DETAILED ACTION
Claims 1-20 are pending and hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1 and 14 are objected to because of the following informalities:
Claim 1, line 3, “indicative of glucose level” should read “indicative of a glucose level”.
Claim 14, line 6, “indicative of glucose level” should read “indicative of a glucose level”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 14, it is unclear how the input parameter space is associated with the sensor measurement data. Are the SG models selected based on which parameters are measured by the glucose sensor, or are the SG models selected based on the measured value? Do one or more of the input parameters correspond to one or more measurements of the sensor glucose sensor?
It is further unclear how estimating a first SG value is performed based on the sensor measurement and the SG model. Does the model use the sensor data measurement as an input to estimate an SG value? Is the SG value estimated based on a calculation made by the model and the sensor measurement data?
For examination purposes, the claims will be interpreted such that the input parameter space corresponds to one or more measurements of the glucose sensor, such as the spaces defined in paragraph 0038, including age, Vcntr, Isig, EIS, or other sensor measurement data. The claims will also be interpreted such that the SG values are estimated by inputting the sensor measurement data into the selected SG models, which result in a SG value. Claims 2-13 and 15-20 are also rejected due to their dependence on claims 1 and 14.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14-17 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they claim a “processor-readable media”. Without specifying that the processor-readable media is non-transitory, the claimed invention could be implemented as data incorporated on a digital signal; however, signals are not patentable subject matter. When a claim covers both statutory and non-statutory embodiments, it is proper to reject as including non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Analysis of independent claims 1, 14, and 18:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 14 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 1 is directed to a computer implemented method, which describes one of the four statutory categories of patentable subject matter, i.e., a method. Claim 18 is directed to a non-transitory computer-program software product, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary regarding claims.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 14, and 18 recite an abstract idea. In particular, the claims generally recite
the following:
selecting, based at least in part on the sensor measurement data, a first regional sensor glucose (SG) model from a first plurality of regional SG models for respective regions of a first plurality of regions of an input parameter space associated with the sensor measurement data (claims 1, 14, and 18);
wherein the input parameter space is partitioned into the first plurality of regions based on a first partition scheme (claims 1, 14, and 18);
estimating a first SG value using the first regional SG model and the sensor measurement data (claims 1, 14, and 18);
selecting, based at least in part on the sensor measurement data, a second regional SG model from a second plurality of regional SG models for respective regions of a second plurality of regions of the input parameter space (claims 1, 14, and 18);
wherein the input parameter space is partitioned into the second plurality of regions based on a second partition scheme that is different from the first partition scheme (claims 1, 14, and 18);
estimating a second SG value using the second regional SG model and the sensor measurement data (claims 1, 14, and 18); and
determining a predicted SG value based on a combination of the first SG value and the second SG value (claims 1, 14, and 18).
These elements recited in claims 1, 14, and 18 are drawn to an abstract idea since they are directed towards mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I) and mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Selecting, based at least in part on the sensor measurement data, a first/second regional sensor glucose (SG) model from a first/second plurality of regional SG models for respective regions of a first/second plurality of regions of an input parameter space associated with the sensor measurement data is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably select a regional sensor glucose model that corresponds to the sensor measurement data. There is nothing to suggest an undue level of complexity in selecting, based at least in part on the sensor measurement data, a first/second regional sensor glucose (SG) model from a first/second plurality of regional SG models for respective regions of a first/second plurality of regions of an input parameter space associated with the sensor measurement data.
Partitioning the input parameter space into the first/second plurality of regions based on a first/second partition scheme is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably select certain parameters of measured data, and divide them according to certain ranges or criteria. There is nothing to suggest an undue level of complexity in partitioning the input parameter space into the first/second plurality of regions based on a first/second partition scheme.
Estimating a first/second SG value using the first/second regional SG model and the sensor measurement data is directed towards a mathematical concept since the data is generated via a machine learning model, algorithm, or equation with the sensor data as input.
Determining a predicted SG value based on a combination of the first SG value and the second SG value is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably determine a sensor glucose value given two other determined values. There is nothing to suggest an undue level of complexity in determining a predicted SG value based on a combination of the first SG value and the second SG value.
