Detailed Notice
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 .
Status of Claims
A 2nd Non-Final has been issued in view of Applicant’s arguments.
Claims 1-17 and 20-22 are currently pending.
Claims 18-19 are canceled.
Claims 21-22 are new.
Claims 1-17 and 20-22 are rejected.
Terminal Disclaimer
The terminal disclaimer filed on 03/13/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11,596,359 B1 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-17, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath et al. (US 20170290531 A1), hereinafter Kamath, in view of Manchiraju et al. (US 20190196952 A1), hereinafter Manchiraju.
Regarding claim 1, Kamath teaches a system comprising: one or more processors; and one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of (Kamath, [0332]: “A processor module 214 is the central control unit that controls the processing of the sensor electronics. In some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein, for example an ASIC can be used for some or all of the sensor's central processing. The processor typically provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (for example, programming for data smoothing and/or replacement of signal artifacts such as is described in more detail elsewhere herein). The processor additionally can be used for the system's cache memory, for example for temporarily storing recent sensor data. In some embodiments, the processor module comprises memory storage components such as ROM, RAM, dynamic-RAM, static-RAM, non-static RAM, EEPROM, rewritable ROMs, flash memory, and the like. In one exemplary embodiment, ROM 216 provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (e.g., programming for signal artifacts detection and/or replacement such as described elsewhere herein). In one exemplary embodiment, RAM 218 can be used for the system's cache memory, for example for temporarily storing recent sensor data”): identifying an error metric associated with an input variable (Kamath, [0270]: “The term “Clarke Error Grid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference”, [0271]: “refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding glucose measurements”, and [0392]: “In another exemplary embodiment, the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data. In yet another exemplary embodiment, the reference data is matched with time-corresponding sensor data, which is then evaluated on a modified clinical error grid to determine its clinical acceptability”) to a model that applies a weighting to the input variable to estimate a glucose level of a user (Kamath, [0060]: “the step of receiving continuous analyte sensor data comprises receiving data from a continuous glucose sensor and wherein the one or more criteria comprise one or more thresholds associated with hypoglycemia and/or hyperglycemia”, [0092]-[0098], and [0082]: “a system is provided for processing continuous glucose sensor data, the system comprising: a continuous glucose sensor configured to generate sensor data associated with a glucose concentration in a host; and a computer system that compares sensor data against predetermined criteria for at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, and triggers an alarm when the sensor data meets predetermined criteria”); determining a reference output of the model based on a reference input value for the input variable (Kamath, [0020]: “a system for calibrating an analyte sensor is provided, the system comprising: a sensor data module configured to receive sensor data from an analyte sensor, the sensor data comprising at least one sensor data point; a reference input module, configured to receive reference data from a reference analyte monitor, the reference data comprising at least one reference analyte value; a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair; and an output module configured to display the estimated analyte value within about 10 minutes of receiving the reference analyte value” and [0184]: “the step of receiving and processing calibration information comprises receiving reference data, and wherein the step of evaluating a predictive accuracy comprises evaluating a leverage of the reference data based at least in part on a glucose concentration associated with the reference data”) to the model; generating a modulated value for the input variable based on the reference input value and the error metric (Kamath, [006]: “receiving reference data from a reference analyte monitor, the reference data comprising at least one reference analyte value; matching at least one sensor data point with at least one reference analyte value to form a matched data pair” and [0175]: “the step of receiving and processing calibration information comprises receiving reference data from a reference analyte monitor, forming at least one matched data pair, forming a calibration set including said at least one matched data pair, and forming a calibration line from said calibration set, wherein the step of evaluating a predictive accuracy comprises evaluating a correlation of the matched data pairs in the calibration set with the calibration line.”), the modulated value simulating a measurement error associated with the input variable (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation”); determining a simulated output of the model based on the modulated value for the input variable to the model, the simulated output of the model representing an effect of the measurement error (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]-[0538]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation… In some embodiments, signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected. In alternative embodiments, other data processing can be applied alone, or in combination with the above-described methods, to replace data signals during system noise and/or signal artifacts”); updating the model when a difference between the simulated output and the reference output is greater than a threshold (Kamath, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”) by modifying weightings applied to the input variable associated with the error metric and other input variables (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”) to the model, wherein modifying the weightings reduces an effect of the measurement error of the input variable on an output of the updated (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”); determining an output glucose value using the updated (Kamath, [0408]: “At block 614, an output module, also referred to as the processor module, provides output to the user via the user interface. The output is representative of the estimated glucose value, which is determined by converting the sensor data into a meaningful glucose value such as described in more detail with reference to block 612, above. User output can be in the form of a numeric estimated glucose value, an indication of directional trend of glucose concentration, and/or a graphical representation of the estimated glucose data over a period of time, for example”) and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (Kamath, [0263]: “For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal”); and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Kamath, [0380]: “The preferred embodiments provide an integrated system 500, which includes a medicament delivery device 502 for administering a medicament to the patient 501. The integrated medicament delivery device can be designed for bolus injection, continuous injection, inhalation, transdermal absorption, other method for administering medicament, or any combinations thereof. The term medicament includes any substance used in therapy for a patient using the system 500, for example, insulin, glucagon, or derivatives thereof” and [0412]: “the substantially real-time estimated glucose value, a predicted future estimated glucose value, a rate of change, and/or a directional trend of the glucose concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system”).
Kamath does not teach to/of a/the translation model.
However, Manchiraju teaches to/of a/the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claims 2, 9, and 16, Kamath teaches an estimation model for providing an estimated glucose value (Kamath, [0524]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts (e.g., a level/classification of noise on the signal) such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a use”, [0567]: “FIG. 14 is a flowchart 1400 that illustrates a process for prediction based on a continuous analyte sensor signal, in one embodiment, including determining whether to trigger a predicted hypoglycemia or predicted hyperglycemia alarm and/or to display a predicted time to predicted hypoglycemia or predicted hyperglycemia. In the embodiment described herein with reference to FIG. 13, a “free flow algorithm” is used for the predictive algorithm, which is in contrast to a conventional predictive algorithms that use model or curve-fitting type algorithms”); determining the reference output comprises determining a reference glucose value using the reference input value (Kamath, [0006]: “receiving sensor data from an analyte sensor, the sensor data comprising at least one sensor data point; receiving reference data from a reference analyte monitor, the reference data comprising at least one reference analyte value; matching at least one sensor data point with at least one reference analyte value to form a matched data pair; converting sensor data into at least one estimated analyte value, utilizing the matched data pair; and displaying the estimated analyte value within about 10 minutes of receiving the reference analyte value”, [0009]: “a method for calibrating glucose sensor data from a continuous glucose sensor is provided, the method comprising: obtaining a reference glucose value; and matching the reference glucose value with a sensor glucose value without compensating for a time lag between the reference glucose value and the sensor glucose value such that a time stamp for the reference glucose value is as close as possible to a time stamp of the sensor glucose value”, and [0020]-[0022]); determining the simulated output comprises determining a simulated glucose value using the modulated value (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]-[0538]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation… In some embodiments, signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected. In alternative embodiments, other data processing can be applied alone, or in combination with the above-described methods, to replace data signals during system noise and/or signal artifacts); and updating… comprises updating the estimation model to reduce the difference between the simulated glucose value and the reference glucose value by assigning the reduced weighting to the input variable in the estimation model (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”, and [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”).
Kamath does not teach the translation model.
