DETAILED ACTION
Applicant’s arguments, filed on 03/04/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 03/04/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 21-38 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 36 is objected to because of the following informalities:
In claim 36, line 3, “a corresponding carbohydrate peak response” should read “the corresponding carbohydrate peak response”
Appropriate correction is required.
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 21-38 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. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 21-38 are all within at least one of the four statutory categories.
Claim 21 and its dependent claims disclose a system (machine).
Regarding Step 2A, Prong One, the independent claim 21 recites an abstract idea. In particular, the claim generally recites the following:
determine time derivatives of a plurality of data points corresponding to the data indicative of the glucose level;
determine, based on the time derivatives, a data set comprising a plurality of carbohydrate intake start candidates and a plurality of carbohydrate peak response candidates of a plurality of carbohydrate peaks;
determine whether each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates;
for each carbohydrate intake start candidate that has a corresponding carbohydrate peak response candidate, pair the each carbohydrate intake start candidate with the corresponding carbohydrate peak response candidate;
for each carbohydrate intake start candidate that does not have a corresponding carbohydrate peak response candidate, remove the each carbohydrate intake start candidate from the data set.
These elements recites in claim 21 are drawn to abstract ideas since they involve a mental process than can be practically performed in the human mind including observation, evaluation, judgement, and using pen and paper.
Determining time derivatives of a plurality of data points corresponding to the data indicative of the glucose level is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind or with the aid of pen and paper. A person of ordinary skill in the art could reasonably determine a time derivative from the data points through observation and judgement mentally. This process is based on observation, evaluation, and mathematical principles, which can be performed by hand. The mathematics and evaluation are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in determining time derivatives of a plurality of data points corresponding to the data indicative of the glucose level.
Determining, based on the time derivatives, a data set comprising a plurality of carbohydrate intake start candidates and a plurality of carbohydrate peak response candidates is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably determine the data set from the time derivatives through observation and judgement mentally. This process is based on observation, evaluation, and mathematical principles, which can be performed by hand. The mathematics and evaluation are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in determining, based on the time derivatives, a data set comprising a plurality of carbohydrate intake start candidates and a plurality of carbohydrate peak response candidates.
Determining whether each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably determine if the carbohydrate intake start candidates have a corresponding carbohydrate peak response candidate through observation and judgement mentally. This process is based in observation and evaluation, which a person can perform in their mind. There is nothing to suggest an undue level of complexity in determining whether each carbohydrate intake start candidate of the plurality of carbohydrate start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates.
For each carbohydrate intake start candidate that has a corresponding carbohydrate peak response candidate, pair the each carbohydrate intake start candidate with the corresponding carbohydrate peak response candidate is drawn to an abstract idea since it is a mental process that can practically be performed in the human mind or with the aid of pen and paper. A person of ordinary skill in the art could reasonably pair the carbohydrate intake start candidate with the corresponding carbohydrate peak response candidate mentally. There is nothing to suggest an undue level of complexity in pairing the each carbohydrate intake start candidate with the corresponding carbohydrate peak response candidate for each carbohydrate intake start candidate that has a corresponding carbohydrate peak response candidate.
For each carbohydrate intake start candidate that does not have a corresponding carbohydrate peak response candidate, remove the each carbohydrate intake start candidate from the data set is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind or with the aid of pen and paper. A person of ordinary skill in the art could reasonably remove each carbohydrate intake start candidate from the data set if it does not have a corresponding carbohydrate peak response candidate mentally. There is nothing to suggest an undue level of complexity in removing the each carbohydrate intake start candidate from the data set for each carbohydrate intake start candidate that does not have a corresponding carbohydrate peak response candidate.
Regarding Step 2A, Prong Two, claim 21 does not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a receiver unit comprising wireless communications circuitry configured to receive the data indicative of the glucose level, the receiver unit further comprising one or more processors coupled with a memory, the memory configured to store instructions that, when executed by the one or more processors, cause the one or more processors”), and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g., “an on-body device comprising sensor electronics coupled with a glucose sensor, wherein the glucose sensor comprises a portion configured to be positioned through skin of a user and in contact with interstitial fluid, wherein the portion is further configured to sense a glucose level in the interstitial fluid, and wherein the on-body device is configured to transmit data indicative of the glucose level”)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Regarding Step 2B, claim 21 does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Claim 21 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, “an on-body device comprising sensor electronics coupled with a glucose sensor, wherein the glucose sensor comprises a portion configured to be positioned through skin of a user and in contact with interstitial fluid, wherein the portion is further configured to sense a glucose level in the interstitial fluid, and wherein the on-body device is configured to transmit data indicative of the glucose level” does not qualify as significantly more because this limitation merely describes the generic data-gathering device of a glucose sensor.