Prong Two: Claims 1, 14, and 18 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional elements merely:
Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “one or more processors; and one or more processor-readable media storing instructions” (claim 14) and "one or more non-transitory processor-readable media storing instructions” (claim 18)) and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data gathering components (e.g., "receiving sensor measurement data measured by a glucose sensor, the sensor measurement data including at least one signal that is indicative of glucose level and at least one signal that is indicative of sensor health" (claims 1, 14, and 18)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 14, and 18 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on generic processing components, the pre-solution activity of using generic data-gathering components, and generic post-solution activities, which merely facilitate the abstract idea.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, the one or more processors and one or more non-transitory processor-readable media storing instructions are generic processing components for implementing the abstract ideas. The glucose sensor is a generic data-gathering component that performs the pre-solution activity of receiving sensor measurement data, which merely facilitates the abstract idea.
These elements do not qualify as significantly more because these limitations are simply appending well understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements include a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Analysis the dependent claims 2-13, 15-17, and 19-20:
Dependent claims 9-12, 17, and 20 recite steps that are mathematical concepts, which add to the abstract idea. The mathematical concepts are identified as:
“generating at least one of a derivative feature or a transformed feature based on the sensor measurement data, wherein input parameters of the first plurality of regional SG models and the second plurality of regional SG models include at least one of the derivative feature or the transformed feature” (claim 9);
“wherein determining the predicted SG value based on the combination of the first SG value and the second SG value comprises determining an average or a weighted average of the first SG value and the second SG value” (claims 10, 17, and 20);
“determining a first probability or confidence level associated with the first SG value; and determining a second probability or confidence level associated with the first SG value, wherein weights associated with the first SG value and the second SG value for the weighted average are determined based on the first probability or confidence level, and the second probability or confidence level” (claim 11);
“estimating a third SG value using the third regional SG model and the sensor measurement data” (claim 12);
Dependent claim 12 recites mental steps that may be performed in the human mind with the aid of pen and paper or a generic computer, which add to the abstract idea. The mental steps are identified as:
“selecting, based at least in part on the sensor measurement data, a third regional SG model from a third plurality of regional SG models for respective regions of a third plurality of regions of the input parameter space” (claim 12);
“wherein the input parameter space is partitioned into the third plurality of regions based on a third partition scheme that is different from the first partition scheme and the second partition scheme” (claim 12); and
“determining the predicted SG value includes determining the predicted SG value based on a combination of the first SG value, the second SG value, and the third SG value” (claim 12).
Dependent claims 1-8, 13, 15-16, and 19 recite limitations in addition to the abstract idea, they merely further describe the abstract idea:
“wherein at least one of the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on sensor current (Isig), counter voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, age of the glucose sensor, temperature, age of a user of the glucose sensor, body mass index (BMI) of the user, or a combination thereof” (claims 2, 15, and 19);
“wherein the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on a sensor current and an age of the glucose sensor” (claims 3 and 16);
“wherein the first partition scheme and the second partition scheme use different combinations of input parameters to partition the input parameter space” (claim 4);
“wherein the first partition scheme and the second partition scheme use different numbers of input parameters to partition the input parameter space” (claim 5);
“wherein the first partition scheme and the second partition scheme partition the input parameter space using different ranges of a same set of input parameters” (claim 6);
“wherein regions of the first plurality of regions of the input parameter space are characterized by different sizes, different shapes, different numbers of dimensions, or a combination thereof” (claim 7);
“wherein a number of regions of the first plurality of regions of the input parameter space is the same as or different from a number of regions of the second plurality of regions of the input parameter space” (claim 8);
“each regional SG model of the first plurality of regional SG models and the second plurality of regional SG models includes one or more machine learning models, equations, functions, or a combination thereof; and a first regional SG model and a second regional SG model in the first plurality of regional SG models and the second plurality of regional SG models use different input parameters” (claim 13).
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. The result of the abstract idea does not cause the computing device and/or application to perform different. The result of the abstract idea does not cause output of the user-accessible output.
Therefore, claims 1-20 are rejected as being directed to non-statutory subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 9-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ajemba (US 20220233108).
