,
specify a repetitive procedure that calculates a collection of HRVs for predefined ECG window sizes with short, medium, long and extra-long term ECG measurements;
Observation and judgment to specify a repetitive procedure that evaluates a collection of HRVs for predefined ECG window sizes with short, medium, long and extra-long term ECG measurements;
specify a repetitive procedure that calculates a collection of HRVs for predefined offset in respect to an analyzed time moment that includes ECG window sizes with short and medium term ECG measurements corresponding to a predefined first duration and a predefined second duration;
Observation and judgment to specify a repetitive procedure that evaluates a collection of HRVs for predefined offset in respect to an analyzed time moment that includes ECG window sizes with short and medium term ECG measurements corresponding to a predefined first duration and a predefined second duration;
calculate a minor coverage factor as a number of ECG parts that contain at least one clean ECG interval within an analyzed ECG part;
evaluate a minor coverage factor as a number of ECG parts that contain at least one clean ECG interval within an analyzed ECG part;
calculate a major coverage factor as the number of ECG parts where a time duration of clean ECG intervals within a first ECG part cover more than half of a time duration of the analyzed ECG part;
evaluate a major coverage factor as the number of ECG parts where a time duration of clean ECG intervals within a first ECG part cover more than half of a time duration of the analyzed ECG part;
specify a coverage factor condition by identifying a relevant HRV collection if a minor coverage factor or the major coverage factor include a majority of ECG parts if a minor coverage factor or the major coverage factor are more than a half of the number of ECG parts;
Observation and judgment to specify a coverage factor condition by identifying a relevant HRV collection if a minor coverage factor or the major coverage factor include a majority of ECG parts if a minor coverage factor or the major coverage factor are more than a half of the number of ECG parts;
eliminate a HRV from a collection set if the HRV differs from an average behavior and a range of possible values that are outside a region formed around an average value and a standard deviation of an analyzed HRV;
Observation and judgment to eliminate a HRV from a collection set if the HRV differs from an average behavior and a range of possible values that are outside a region formed around an average value and a standard deviation of an analyzed HRV;
eliminate the HRV from the collection set by applying an IQR, Z score.
Observation and judgment to eliminate the HRV from the collection set by applying an IQR, Z score.
Applying a set of threshold decisions for detecting the ability to regulate the glucose level, wherein the personal device is configured to
Observation and judgment to apply a set of threshold decisions for detecting the ability to regulate the glucose level, wherein the personal device is configured to
select a specific set of HRV parameters from an aggregation of HRV collections;
Observation and judgment to select a specific set of HRV parameters from an aggregation of HRV collections;
classify a bad ability to regulate glucose levels with a predefined confidence if all HRVs are smaller than a plurality of thresholds;
Observation and judgment to classify a bad ability to regulate glucose levels with a predefined confidence if all HRVs are smaller than a plurality of thresholds;
classify a good ability to regulate glucose levels with a predefined confidence if all HRVs are higher than the plurality of thresholds;
Observation and judgment to classify a good ability to regulate glucose levels with a predefined confidence if all HRVs are higher than the plurality of thresholds;
determine a number of results if a number of selected HRVs are smaller than the plurality of thresholds and classifying the bad ability to regulate glucose levels if the number of results is smaller than the number of selected HRVs.
Observation and judgment to evaluate a number of results if a number of selected HRVs are smaller than the plurality of thresholds and classifying the bad ability to regulate glucose levels if the number of results is smaller than the number of selected HRVs.
calculate the equivalent to HbA1C as a value that expresses the two-month average ability to regulate glucose is accomplished by calculating a regression function or any other mathematical, computer or data science functional method of selected dominant HRVs
evaluate the equivalent to HbA1C as a value that expresses the two-month average ability to regulate glucose is accomplished by calculating a regression function or any other mathematical, computer or data science functional method of selected dominant HRVs
select a set of dominant long and extra-long term HRVs that impact overall ability to regulate glucose level;
Observation and judgment to select a set of dominant long and extra-long term HRVs that impact overall ability to regulate glucose level;
specify a processing procedure based on applying weighting factors to selected HRVs.