The data gathering step of “an on-body device comprising sensor electronics coupled with a glucose sensor, wherein the glucose sensor comprises a portion configured to be positioned through skin of a user and in contact with interstitial fluid, wherein the portion is further configured to sense a glucose level in the interstitial fluid, and wherein the on-body device is configured to transmit data indicative of the glucose level” is nothing more than a generic glucose sensor. Such sensors are evidenced by:
US Patent Application Publication No. 20140066892 (Keenan) discloses these types of glucose sensors as conventional (Keenan, [0381]);
US Patent Application Publication No. 20140005509 (Bhavaraju) discloses implantable glucose sensors as conventional (Bhavaraju, [0004]);
US Patent No. 8517941 (Wenzel) discloses conventional glucose sensors that are implantable (Wenzel, Column 12, lines 18-22);
US Patent Application Publication No. 20100317952 (Budiman) discloses conventional monitoring of glucose by implanted glucose sensors (Budiman, [0041]).
Further, the elements of “a receiver unit comprising wireless communications circuitry configured to receive the data indicative of the glucose level, the receiver unit further comprising one or more processors coupled with a memory, the memory configured to store instructions that, when executed by the one or more processors, cause the one or more processors” in claim 21 do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above judicial exception. Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Regarding the dependent claims, claims 22-38 depend on claim 21. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known to the industry.
For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind:
(Claim 22): “wherein the plurality of carbohydrate peak response candidates comprise a first carbohydrate peak response candidate and a second carbohydrate peak response candidate, wherein the first carbohydrate peak response candidate is adjacent to the second carbohydrate peak response candidate, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: remove the first carbohydrate peak response candidate in response to a determination that the second carbohydrate peak response candidate has a larger glucose level than the first carbohydrate peak response candidate” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 23): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates or one or more carbohydrate peak response candidates from the plurality of carbohydrate peak response candidates that comprise a signal artifact, and remove the one or more carbohydrate intake start candidates or the one or more carbohydrate peak response candidates that comprise the signal artifact” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 24): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates having a proximity or a level drop relative to an adjacent carbohydrate peak response candidate from the plurality of carbohydrate intake start candidates that is below a predetermined threshold; and remove the one or more carbohydrate intake start candidates that has the proximity or the level drop relative to the adjacent carbohydrate peak response candidate that is below the predetermined threshold” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 25): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates or carbohydrate peak response candidates from the plurality of carbohydrate peak response candidates that comprise a difference in amplitude below a predetermined level; and remove the one or more carbohydrate intake start candidates or carbohydrate peak response candidates that comprise the difference in amplitude below the predetermined level” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 26): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: refine the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 27): “wherein the receiver unit further comprises a display, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a carbohydrate intake start event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; and output an indication associated with the carbohydrate intake start event on the display” further defines the abstract idea since it can be performed mentally through evaluation and judgement, and is insignificant post-solution activity;
(Claim 28): “wherein the carbohydrate intake start event comprises a meal start event” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 29): “wherein the receiver unit further comprises a display, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a peak carbohydrate response event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; and output an indication associated with the peak carbohydrate intake response event on the display” further defines the abstract idea since it can be performed mentally through evaluation and judgement and is insignificant post-solution activity;
(Claim 30): “wherein the peak carbohydrate intake response event includes a peak meal response event” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 31): “wherein the receiver unit further comprises a display, wherein the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates is utilized to confirm an absence or a presence of one or more meal tags manually entered by the user on the receiver unit” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 32): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a carbohydrate intake start event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; and determine a peak carbohydrate response event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; wherein the carbohydrate intake start event and the peak carbohydrate response event provide one or more meal times that can be utilized to confirm an absence or a presence of one or more meal tags to determine a glycemic model” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 33): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: condition the plurality of data points corresponding to the data indicative of the glucose level over a first time period” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 34): “wherein time derivatives of the plurality of data points are determined based on the conditioned plurality of data points” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 35): “wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: remove any outlier data from the plurality of data points corresponding to the data indicative of the glucose level; and smooth the plurality of data points” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 36): “wherein determining whether each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates comprises determining an optima of acceleration” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 37): “wherein the optima of acceleration is based on the determined time derivatives” further defines the abstract idea since it can be performed mentally through evaluation and judgement;
(Claim 38): “wherein determining the optima of acceleration based on the time derivatives includes determining acceleration of the plurality of data points based on the time derivatives” further defines the abstract idea since it can be performed mentally through evaluation and judgement.