Regarding claim 1, Ajemba discloses a processor-implemented method comprising:
receiving sensor measurement data measured by a glucose sensor (Fig. 2, subcutaneous sensor set 10), the sensor measurement data including at least one signal that is indicative of glucose level and at least one signal that is indicative of sensor health (Paragraph 0052, “receive the sensor data at a sensor device. For example, the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0051, “data characteristics may include sensor data availability, sensor data accuracy, or probabilistic reliance”. While Ajemba does not explicitly disclose that the sensor measurement data includes these data characteristics, a plurality of machine learning models use these data characteristics of glucose measurements to predict sensor glucose values. Thus, Ajemba discloses a signal indicative of “sensor health” as the machine learning models use sensor data availability, sensor data accuracy, or probabilistic reliance);
selecting, based at least in part on the sensor measurement data, a first regional sensor glucose (SG) model from a first plurality of regional SG models for respective regions of a first plurality of regions of an input parameter space associated with the sensor measurement data (Paragraph 0051, “process 700 may retrieve a first machine learning model that is able to predict sensor glucose values based on available and accurate sensor data (e.g., normal conditions)”), wherein the input parameter space is partitioned into the first plurality of regions based on a first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “normal conditions” input);
estimating a first SG value using the first regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values);
selecting, based at least in part on the sensor measurement data, a second regional SG model from a second plurality of regional SG models for respective regions of a second plurality of regions of the input parameter space (Paragraph 0051, “Process 700 may retrieve a second machine learning model that is able to predict sensor glucose values based largely on sensor data and partially on probabilistic information (e.g., under conditions in which some accurate sensor data is lacking)”), wherein the input parameter space is partitioned into the second plurality of regions based on a second partition scheme that is different from the first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “conditions in which some accurate sensor data is lacking” input);
estimating a second SG value using the second regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values); and
determining a predicted SG value based on a combination of the first SG value and the second SG value (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the predicted SG value is the output; Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Regarding claim 2, Ajemba further discloses wherein at least one of the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on sensor current (Isig), counter voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data (Paragraph 0052, “the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0073, “As shown in FIG. 11, inputs 1102 may include Isig, EIS, Vcntr, or other input signals”), age of the glucose sensor (Paragraph 0057, wherein outlier conditions may include wear conditions such as high levels of wear due to the sensor experiencing use over a long period of time), temperature (Paragraph 0057, wherein environmental conditions are considered, such as temperature), or a combination thereof.
Regarding claim 3, Ajemba further discloses wherein the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on a sensor current (Paragraph 0052, “the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0073, “As shown in FIG. 11, inputs 1102 may include Isig, EIS, Vcntr, or other input signals”) and an age of the glucose sensor (Paragraph 0057, wherein outlier conditions may include wear conditions such as high levels of wear due to the sensor experiencing use over a long period of time).
Regarding claim 9, Ajemba further discloses generating at least one of a derivative feature or a transformed feature based on the sensor measurement data, wherein input parameters of the first plurality of regional SG models and the second plurality of regional SG models include at least one of the derivative feature or the transformed feature (Paragraph 0051, “ For example, process 700 may detect a brief abnormality in the sensor data (e.g., a spike) and may therefore remove or minimize the impact of the abnormal sensor data in the final output calculation, relying more on the remaining reliable sensor data as well as probabilistic information”; Examiner interprets the removed/minimized data to be filtered. As disclosed in Applicant’s specification paragraph 0088, filtered signals are provided as an example of derivative features).
Regarding claim 10, Ajemba further discloses wherein determining the predicted SG value based on the combination of the first SG value and the second SG value comprises determining an average or a weighted average of the first SG value and the second SG value (Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Regarding claim 11, Ajemba further discloses determining a first probability or confidence level associated with the first SG value; and determining a second probability or confidence level associated with the first SG value (Paragraph 0051, wherein the second machine learning model predicts glucose values partially based on probabilistic information and a third machine learning model may predict glucose values largely based on probabilistic information),
wherein weights associated with the first SG value and the second SG value for the weighted average are determined based on the first probability or confidence level, and the second probability or confidence level (Paragraph 0054, wherein when determining the glucose value, the process may select a machine learning model that relies more on sensor data when the data is more accurate and reliable, and the process may select a machine learning model that relies more on probabilistic information when the data is less accurate or reliable; Examiner interprets the process selecting which models to use based on how accurate/reliable the sensor data is as applying a weight to each model. As further disclosed in paragraph 0054, the sensor glucose value could be based on a weighted average of all of the plurality of predicted sensor glucose values).
Regarding claim 12, Ajemba further discloses selecting, based at least in part on the sensor measurement data, a third regional SG model from a third plurality of regional SG models for respective regions of a third plurality of regions of the input parameter space (Paragraph 0051, wherein three machine learning models make predictions of sensor glucose values), wherein the input parameter space is partitioned into the third plurality of regions based on a third partition scheme that is different from the first partition scheme and the second partition scheme (Paragraph 0051, wherein the three machine learning models is used under conditions in which accurate sensor data is extremely lacking); and estimating a third SG value using the third regional SG model and the sensor measurement data, wherein determining the predicted SG value includes determining the predicted SG value based on a combination of the first SG value, the second SG value, and the third SG value (Paragraph 0051, wherein the three machine learning models all predict a value of glucose, and the sensor glucose value is determined based on a weighted average of the plurality of predicted sensor glucose values).