However, Manchiraju teaches the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claims 3, 10, and 17, Kamath further teaches the estimation model comprises a sensor glucose estimation model for providing the estimated glucose value (Kamath, [0088]: “ a method for processing continuous glucose sensor data is provided, the method comprising: receiving sensor data from a continuous glucose sensor, wherein the sensor data comprises one or more sensor data points; obtaining an estimated sensor glucose value from the one or more sensor data points; calculating at least two rate of change values; and filtering the at least two rate of change values to obtain a filtered rate of change value”); the reference glucose value comprises an estimated glucose value determined by the sensor glucose estimation model as a function of the reference input value and the weighting applied to the input variable ((Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”, [0405]-[0406]: “the conversion function module, also referred to as the processor module, uses the calibration set to create a conversion function. The conversion function substantially defines the relationship between the reference glucose data and the glucose sensor data. A variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set… a sensor data transformation module, also referred to as the processor module, uses the conversion function to transform sensor data into substantially real-time glucose value estimates, also referred to as calibrated data, or converted sensor data, as sensor data is continuously (or intermittently) received from the sensor”, and [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”); and the simulated glucose value comprises an estimated glucose value (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”, and [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”) determined by the sensor glucose estimation model as a function of the modulated value and the weighting applied to the input variable (Kamath, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”, [0405]-[0406]: “the conversion function module, also referred to as the processor module, uses the calibration set to create a conversion function. The conversion function substantially defines the relationship between the reference glucose data and the glucose sensor data. A variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set… a sensor data transformation module, also referred to as the processor module, uses the conversion function to transform sensor data into substantially real-time glucose value estimates, also referred to as calibrated data, or converted sensor data, as sensor data is continuously (or intermittently) received from the sensor”).
Regarding claims 4 and 11, Kamath does not teach the translation model comprises an insight model for generating a notification.
However, Manchiraju teaches the translation model comprises an insight model for generating a notification (Manchiraju, [0080]: “The process of FIG. 7 may further include obtaining additional matching test cases from an administrator (block 730). For example, in some implementations, smart engine 320 may alert an administrator that the obtained code coverage information does not satisfy the code coverage criterion, receive one or more additional test cases associated with changes in the source code from the administrator, and inject the received one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases received from the administrator”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claims 5 and 12, Kamath teaches as a function of the estimated glucose value provided by the updated estimation model having the reduced weighting applied to the input variable (Kamath, [0352]-[0353], [0411], and [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”, [0405]-[0406]: “the conversion function module, also referred to as the processor module, uses the calibration set to create a conversion function. The conversion function substantially defines the relationship between the reference glucose data and the glucose sensor data. A variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set… a sensor data transformation module, also referred to as the processor module, uses the conversion function to transform sensor data into substantially real-time glucose value estimates, also referred to as calibrated data, or converted sensor data, as sensor data is continuously (or intermittently) received from the sensor”).
Kamath does not teach updating the translation model comprises: updating the insight model for generating the notification.
However, Manchiraju teaches updating the translation model comprises: updating the insight model for generating the notification (Manchiraju, [0080]: “The process of FIG. 7 may further include obtaining additional matching test cases from an administrator (block 730). For example, in some implementations, smart engine 320 may alert an administrator that the obtained code coverage information does not satisfy the code coverage criterion, receive one or more additional test cases associated with changes in the source code from the administrator, and inject the received one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases received from the administrator”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claims 6 and 13, Kamath teaches reducing the weighting applied to the input variable (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”); determining an updated reference output based at least in part on the reduced weighting (Kamath, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”); and determining an updated simulated output based at least in part on the reduced weighting applied to the modulated value such that the difference between the simulated output and the reference output is less than the threshold (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”).
Kamath does not teach updating the translation model.
However, Manchiraju teaches updating the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claims 7 and 14, Kamath further teaches the input variable comprises an output electrical current, an electrode voltage, or an electrochemical impedance spectroscopy (EIS) (Kamath, [0263]: “For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal; the signal can then be transmitted to an electronic circuit. In this case, the electrode is “operably linked” to the electronic circuitry. These terms are broad enough to include wireless connectivity.”) value determined based on electrical signals provided by an interstitial glucose sensing arrangement (Kamath, [0434]: “As one example, a temporary signal disagreement, or a transient lack of correlation between interstitial analyte sensor data and blood analyte reference data, can be related to biology, such as differences in interstitial and blood glucose levels”).