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 21-38 are rejected as being directed to non-statutory subject matter.
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 21-30 and 33-38 are rejected under 35 U.S.C. 103 as being unpatentable over Taub (WO 2010054408) in further view of Lopatka (“Probabilistic peak detection for first-order chromatographic data”).
Regarding independent claim 21, Taub teaches a glucose monitoring system for algorithmically removing one or more false carbohydrate intake start candidates and false carbohydrate peak response candidates (Page 2: “Certain in vivo analyte sensors experience signal dropout conditions or false indications of the detected signal level, where the signal output indication does not correlate with the actual monitored or detected analyte level”; Abstract: “A method including receiving a data stream including a plurality of monitored analyte signals from an analyte monitoring device, performing a recursive signal processing routine on the received data stream to detect a signal dropout condition in the data stream, generating a signal dropout condition notification when the signal dropout condition is detected, applying one or more dropout confirmation routines to the data stream to confirm the detected dropout condition, outputting a notification associated with the confirmed dropout condition, and modifying one or more operational parameters of the analyte monitoring device when the detected dropout condition is confirmed is disclosed. Devices, systems and kits incorporating the method are also disclosed.”), the glucose monitoring system comprising:
an on-body device comprising sensor electronics coupled with a glucose sensor (Page 6: “Embodiments relate to the continuous and/or automatic in vivo monitoring of the level of one or more analytes using a continuous analyte monitoring system that includes an analyte sensor for the in vivo detection, of an analyte, such as glucose, lactate, and the like, in a body fluid. Embodiments include wholly implantable analyte sensors and analyte sensors in which only a portion of the sensor is positioned under the skin and a portion of the sensor resides above the skin, e.g., for contact to a control unit, transmitter, receiver, transceiver, processor, etc. At least a portion of a sensor may be, for example, subcutaneously positionable in a patient for the continuous or semi-continuous monitoring of a level of an analyte in a patient's interstitial fluid.”), wherein the glucose sensor comprises a portion configured to be positioned through skin of a user and in contact with interstitial fluid, wherein the portion is further configured to sense a glucose level in the interstitial fluid (Page 6: “The sensor response may be correlated and/or converted to analyte levels in blood or other fluids … an analyte sensor may be positioned in contact with interstitial fluid to detect the level of glucose, which detected glucose may be used to infer the glucose level in the patient's bloodstream. Analyte sensors may be insertable into a vein, artery, or other portion of the body containing fluid.”), and wherein the on-body device is configured to transmit data indicative of the glucose level (Page 6: “Embodiments include wholly implantable analyte sensors and analyte sensors in which only a portion of the sensor is positioned under the skin and a portion of the sensor resides above the skin, e.g., for contact to a control unit, transmitter, receiver, transceiver, processor, etc”); and
a receiver unit comprising wireless communications circuitry configured to receive the data indicative of the glucose level, the receiver unit further comprising one or more processors coupled with a memory (Pages 12: “the processor 204 also includes memory (not shown) for storing data such as the identification information for the data processing unit 102, as well as the data signals received from the sensor 101”), the memory configured to store instructions that, when executed by the one or more processors, cause the one or more processors to:
determine time derivatives of a plurality of data points corresponding to the data indicative of the glucose level ( Page 26: “derivatives may be used to refine the boundaries, such as using the second derivatives to refine the boundaries as the dropouts start or end at where dG/dt changes fastest, i.e. where the second derivative reaches it local minimum or maximum, as shown in FIG. 18. FIG. 19 illustrates using derivatives to refine the boundaries.”);
determine, based on the time derivatives, a data set comprising a plurality of carbohydrate intake start candidates and a plurality of carbohydrate peak response candidates of a plurality of carbohydrate peaks (Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”; “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout”; Page 26: “derivatives may be used to refine the boundaries, such as using the second derivatives to refine the boundaries as the dropouts start or end at where dG/dt changes fastest, i.e. where the second derivative reaches it local minimum or maximum, as shown in FIG. 18. FIG. 19 illustrates using derivatives to refine the boundaries.”).