Regarding claim 14, Ajemba discloses a system comprising:
one or more processors (Fig. 5, signal processor 590, measurement processor 595, and microcontroller 510); and
one or more processor-readable media storing instructions which, when executed by the one or more processors (Paragraph 0025, wherein the computer/system includes programming instructions to execute the disclosed functions), cause performance of operations including:
receiving sensor measurement data measured by a glucose sensor (Fig. 2, subcutaneous sensor set 10), the sensor measurement data including at least one signal that is indicative of glucose level and at least one signal that is indicative of sensor health (Paragraph 0052, “receive the sensor data at a sensor device. For example, the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0051, “data characteristics may include sensor data availability, sensor data accuracy, or probabilistic reliance”. While Ajemba does not explicitly disclose that the sensor measurement data includes these data characteristics, a plurality of machine learning models use these data characteristics of glucose measurements to predict sensor glucose values. Thus, Ajemba discloses a signal indicative of “sensor health” as the machine learning models use sensor data availability, sensor data accuracy, or probabilistic reliance);
selecting, based at least in part on the sensor measurement data, a first regional sensor glucose (SG) model from a first plurality of regional SG models for respective regions of a first plurality of regions of an input parameter space associated with the sensor measurement data (Paragraph 0051, “process 700 may retrieve a first machine learning model that is able to predict sensor glucose values based on available and accurate sensor data (e.g., normal conditions)”), wherein the input parameter space is partitioned into the first plurality of regions based on a first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “normal conditions” input);
estimating a first SG value using the first regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values);
selecting, based at least in part on the sensor measurement data, a second regional SG model from a second plurality of regional SG models for respective regions of a second plurality of regions of the input parameter space (Paragraph 0051, “Process 700 may retrieve a second machine learning model that is able to predict sensor glucose values based largely on sensor data and partially on probabilistic information (e.g., under conditions in which some accurate sensor data is lacking)”), wherein the input parameter space is partitioned into the second plurality of regions based on a second partition scheme that is different from the first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “conditions in which some accurate sensor data is lacking” input);
estimating a second SG value using the second regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values); and
determining a predicted SG value based on a combination of the first SG value and the second SG value (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the predicted SG value is the output; Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Regarding claim 15, Ajemba further discloses wherein at least one of the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on sensor current (Isig), counter voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data (Paragraph 0052, “the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0073, “As shown in FIG. 11, inputs 1102 may include Isig, EIS, Vcntr, or other input signals”), age of the glucose sensor (Paragraph 0057, wherein outlier conditions may include wear conditions such as high levels of wear due to the sensor experiencing use over a long period of time), temperature (Paragraph 0057, wherein environmental conditions are considered, such as temperature), or a combination thereof.
Regarding claim 16, Ajemba further discloses wherein the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on a sensor current (Paragraph 0052, “the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0073, “As shown in FIG. 11, inputs 1102 may include Isig, EIS, Vcntr, or other input signals”) and an age of the glucose sensor (Paragraph 0057, wherein outlier conditions may include wear conditions such as high levels of wear due to the sensor experiencing use over a long period of time).
Regarding claim 17, Ajemba further discloses wherein determining the predicted SG value based on the combination of the first SG value and the second SG value comprises determining an average or a weighted average of the first SG value and the second SG value (Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Regarding claim 18, Ajemba discloses one or more non-transitory processor-readable media storing instructions which, when executed by one or more processors, cause performance of operations comprising:
receiving sensor measurement data measured by a glucose sensor (Fig. 2, subcutaneous sensor set 10), the sensor measurement data including at least one signal that is indicative of glucose level and at least one signal that is indicative of sensor health (Paragraph 0052, “receive the sensor data at a sensor device. For example, the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0051, “data characteristics may include sensor data availability, sensor data accuracy, or probabilistic reliance”. While Ajemba does not explicitly disclose that the sensor measurement data includes these data characteristics, a plurality of machine learning models use these data characteristics of glucose measurements to predict sensor glucose values. Thus, Ajemba discloses a signal indicative of “sensor health” as the machine learning models use sensor data availability, sensor data accuracy, or probabilistic reliance);
selecting, based at least in part on the sensor measurement data, a first regional sensor glucose (SG) model from a first plurality of regional SG models for respective regions of a first plurality of regions of an input parameter space associated with the sensor measurement data (Paragraph 0051, “process 700 may retrieve a first machine learning model that is able to predict sensor glucose values based on available and accurate sensor data (e.g., normal conditions)”), wherein the input parameter space is partitioned into the first plurality of regions based on a first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “normal conditions” input);