Regarding claim 8, Kamath teaches a processor-implemented method comprising (Kamath, [0332]: “A processor module 214 is the central control unit that controls the processing of the sensor electronics. In some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein, for example an ASIC can be used for some or all of the sensor's central processing. The processor typically provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (for example, programming for data smoothing and/or replacement of signal artifacts such as is described in more detail elsewhere herein). The processor additionally can be used for the system's cache memory, for example for temporarily storing recent sensor data. In some embodiments, the processor module comprises memory storage components such as ROM, RAM, dynamic-RAM, static-RAM, non-static RAM, EEPROM, rewritable ROMs, flash memory, and the like. In one exemplary embodiment, ROM 216 provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (e.g., programming for signal artifacts detection and/or replacement such as described elsewhere herein). In one exemplary embodiment, RAM 218 can be used for the system's cache memory, for example for temporarily storing recent sensor data”): identifying an error metric associated with an input variable (Kamath, [0270]: “The term “Clarke Error Grid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference”, [0271]: “refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding glucose measurements”, and [0392]: “In another exemplary embodiment, the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data. In yet another exemplary embodiment, the reference data is matched with time-corresponding sensor data, which is then evaluated on a modified clinical error grid to determine its clinical acceptability”) to a model that applies a weighting to the input variable to estimate a glucose level of a user; determining a reference output (Kamath, [0060]: “the step of receiving continuous analyte sensor data comprises receiving data from a continuous glucose sensor and wherein the one or more criteria comprise one or more thresholds associated with hypoglycemia and/or hyperglycemia”, [0092]-[0098], and [0082]: “a system is provided for processing continuous glucose sensor data, the system comprising: a continuous glucose sensor configured to generate sensor data associated with a glucose concentration in a host; and a computer system that compares sensor data against predetermined criteria for at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, and triggers an alarm when the sensor data meets predetermined criteria”) of the model based on a reference input value for the input variable (Kamath, [0060]: “the step of receiving continuous analyte sensor data comprises receiving data from a continuous glucose sensor and wherein the one or more criteria comprise one or more thresholds associated with hypoglycemia and/or hyperglycemia”, [0092]-[0098], and [0082]: “a system is provided for processing continuous glucose sensor data, the system comprising: a continuous glucose sensor configured to generate sensor data associated with a glucose concentration in a host; and a computer system that compares sensor data against predetermined criteria for at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, and triggers an alarm when the sensor data meets predetermined criteria”) to the model; generating a modulated value for the input variable based on the reference input value and the error metric (Kamath, [006]: “receiving reference data from a reference analyte monitor, the reference data comprising at least one reference analyte value; matching at least one sensor data point with at least one reference analyte value to form a matched data pair” and [0175]: “the step of receiving and processing calibration information comprises receiving reference data from a reference analyte monitor, forming at least one matched data pair, forming a calibration set including said at least one matched data pair, and forming a calibration line from said calibration set, wherein the step of evaluating a predictive accuracy comprises evaluating a correlation of the matched data pairs in the calibration set with the calibration line.”), the modulated value simulating a measurement error associated with the input variable (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation”); determining a simulated output of the model based on the modulated value for the input variable to the model, the simulated output of the model representing an effect of the measurement error (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]-[0538]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation… In some embodiments, signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected. In alternative embodiments, other data processing can be applied alone, or in combination with the above-described methods, to replace data signals during system noise and/or signal artifacts”); updating the model when a difference between the simulated output and the reference output is greater than a threshold (Kamath, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”) by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the model based on the reduced weighting (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”), wherein reducing the weighting of the input variable and reallocating the weightings for the other input variables reduces an effect of the measurement error of the input variable