However, Taub does not teach determine whether each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; and for each carbohydrate intake start candidate that has a corresponding carbohydrate peak response candidate, pair the each carbohydrate intake start candidate with the corresponding carbohydrate peak response candidate.
Lopatka discloses methods of finding peaks and determining false positives. Specifically, Lopatka teaches the steps of determine whether each start candidate of the plurality of start candidates has a corresponding peak response candidate of the plurality of peak response candidates; for each start candidate that has a corresponding peak response candidate, pair the each start candidate with the corresponding peak response candidate (Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”). Taub and Lopatka are analogous arts as they are both related to processes used to determine peak measurements and remove false positives from data.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of determining the correct peaks from Lopatka into the system from Taub as it allows the system to determine which glucose measurements correctly indicate a carbohydrate intake and which measurements are false positives, which can ensure that only the correct measurements are used and a more accurate result is provided to the user.
The Taub/Lopatka combination teaches for each carbohydrate intake start candidate that does not have a corresponding carbohydrate peak response candidate, remove the each carbohydrate intake start candidate from the data set (Taub, Pages 25-26: “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _ where L and R are the left and right boundaries of the valley, B is the bottom; [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17. With this constraint, in one embodiment, the peaks will be excluded from the dropout set.”).
Regarding claim 22, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the plurality of carbohydrate peak response candidates comprise a first carbohydrate peak response candidate and a second carbohydrate peak response candidate, wherein the first carbohydrate peak response candidate is adjacent to the second carbohydrate peak response candidate, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: remove the first carbohydrate peak response candidate in response to a determination that the second carbohydrate peak response candidate has a larger glucose level than the first carbohydrate peak response candidate (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”; Taub, Page 27: “Furthermore experimental observations indicated that many dropouts were led by a small but fast peak”. Lopatka teaches determining peaks based on their responses, and Taub teaches removing dropouts which can be small peaks (lower glucose levels), and therefore the compared signals will be removed if the signal is smaller than an adjacent signal.).
Regarding claim 23, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates or one or more carbohydrate peak response candidates from the plurality of carbohydrate peak response candidates that comprise a signal artifact (Taub, Page 25: “Analysis or evaluation of the wavelet details compared with the true dropouts of the sensor data in one embodiment indicates that the wavelet details D3, D4, and in particular wavelet detail D5 captures the dropouts, while lower level details Dl and D2 may be regarded as noise”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”), and remove the one or more carbohydrate intake start candidates or carbohydrate peak response candidates that comprise the signal artifact (Taub, Pages 25-26: “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _ where L and R are the left and right boundaries of the valley, B is the bottom; [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17. With this constraint, in one embodiment, the peaks will be excluded from the dropout set.”).
Regarding claim 24, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates having a proximity or a level drop relative to an adjacent carbohydrate peak response candidate from the plural of carbohydrate intake start candidates that is below a predetermined threshold (Taub, Page 25: “Analysis or evaluation of the wavelet details compared with the true dropouts of the sensor data in one embodiment indicates that the wavelet details D3, D4, and in particular wavelet detail D5 captures the dropouts, while lower level details Dl and D2 may be regarded as noise”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”); and remove the one or more carbohydrate intake start candidates that has the proximity or the level drop relative to the adjacent carbohydrate peak response candidate that is below the predetermined threshold (Taub, Pages 25-26: “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _where L and R are the left and right boundaries of the valley, B is the bottom; [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17. With this constraint, in one embodiment, the peaks will be excluded from the dropout set.”).
Regarding claim 25, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: identify one or more carbohydrate intake start candidates from the plurality of carbohydrate intake start candidates or carbohydrate peak response candidates from the plurality of carbohydrate peak response candidates that comprise a difference in amplitude below a predetermined level (Taub, Page 25: “Analysis or evaluation of the wavelet details compared with the true dropouts of the sensor data in one embodiment indicates that the wavelet details D3, D4, and in particular wavelet detail D5 captures the dropouts, while lower level details Dl and D2 may be regarded as noise”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”); and remove the one or more carbohydrate intake start candidates or carbohydrate peak response candidates that comprise the difference in amplitude below the predetermined level (Taub, Pages 25-26: “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _where L and R are the left and right boundaries of the valley, B is the bottom; [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17. With this constraint, in one embodiment, the peaks will be excluded from the dropout set.”).
Regarding claim 26, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: refine the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 27, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the receiver unit further comprises a display (Taub, Page 17: “The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information”), wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a carbohydrate intake start event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates (Taub, Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”; “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”); and output an indication associated with the carbohydrate intake start event on the display (Taub, Page 17: “The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information. Additionally, the output 310 may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones, pagers, etc”).