Observation and judgment to specify a processing procedure based on applying weighting factors to selected HRVs.
calculate the equivalent to an instantaneous plasma glucose level by calculating a regression function or any other mathematical, computer or data science functional method of selected dominant HRVs,
evaluate the equivalent to an instantaneous plasma glucose level by calculating a regression function or any other mathematical, computer or data science functional method of selected dominant HRVs,
select an aggregation of dominant short and medium term HRV collections with different window sizes and different offsets to the analyzed time moment that impact the instantaneous plasma glucose level;
Observation and judgment to select an aggregation of dominant short and medium term HRV collections with different window sizes and different offsets to the analyzed time moment that impact the instantaneous plasma glucose level;
specify a processing procedure based on applying weighting factors to selected HRVs.
Observation and judgment to specify a processing procedure based on applying weighting factors to selected HRVs.
form the reports containing AGP and glucose statistics by selecting a specific time frame and calculating methods to calculate average glucose behavior and variability indexes and coefficients
Observation and judgment to form the reports containing AGP and glucose statistics by selecting a specific time frame and calculating methods to evaluate average glucose behavior and variability indexes and coefficients
select a time frame to analyze instantaneous plasma glucose levels;
Observation and judgment to select a time frame to analyze instantaneous plasma glucose levels;
calculate the average glucose behavior;
evaluate the average glucose behavior;
calculate an average behavior of glucose statistics and an ambulatory glucose profile, including a glycemic control index, a glucose management index, a glucose variability, a coefficient of glucose variation (CV), a time spent in normal range, a time spent below range, a time spent after range, and a mean amplitude of glycemic excursions (MAGE).
evaluate an average behavior of glucose statistics and an ambulatory glucose profile, including a glycemic control index, a glucose management index, a glucose variability, a coefficient of glucose variation (CV), a time spent in normal range, a time spent below range, a time spent after range, and a mean amplitude of glycemic excursions (MAGE).
all of which are grouped as mental processes or mathematical calculations under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claim 10 (and their respective Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in Independent Claim 10), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional pre-solution activity elements of:
form reports containing AGP and achieved glucose statistics.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps. These pre-solution data gathering steps are not recited to improve the functioning of a computer, or any other technology or technical field.
Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Independent Claim 10 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claim 10 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 10 – 18 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
There are no particular additional hardware elements recited in Claims 10 – 18.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method of Claims 10 - 18 are directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 10 - 18 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in Independent Claim 10 (and its dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 10 - 18 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 10 - 18 amounts to significantly more than the abstract idea itself. Accordingly, Claims 10 - 18 are not patent eligible and are rejected under 35 U.S.C. 101
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.
Claims 10 and 17 - 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cichosz in view of Johnson et. al., (“Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice”, Ref U on PTO-892)
Regarding Claim 10, Cichosz discloses analyzing ECG annotations (Figure 1 “Feature extraction…”; [Page 732, “Preprocessing” section] “ECG signal was used for detection of peaks and calculation of RR intervals…”)
extracting a clean ECG interval (Figure 1 “Feature extraction…”; [Page 732, “Preprocessing” section] “excluded 2 sessions…ECG signals…unusable noisy ECG signals”, “ECG signal was used…RR intervals”)(Examiner notes that the “unusable noisy ECG signals” were rejected, with “clean” signals that were not noisy remaining.);
calculating an extended set of HRV parameters (Figure 1 “Feature extraction…”; [Page 732, “Preprocessing” section] “detection of peaks and calculation of RR intervals…RR intervals were divided in epochs of 5 minutes”; Table 1; Figure 1; [Page 733, Left Column, Top] “Examples of discriminative features…median heart rate averaged from epochs 10 – 20 prior to a sample.”)(Examiner notes that the set of HRV parameters extended over at least 5 minutes duration for each epoch with at least 10 – 20 previously-recorded epochs (50 – 100 minutes).)