on an output (Kamath, [0270]: “The term “Clarke Error Grid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference”, [0271]: “refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding glucose measurements”, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”); determining an output glucose value (Kamath, [0408]: “At block 614, an output module, also referred to as the processor module, provides output to the user via the user interface. The output is representative of the estimated glucose value, which is determined by converting the sensor data into a meaningful glucose value such as described in more detail with reference to block 612, above. User output can be in the form of a numeric estimated glucose value, an indication of directional trend of glucose concentration, and/or a graphical representation of the estimated glucose data over a period of time, for example”) and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (Kamath, [0263]: “For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal”); and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Kamath, [0380]: “The preferred embodiments provide an integrated system 500, which includes a medicament delivery device 502 for administering a medicament to the patient 501. The integrated medicament delivery device can be designed for bolus injection, continuous injection, inhalation, transdermal absorption, other method for administering medicament, or any combinations thereof. The term medicament includes any substance used in therapy for a patient using the system 500, for example, insulin, glucagon, or derivatives thereof” and [0412]: “the substantially real-time estimated glucose value, a predicted future estimated glucose value, a rate of change, and/or a directional trend of the glucose concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system”).
Kamath does not teach to/of a/the translation model.
However, Manchiraju teaches to/of a/the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claim 15, Kamath teaches one or more non-transitory processor-readable media storing instructions which, when executed by one or more processors, cause performance of (Kamath, [0332]: “A processor module 214 is the central control unit that controls the processing of the sensor electronics. In some embodiments, the processor module includes a microprocessor, however a computer system other than a microprocessor can be used to process data as described herein, for example an ASIC can be used for some or all of the sensor's central processing. The processor typically provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (for example, programming for data smoothing and/or replacement of signal artifacts such as is described in more detail elsewhere herein). The processor additionally can be used for the system's cache memory, for example for temporarily storing recent sensor data. In some embodiments, the processor module comprises memory storage components such as ROM, RAM, dynamic-RAM, static-RAM, non-static RAM, EEPROM, rewritable ROMs, flash memory, and the like. In one exemplary embodiment, ROM 216 provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (e.g., programming for signal artifacts detection and/or replacement such as described elsewhere herein). In one exemplary embodiment, RAM 218 can be used for the system's cache memory, for example for temporarily storing recent sensor data”): identifying an error metric associated with an input variable (Kamath, [0270]: “The term “Clarke Error Grid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference”, [0271]: “refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding glucose measurements”, and [0392]: “In another exemplary embodiment, the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data. In yet another exemplary embodiment, the reference data is matched with time-corresponding sensor data, which is then evaluated on a modified clinical error grid to determine its clinical acceptability”) to a model that applies a weighting to the input variable to estimate a glucose level of a user (Kamath, [0060]: “the step of receiving continuous analyte sensor data comprises receiving data from a continuous glucose sensor and wherein the one or more criteria comprise one or more thresholds associated with hypoglycemia and/or hyperglycemia”, [0092]-[0098], and [0082]: “a system is provided for processing continuous glucose sensor data, the system comprising: a continuous glucose sensor configured to generate sensor data associated with a glucose concentration in a host; and a computer system that compares sensor data against predetermined criteria for at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, and triggers an alarm when the sensor data meets predetermined criteria”); determining a reference output of the model based on a reference value for the input variable to the model; generating a modulated value for the input variable based on the reference value and the error metric (Kamath, [006]: “receiving reference data from a reference analyte monitor, the reference data comprising at least one reference analyte value; matching at least one sensor data point with at least one reference analyte value to form a matched data pair” and [0175]: “the step of receiving and processing calibration information comprises receiving reference data from a reference analyte monitor, forming at least one matched data pair, forming a calibration set including said at least one matched data