Regarding claim 28, the Taub/Lopatka combination teaches the glucose monitoring system of claim 27, wherein the carbohydrate intake start event comprises a meal start event (Taub, Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”).
Regarding claim 29, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the receiver unit further comprises a display (Taub, Page 17: “The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information”), wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a peak carbohydrate response event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates (Taub, Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”; “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”); and output an indication associated with the peak carbohydrate intake response event on the display (Taub, Page 17: “The output 310 of the primary receiver unit 104 is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information. Additionally, the output 310 may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones, pagers, etc”).
Regarding claim 30, the Taub/Lopatka combination teaches the glucose monitoring system of claim 29, wherein the peak carbohydrate intake response event includes a peak meal response event (Taub, Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”).
Regarding claim 33, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: condition the plurality of data points corresponding to the data indicative of the glucose level over a first time period (Taub, Page 6: “Embodiments of the analyte sensors of the subject disclosure may be configured for monitoring the level of the analyte over a time period which may range from minutes, hours, days, weeks, or longer”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 34, the Taub/Lopatka combination teaches the glucose monitoring system of claim 33, wherein time derivatives of the plurality of data points are determined based on the conditioned plurality of data points (Taub, Page 6: “Embodiments of the analyte sensors of the subject disclosure may be configured for monitoring the level of the analyte over a time period which may range from minutes, hours, days, weeks, or longer.”; Page 11: “Serial communication section 309 can also be used to upload data to a computer, such as time-stamped blood glucose data”; Page 17: “the data processing unit 102 (FIG. 1) is configured to detect the current signal from the sensor unit 101 (FIG. 1) and optionally the skin and/or ambient temperature near the sensor unit 101, which may be preprocessed by, for example, the data processing unit processor 204 (FIG. 2) and transmitted to the receiver unit (for example, the primary receiver unit 104 (FIG. I)) at least at a predetermined time interval”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 35, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: remove any outlier data from the plurality of data points corresponding to the data indicative of the glucose level; and smooth the plurality of data points (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 36, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein determining whether each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates has a corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates comprises determining an optima of acceleration (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 37, the Taub/Lopatka combination teaches the glucose monitoring system of claim 36, wherein the optima of acceleration is based on the determined time derivatives (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Regarding claim 38, the Taub/Lopatka combination teaches the glucose monitoring system of claim 37, wherein determining the optima of acceleration based on the time derivatives includes determining acceleration of the plurality of data points based on the time derivatives (Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
Claims 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over the Taub/Lopatka combination as applied to claim 21 above, and further in view of Jennewine (US 2009/0054745).
Regarding claim 31, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the receiver unit further comprises a display.
However, the Taub/Lopatka combination does not teach wherein the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates is utilized to confirm an absence or a presence of one or more meal tags manually entered by the user on the receiver unit.
Jennewine discloses a system for managing data related to analyte monitoring and diabetes management. Specifically, Jennewine teaches wherein the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates is utilized to confirm an absence or a presence of one or more meal tags manually entered by the user on the receiver unit ([0047]-[0050]: “A method in accordance with yet another embodiment of the present invention includes retrieving a meal schedule for a predetermined time period, retrieving monitored analyte levels for the predetermined time period, and arranging the monitored analyte levels based on one or more meal types of the meal schedule for the predetermined time period. The method may also include outputting the arranged monitored analyte levels. The meal schedule in one embodiment may include one or more time period associated with breakfast, lunch or dinner. In a further aspect, arranging the monitored analyte levels may include associating a respective analyte level for each retrieved meal schedule.”; [0034]: “the meal schedule of the patient for a predetermined time period is retrieved. Thereafter, the monitored analyte levels for the predetermined time period is retrieved, for example, from the storage unit 320 of the remote terminal 140. Thereafter, the monitored analyte levels are arranged or realigned based on the meal types for the predetermined time period, and the arranged or realigned monitored analyte levels are output on the display unit 340”). Taub and Jennewine are analogous arts as they are both related to systems that monitor analyte data and validate data to ensure the correct data is being measured.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the meal tags from Jennewine into the system from the Taub/Lopatka combination as it allows the system to have an additional check, which can further confirm that the correct data is being used for analysis.