calculating an aggregation of HRV collections with predefined window sizes (Figure 1 “Feature extraction…”; [Page 732, “Preprocessing” section] “…RR intervals were divided in epochs of 5 minutes”)(Examiner notes that windows sizes of epochs were predefined at 5 minutes.) and offsets (Figure 1 “Feature extraction…”; [Page 732, “Preprocessing” section] “…outliers were defined as RR intervals deviating 50% from previous RR interval…”)(Examiner notes that the offset for the aggregation for outliers is 1, as it is evaluated based on the interval that is offset 1 interval prior.)
applying a set of threshold decisions to detect ability to regulate a predefined glucose level (Figure 1; [Page 733, Left Column, Bottom] “…find preferably small numbers of features that are particularly distinguishing or informative for the classification…”; Figure 3; [Page 733, “Algorithm performance” section] “The algorithm…make early prediction of a hypoglycemic event, up to 10 minutes prior to the blood reference reaching the predefined hypoglycemic level of 3.9 mmol/L”; [Page 732, Left Column, Paragraph, 4th Full Paragraph] “HRV patterns in combination with CGM data could be used as an improved method to real-time early detection of hypoglycemia.”);
calculating an equivalent to HbA1C and plasma glucose levels (Figure 2, Plasma glucose levels”; Table 2, “HRV/CGM algorithm”; Figure 1; [Abstract] “features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L.”; Figure 1; [Page 732, Left Column] “…mean HbA1c7.4 ± 0.9% (57 mmol/mol).”)(Examiner notes that each of the participants in the study from which the hypoglycemia is determined have a calculated HbA1c level. There is nothing particularly recited about when the calculation occurs or for how many subjects.)
forming reports containing AGP and achieved glucose statistics (Reporting shown in Figure 4, Table 2, Table 1; [Page 734, “Statistics” section, Paragraph 1 - 2] “…classical statistics to test results for the proposed algorithm and that of CGM alone…”; Figure 3; Overall forming a report as the full disclosure as submitted to a journal, Journal of Diabetes Science and Technology.)(Examiner notes that the limitation broadly includes statistics that would be in an AGP report (average glucose level over a period of time, where the period of time is reported) and statistics of achieved glucose (average blood glucose levels, hypoglycemia events, etc.). As described above, there is nothing particularly recited in the claim that indicates that this is a the full standardized, one-page glucose report or that it is derived from HRV and not CGM, just that reports are made, which broadly includes reporting statistics in figures and text in a published journal.)
Cichosz broadly discloses forming reports containing AGP and achieved glucose statistics, as described above. For a more particular teaching of an AGP report, Johnson teaches strategies to use the ambulatory glucose profile (AGP) as a standard of care for individuals with diabetes with continuous glucose monitoring [Abstract]. Specifically for Claim 10, Johnson teaches forming reports containing AGP (Fig 1, “AGP Report”).
Johnson provides a motivation to combine at [Page S2-18, Left Column, Paragraph 2] with “The ambulatory glucose profile (AGP) is a standardized, single-page glucose report designed to simplify analysis and interpretation of downloaded CGM data,” and [Page S2-20], Paragraphs 2 – 3] “…referenced as an example of a standardized CGM report in the ADA 2019 Standards of Care…Most of the CGM device manufacturers now include variations of the AGP format in the download software”. A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that including downloaded CGM data in the standardized, single-page glucose report format that comes standard with “most of the CGM manufacturers” devices would be useful to include among any glucose-statistic data, in order to meet a normal standard of care and comparison of data between subjects. As claimed, the AGP report can be broadly included as additional standard-of-care information obtained from a CGM in order to help validate an experimental HRV-associated glucose determination device. Cichosz references at [Page 732, 5th Full Paragraph] that “The present study investigated whether HRV patterns combined with CGM data could improve the accuracy of hypoglycemia as detected by a CGM device.“ Including AGP information from the CGM data would also help improve data trend analysis.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of using HRV patterns combined with CGM data to determine more accurate glucose information disclosed by Cichosz with AGP report information (a standardized CGM report in the ADA 2019 Standards of Care included with most CGM devices) taught by Johnson, creating a single method of using HRV patterns combined with comprehensive CGM data, including ambulatory glucose profile reporting, which would be useful to build a more accurate view of a patient’s glucose regulation abilities.