pair, and forming a calibration line from said calibration set, wherein the step of evaluating a predictive accuracy comprises evaluating a correlation of the matched data pairs in the calibration set with the calibration line.”); determining a simulated output of the model based on the modulated value for the input variable (Kamath, [0020]: “a processor module configured to match at least one sensor data point with at least one reference analyte value to form a matched data pair, wherein the processor module is further configured to convert sensor data into at least one estimated analyte value utilizing the matched data pair”, [0295]: “In some embodiments, estimated analyte values are estimated for a time period during which no data exists. However, estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated (e.g. processed or filtered) data due to noise or a time lag in the measured data, for example”, and [0537]-[0538]: “In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation… In some embodiments, signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected. In alternative embodiments, other data processing can be applied alone, or in combination with the above-described methods, to replace data signals during system noise and/or signal artifacts”) to the model; and updating the model when a difference between the simulated output and the reference output is greater than a threshold (Kamath, [0498]-[0500]: “In one embodiment of signal artifacts detection that utilizes examination or evaluation of the signal information content, filtered (e.g., smoothed) data is compared to raw data (e.g., in sensor electronics or in receiver electronics). In one such embodiment, a signal residual is calculated as the difference between the filtered data and the raw data. For example, at one time point (or one time period that is represented by a single raw value and single filtered value), the filtered data can be measured at 50,000 counts and the raw data can be measured at 55,500 counts, which would result in a signal residual of 5,500 counts. In some embodiments, a threshold can be set (e.g., 5000 counts) that represents a first level of noise (e.g., signal artifact) in the data signal, when the residual exceeds that level. Similarly, a second threshold can be set (e.g., 8,000 counts) that represents a second level of noise in the data signal. Additional thresholds and/or noise classifications can be defined as is appreciated by one skilled in the art. Consequently, signal filtering, processing, and/or displaying decisions can be executed based on these conditions (e.g., the predetermined levels of noise)… Although the above-described example illustrates one method of determining a level of noise, or signal artifact(s), based on a comparison of raw vs. filtered data for a time point (or single values representative of a time period), a variety of alternative methods are contemplated. In an alternative exemplary embodiment for determining noise, signal artifacts are evaluated for noise episodes lasting a certain period of time. For example, the processor (in the sensor or receiver) can be configured to look for a certain number of signal residuals above a predetermined threshold (representing noise time points or noisy time periods) for a predetermined period of time (e.g., a few minutes to a few hours or more)”) by determining a reduced weighting that is applied to the input variable and determining reallocated weightings for other input variables to the model based on the reduced weighting (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”); determining an output glucose value (Kamath, [0408]: “At block 614, an output module, also referred to as the processor module, provides output to the user via the user interface. The output is representative of the estimated glucose value, which is determined by converting the sensor data into a meaningful glucose value such as described in more detail with reference to block 612, above. User output can be in the form of a numeric estimated glucose value, an indication of directional trend of glucose concentration, and/or a graphical representation of the estimated glucose data over a period of time, for example”) using the updated model and one or more values of the input variable derived from one or more electrical signals outputted by a glucose sensor of the user (Kamath, [0263]: “For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal”); and causing administration of an amount of insulin to the user by an insulin delivery device based on at least the output glucose value, wherein the amount of insulin is determined based on the output glucose value (Kamath, [0380]: “The preferred embodiments provide an integrated system 500, which includes a medicament delivery device 502 for administering a medicament to the patient 501. The integrated medicament delivery device can be designed for bolus injection, continuous injection, inhalation, transdermal absorption, other method for administering medicament, or any combinations thereof. The term medicament includes any substance used in therapy for a patient using the system 500, for example, insulin, glucagon, or derivatives thereof” and [0412]: “the substantially real-time estimated glucose value, a predicted future estimated glucose value, a rate of change, and/or a directional trend of the glucose concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system”).
Kamath does not teach to/of a/the translation model.