estimating a first SG value using the first regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values);
selecting, based at least in part on the sensor measurement data, a second regional SG model from a second plurality of regional SG models for respective regions of a second plurality of regions of the input parameter space (Paragraph 0051, “Process 700 may retrieve a second machine learning model that is able to predict sensor glucose values based largely on sensor data and partially on probabilistic information (e.g., under conditions in which some accurate sensor data is lacking)”), wherein the input parameter space is partitioned into the second plurality of regions based on a second partition scheme that is different from the first partition scheme (Paragraph 0051, wherein the input parameter space partition scheme is the “conditions in which some accurate sensor data is lacking” input);
estimating a second SG value using the second regional SG model and the sensor measurement data (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the first SG value estimate is one of the plurality of predicted sensor glucose values); and
determining a predicted SG value based on a combination of the first SG value and the second SG value (Paragraph 0053, “At step 708, process 700 (e.g., using components described in FIGS. 1-6) receives an output from the plurality of machine learning models indicating a plurality of predicted sensor glucose values”, wherein the predicted SG value is the output; Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Regarding claim 19, Ajemba further discloses wherein at least one of the first partition scheme or the second partition scheme partitions the input parameter space based at least in part on sensor current (Isig), counter voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data (Paragraph 0052, “the sensor data may comprise an Interstitial Current Signal (“Isig”), Electrochemical Impedance Spectroscopy Signal (“EIS”), and counter voltage (“Vcntr”)”; Paragraph 0073, “As shown in FIG. 11, inputs 1102 may include Isig, EIS, Vcntr, or other input signals”), age of the glucose sensor (Paragraph 0057, wherein outlier conditions may include wear conditions such as high levels of wear due to the sensor experiencing use over a long period of time), temperature (Paragraph 0057, wherein environmental conditions are considered, such as temperature), or a combination thereof.
Regarding claim 20, Ajemba further discloses wherein determining the predicted SG value based on the combination of the first SG value and the second SG value comprises determining an average or a weighted average of the first SG value and the second SG value (Paragraph 0054, “In some embodiments, the sensor glucose value may be based on a weighted average of the plurality of predicted sensor glucose values”).
Claims 4-8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ajemba (US 20220233108) as applied to claim 1 above, and further in view of Ajemba (US 20220245306), hereinafter “Ajemba 306”.
Regarding claims 4-8 and 13, Ajemba discloses the system as described above. With regards to the limitations of claim 13, Ajemba discloses wherein each regional SG model comprises one or more machine learning models, equations, and/or functions (Paragraph 0050, ”applying layered prediction models (e.g., supervised machine learning models, unsupervised machine learning models, semi-supervised machine learning models, or any other suitable type of machine learning models”).
With regards to the limitations of claims 4-8 and 13, Ajemba fails to disclose wherein the partition schemes use different numbers of input parameters, use different combinations of input parameters, and/or use different ranges of the same set of input parameters. Ajemba also fails to disclose wherein the regions are characterized by different sizes, shapes, and/or dimensions and wherein the number of regions is the same or different. Between the first and second plurality of regions.
Ajemba and Ajemba 306 are in the same field of predicting glucose measurements. Ajemba 306 teaches an analogous method of partitioning input signals and using one or more machine learning models to predict a glucose value (Abstract). As shown in Figs. 9A-B, an input signal feature space may comprise various partitions of a sensor property or feature. Each of the partitions or subspaces may correspond to a range of values associated with the sensor electrical property for a respective subspace (Paragraph 0054), and that different models may be used for each subspace (Paragraph 0071). As the partitions as shown in Fig. 9A are different sizes/shapes, Examiner interprets the ranges to be different ranges of the same input parameters on the axes (Paragraphs 0066-0070). In another embodiment as shown in Fig. 9B, the partition may be three dimensional instead of two dimensional; thus, Ajemba 306 teaches using different numbers/combinations of input parameters. Ajemba 306 further teaches that the system may partition the training data according to the same criteria used to partition the input signal feature space into a plurality of contiguous subspaces (Paragraph 0062), which Examiner interprets as the system partitioning each subspace into the same number of regions. As Ajemba discloses using multiple machine learning models to determine a glucose level, Ajemba 306 teaches partitioning the input signals in various ways that correspond to different models. Ajemba 306 discusses this type of partitioning generates a more accurate “mosaic” model rather than one single model. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Ajemba to incorporate the teachings of partitioning the input signals corresponding to machine learning models, the benefit being having an accurate mosaic model rather than only one model.
Conclusion
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
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