Regarding Claim 17, Cichosz in view of Johnson discloses as described above, The method in claim 10. For the remainder of Claim 17, Cichosz discloses wherein the step of calculating the equivalent to instantaneous plasma glucose level is accomplished by calculating a regression function ([Page 732, Right Column, 3rd Full Paragraph] “…models…used to estimate power spectral density…auto regression…”; [Page 733, Right Column, 2nd Full Paragraph] “A binary linear logistic regression classifier was used to classify the samples into either Cn or Chy …”)(Examiner notes than Cn is “normal glucose level” and Chy is hypoglycemia.”)
or any other mathematical, computer or data science functional method of selected dominant HRVs comprising: selecting an aggregation of dominant short and medium term HRV collections with different window sizes and different offsets to the analyzed time moment that impact the instantaneous plasma glucose level; specifying a processing procedure based on applying weighting factors to selected HRVs ([Page 733, Right Column, 2nd Full Paragraph] “A ranking algorithm…and intercorrelation weighting of features was used to eliminate uninformative features.”).
Examiner notes that as recited, it appears that the limitations of the claim are satisfied if the metric is either calculated by 1. using a regression, or 2. calculated using any other mathematical, computer or data science functional method of selected dominant HRVs comprising, where the method of selected dominant HRVs includes selecting an aggregation of dominant short and medium term HRV collections with different window sizes and different offsets to the analyzed time moment that impact the instantaneous plasma glucose level and specifying a processing procedure based on applying weighting factors to selected HRVs.) As such, the metes and bounds appear to be met by calculating a regression function without the remaining limitations of the claim.
Regarding Claim 18, Cichosz in view of Johnson discloses as described above, The method in claim 10. For the remainder of Claim 18, Cichosz discloses wherein the step of forming the reports containing AGP and glucose statistics (See citation above in Claim 10) is accomplished by
selecting a specific time frame and calculating methods to calculate average glucose behavior (Figure 4, “Average Plot” blood glucose from time = 100 mins – 400 minutes) and variability indexes (Table 1, “Heart Rate Variability (HRV) Measures”; [Page 734, Left Column, 3rd Full Paragraph] “Coefficient of variation (CV) was calculated…”) and coefficients [Page 734, Left Column, 3rd Full Paragraph] “Coefficient of variation (CV) was calculated…”) comprising
selecting a time frame to analyze instantaneous plasma glucose levels (Figure 4, “Average Plot” blood glucose from time = 100 mins – 400 minutes);
calculating the average glucose behavior (Figure 4, “Average Plot” blood glucose from time = 100 mins – 400 minutes);
calculating an average behavior of glucose statistics (Figure 4, “Average Plot” blood glucose from time = 100 mins – 400 minutes)
Cichosz does not particularly teach and ambulatory glucose profile, including a glycemic control index, a glucose management index, a glucose variability, a coefficient of glucose variation (CV), a time spent in normal range, a time spent below range, a time spent after range, and a mean amplitude of glycemic excursions (MAGE).
Johnson teaches ambulatory glucose profile (Table 1, “AGP, ambulatory glucose profile”, “Standardized CGM report”) including a glycemic control index (Fig 1, “key characteristics of glycemic control (e.g., time in range and glycemic variability). AGP, ambulatory glucose profile.”; Table 1, “GMI (previously eA1c)”)(Examiner notes that Applicant’s specification at [Page 6, Lines 11 - 14] that indicates that a degree of glycemic control is “glycosylated hemoglobin HbA1C (often referred to as AlC) that is an indicator of the degree of glycemic control (usually referred to as a measure of the ability to control the blood sugar over a period of about 2 or 3 months)”. For a general index of glycemic control, key characteristics are provided.), a glucose management index (Table 1, “GMI (previously eA1c)”), a glucose variability (Table 1, “GV, glucose variability”), a coefficient of glucose variation (CV) (Table 1, “%CV, percent coefficient of variation”), a time spent in normal range (Table 1, “Percentage of TIR”) a time spent below range (Table 1, “Percentage of time in hypoglycemic ranges…”), a time spent after range (Table 1, “Percentage of time in hyperglycemic ranges…”), and a mean amplitude of glycemic excursions (MAGE).