However, Manchiraju teaches to/of a/the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claim 20, Kamath teaches updating the model comprises reducing the weighting of the input variable and reallocating the weightings for the other input variables a plurality of times until a difference between a simulated output of the updated model and the reference output is less than the threshold (Kamath, [0270]: “The term “Clarke Error Grid”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference”, [0271]: “refers without limitation to an error grid analysis that assigns a specific level of clinical risk to any possible error between two time corresponding glucose measurements”, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”).
Kamath does not teach the translation model.
However, Manchiraju teaches the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claim 21, Kamath teaches updating the model when a difference between the simulated output and the reference output is greater than the threshold (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails. For example, the 50% trimmed mean is the mean of the values between the upper and lower quartiles. The 90% trimmed mean is the mean of the values after truncating the lowest and highest 5% of the values. In one example, two highest and two lowest measurements are removed from a data set and then the remaining measurements are averaged”, [0450]-[0451], and [0505]) comprises: determining a likelihood of a false positive based on the difference (Kamath, [0500]: “It has been shown in certain circumstances that a differential measurement as compared to a residual measurement as described herein, amplifies noise and therefore may be more sensitive to noise episodes, without increasing false positives due to fast, but physiological, rates of change.”); determining the likelihood of the false positive exceeds the threshold (Kamath, [0500]: “It has been shown in certain circumstances that a differential measurement as compared to a residual measurement as described herein, amplifies noise and therefore may be more sensitive to noise episodes, without increasing false positives due to fast, but physiological, rates of change. Accordingly, a noise episode, or noise episode level, can be defined by one or more points (e.g., residuals or differentials) above a predetermined threshold, and in some embodiments, for a predetermined period of time. Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”); and reducing the weighting of the input variable based on the likelihood of the false positive (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails”, [0499]: “Similarly, a noise level determination can be reduced or altered when a different (e.g., reduced) number of points above the predetermined threshold are calculated in a predetermined period of time”, [0505]: “One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user”).
Kamath does not teach the translation model.
However, Manchiraju teaches the translation model (Manchiraju,, [0046]: “For example, smart engine 320 may extract source code keywords from comments, file names, variable names, function names, and/or other types of entities associated with the source code changes and may indicate to translation engine 340 which source code keywords from source code keywords DB 330 to use as input in the mapping process. Translation engine 340 may map the extracted source code keywords to testing keywords and may indicate to smart engine 320 which testing keywords from testing keywords DB 350 to use in identifying relevant test cases for the identified source code changes” and [0079]: “The process of FIG. 7 may further include identifying additional matching test cases based on testing history (block 720). For example, in some implementations, smart engine 320 may identify untested changes in the source code based on the obtained code coverage information, select one or more additional test cases associated with the untested changed by accessing testing history DB 375, and inject the selected one or more additional test cases into the testing queue. Furthermore, smart engine 320 may update translation engine 340 based on the testing keywords associated with the additional test cases selected from testing history DB 375”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kamath to incorporate the teachings of Manchiraju and account for implementing mapping and data on a translation model (Manchiraju, [0046]).
Regarding claim 22, Kamath further teaches a magnitude of reduction in the weighting of the input variable is based on a percentage agreement between the simulated output and the reference output (Kamath, [0289]: “The term “trimmed mean” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails. For example, the 50% trimmed mean is the mean of the values between the upper and lower quartiles. The 90% trimmed mean is the mean of the values after truncating the lowest and highest 5% of the values. In one example, two highest and two lowest measurements are removed from a data set and then the remaining measurements are averaged”, [0450]-[0451], and [0505]).
Response to Arguments
Applicant's arguments filed 03/13/2026 have been fully considered. Regarding the telephonic interview with Primary Patent Examiner, Michael Ezewoko, the 35 U.S.C. 112 Rejection is withdrawn, and an issuance of a 2nd Non-Final has been issued to replace the Agrawal et al. (US 20210038163 A1) reference.
Applicant’s arguments with respect to the 35 U.S.C 102/103 Rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