Regarding claim 32, the Taub/Lopatka combination teaches the glucose monitoring system of claim 21, wherein the instructions stored in the memory, when executed by the one or more processors, further cause the one or more processors to: determine a carbohydrate intake start event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates; and determine a peak carbohydrate response event based on the each carbohydrate intake start candidate of the plurality of carbohydrate intake start candidates that has the corresponding carbohydrate peak response candidate of the plurality of carbohydrate peak response candidates (Taub, Page 22: “the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake)”; “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout”; Lopatka, Pages 9-10: “Derivative-based peak detection methods make use of the fact that the first derivative of a peak will have a positive-to-negative zero-crossing at the local maxima of a peak [6]. To avoid false positives, a threshold on the slope is often imposed. By convention, this method smooths the first derivative of the signal prior to seeking zero-crossings with downward slope, after which only those zero-crossings whose slope exceeds a certain predetermined minimum are retained. Likewise the beginning and end point of a peak are often defined in terms of the zero-crossings in the second-order derivative relating to the same signal. Thus a central point, width, and fairly precise estimate of peak dimensions are accessible using derivative-based measurements”).
However, the Taub/Lopatka combination does not teach wherein the carbohydrate intake start event and the peak carbohydrate response event provide one or more meal times that can be utilized to confirm an absence or a presence of one or more meal tags to determine a glycemic model.
Jennewine teaches wherein the carbohydrate intake start event and the peak carbohydrate response event provide one or more meal times that can be utilized to confirm an absence or a presence of one or more meal tags to determine a glycemic model ([0047]-[0050]: “A method in accordance with yet another embodiment of the present invention includes retrieving a meal schedule for a predetermined time period, retrieving monitored analyte levels for the predetermined time period, and arranging the monitored analyte levels based on one or more meal types of the meal schedule for the predetermined time period. The method may also include outputting the arranged monitored analyte levels. The meal schedule in one embodiment may include one or more time period associated with breakfast, lunch or dinner. In a further aspect, arranging the monitored analyte levels may include associating a respective analyte level for each retrieved meal schedule.”; [0034]: “the meal schedule of the patient for a predetermined time period is retrieved. Thereafter, the monitored analyte levels for the predetermined time period is retrieved, for example, from the storage unit 320 of the remote terminal 140. Thereafter, the monitored analyte levels are arranged or realigned based on the meal types for the predetermined time period, and the arranged or realigned monitored analyte levels are output on the display unit 340”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the meal tags from Jennewine into the system from the Taub/Lopatka combination as it allows the system to have an additional check, which can further confirm that the correct data is being used for analysis.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant has amended the claims to overcome the objection to claim 25 and the 112(b) rejections. However, the objection to claim 36 is reiterated, as the claim has not been amended to overcome the objection.
Applicant's arguments filed 03/04/2026 have been fully considered but they are not persuasive. Applicant argues that the amendments of claim 21 would overcome the 101 rejections. However, claim 21 has only been amended to include the phrase “a plurality of carbohydrate peak response candidate of a plurality of carbohydrate peaks”, which is still within a mental process, as described in the 101 rejection above, therefore the arguments are not persuasive.
Applicant also argues that Lopatka is not analogous art. However, as explained in the 103 rejection above, both Lopatka and Taub analyze measured signals to determine if the measured peak is determined to be correct, or is measuring other information not related to the desired signal. Both Taub and Lopatka use methods to determine the accuracy of a peak detection, and therefore one of ordinary skill in the art would look to Lopatka in reference to this problem, as both references are reasonably pertinent to the particular problem being solved. Both references are related to processes used to determine peak measurements and remove false positives from data, and therefore would be reasonable to combine. Further, Lopatka relates to chemical analysis generally (see introduction to Lopatka) to which Taub is certainly drawn to.
Applicant also argues that the combination does not teach removing each carbohydrate intake start candidate from the data set for each carbohydrate intake start candidate that does not have a corresponding peak response candidate. However, as stated in the 103 rejection above, Taub teaches removing signals if it is determined that they are not an accurate measurement (Taub, Pages 25-26: “when a valley (or peak) is detected, the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _ where L and R are the left and right boundaries of the valley, B is the bottom; [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17. With this constraint, in one embodiment, the peaks will be excluded from the dropout set.”). Applicant states that the cited lines describe conditions in which a whole peak will be excluded and therefore doesn’t teach the start candidate being removed, however the start candidate is part of the measurement used for the peak, therefore the start candidate will be removed, and therefore teaching on this limitation.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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