The motivation for Claim 18 to combine Cichosz with Johnson is the same as that described in more detail in Claim 10. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of using HRV patterns combined with CGM data to determine more accurate glucose information disclosed by Cichosz with AGP report information (a standardized CGM report in the ADA 2019 Standards of Care included with most CGM devices) taught by Johnson, creating a single method of using HRV patterns combined with comprehensive CGM data, including ambulatory glucose profile reporting, which would be useful to build a more accurate view of a patient’s glucose regulation abilities.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Cichosz in view of Johnson, further in view of Vanvinckenroye et. al., (US 2023/0054041 A1).
Regarding Claim 11, Cichosz in view of Johnson discloses as described above, The method in claim 10. For the remainder of Claim 11, Cichosz discloses wherein the extracting step is accomplished by using marking of dirty beats (Fig 7a ECG, “UD”, “IQ”), wherein using marking of dirty beats comprising
identifying first beats that do not belong to a normal N class (Fig 7a ECG, 1st “UD” on the left) and second beats that succeed said first beats (Fig 7a ECG, 1st “SR” after the “UD” from left to right);
identifying third beats identified as artifacts or segments with identified noise (Fig 7a ECG, 1st “IQ” from left to right);
identifying fourth beats that succeed second beats (Fig 7a ECG,, any beats after the 1st “SR” beats, including as an example the 3rd “SR” section from left to right);
identifying fifth beats where beat-to-beat interval differs from a previous beat-to-beat interval for a factor larger than a predefined threshold (Fig 7a ECG, 2nd “IQ” section from left to right, differs from the previous SR section”; [0001] “ (regular sine rhythm and various irregular rhythm annotations)…“; [0041] “when a normal sine rhythm is interrupted by a premature cardiac contraction…a visible premature beat in the ECG signal”)
identifying sixth beats that succeed the fifth beats (Fig 7a ECG, any beats after the 2nd “IQ” section, including as an example the 3rd “SR” section from left to right);
identifying seventh beats that are not marked as dirty (Fig 7a ECG, 2nd “SR” section from left to right), wherein a longest continuous sequence is smaller than a predefined threshold ([0053] “the local maximum is preferably searched for in a small window, for instance having a total length of 100 milliseconds, i.e. 50 milliseconds left of (before) and 50 milliseconds right of (after) the ECG beat. “)(Examiner notes that the sequence is the interval in which a beat is searched for.).
Vanvinckenroye provides a motivation to combine at [0028] with “The ECG signal is annotated either algorithm-based or expert-based…based on the expertise of one or plural people like cardiologists, medical technicians, data scientists, nurses, etc. The segments can be fixed length segments like for instance segments of 1 second, or variable length segments like for instance inter beat intervals…” and [0069] “obtaining high-quality annotations for training a neural network/machine learning model”. A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that ECG data annotation could be performed automatically or manually by a medical professional in order to investigate information about the signal and possibly use that information in an algorithm, such as training a neural network or machine learning model.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the ECG data obtained for HRV investigation with an algorithm using parts of the ECG data taught by Cichosz with the ECG annotation taught by Vanvinckenroye, creating a single method of obtaining HRV patterns from detailed ECG annotation techniques, in order to obtain more precise ECG feature identification for further algorithmic use (such as in determining glucose levels from the HRV patterns).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Cichosz in view of Johnson, further in view of Fabris et. al., (“Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?”, Ref V on PTO 892)
Regarding Claim 16, Cichosz in view of Johnson discloses as described above, The method in claim 10. For the remainder of Claim 16, Cichosz discloses wherein the step of calculating the equivalent to HbA1C (See citation in Claim 10)(Examiner notes the 112(b) interpretation above of this term as wherein the step of calculating the equivalent to HbA1C and plasma glucose levels comprises calculating a value that expresses a two-month average ability to regulate the plasma glucose level, which is accomplished by...”) comprises calculating a value that expresses the two-month average ability to regulate glucose ([Page 732, Left Column] “…mean HbA1c7.4 ± 0.9% (57 mmol/mol).”)
specifying a processing procedure based on applying weighting factors to selected HRVs ([Page 733, Right Column, 2nd Paragraph] “intercorrelation weighting of features was used to eliminate uninformative features.”).
Cichosz does not particularly teach calculating a value that expresses the two-month average ability to regulate glucose by calculating a regression function, or by any other mathematical, computer or data science functional method of selected dominant HRVs comprising selecting a set of dominant long and extra-long term HRVs that impact overall ability to regulate glucose level. Cichosz does broadly disclose calculating a regression function regarding the glucose level ([Page 732, Right Column, 3rd Full Paragraph] “…models…used to estimate power spectral density…auto regression…”; [Page 733, Right Column, 2nd Full Paragraph] “A binary linear logistic regression classifier was used to classify the samples into either Cn or Chy …”)(Examiner notes than Cn is “normal glucose level” and Chy is hypoglycemia.”) and
Fabris teaches a method for estimating HbA1c from continuous glucose monitoring data using a regression function. Specifically for Claim 16, Fabris teaches calculating a value that expresses the two-month average ability to regulate glucose ([Page 502, Left Column, 2nd Full Paragraph] “estimate HbA1c…”), which is accomplished by calculating a regression function ([Page 502, Left Column, 2nd Full Paragraph] “estimate HbA1c from self-monitoring of BG or CGM measurements…”, “Bergenstal and coworkers introduced a new metric for converting CGM-derived mean BG to HbA1c units, which was named “glucose management indicator…The formula to compute GMI was obtained by regressing central laboratory-measured HbA1c on CGM-derived average BG using a data set comprising 528 mean BG-HbA1c pairs.”)
or any other mathematical, computer or data science functional method of selected dominant HRVs comprising: selecting a set of dominant long and extra-long term HRVs that impact overall ability to regulate glucose level; specifying a processing procedure based on applying weighting factors to selected HRVs. (As recited, it appears that the limitations of the claim are satisfied if the metric is either calculated by 1. using a regression, or 2. calculated using any other mathematical, computer or data science functional method of selected dominant HRVs comprising, where the method of selected dominant HRVs includes selecting a set of dominant long and extra-long term HRVs that impact overall ability to regulate glucose level and specifying a processing procedure based on applying weighting factors to selected HRVs.)
Fabris provides a motivation to combine at [Page 502, Left Column, 2nd Full Paragraph] with “Owing to these possible discrepancies between HbA1c and average glycemic levels, mathematical models have been developed to estimate HbA1c from self-monitoring of BG or CGM measurements...a new metric for converting CGM-derived mean BG to HbA1c units, which was named ‘‘glucose management indicator’’ (GMI).” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that incorporating a regression equation to correlate HbA1c levels and CGM measurements would be useful for building a more-detailed dataset of glucose regulation for a subject over time. As claimed, the two-month average ability to regulate glucose can be broadly included as additional glucose-related information obtained from a CGM in order to help validate an experimental HRV-associated glucose determination device. Cichosz references at [Page 732, 5th Full Paragraph] that “The present study investigated whether HRV patterns combined with CGM data could improve the accuracy of hypoglycemia as detected by a CGM device.“ Including regression-based HbA1c information from the CGM data would also help improve data trend analysis.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of using HRV patterns combined with CGM data to determine more accurate glucose information disclosed by Cichosz with the calculated HbA1c based on regression and CGM blood glucose data taught by Fabris, creating a single method of using HRV patterns combined with comprehensive CGM data, including HbA1c from regression calculation of CGM data, which would be useful to build a more accurate view of a patient’s glucose regulation abilities.
Conclusion
In light of the current 112(b) rejections, no prior art rejection is currently able to be applied to Claims 12 – 15.
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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