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 .
Response to Arguments
Applicant's arguments filed 7/21/26 have been fully considered but they are not persuasive. Specifying that devices that devices use network devices and memory does not change the scope of the claims. See rejections below. All arguments have been addressed in the body of the rejections.
Applicant argues claims are significantly more and integrate into a practical application.
In response, It is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do").
Claims do not specify a clear practical application. It is true that making physical changes to at least a portion of hardware is not a mental process but if applicant merely states e.g., “making physical changes” without any more description it would be so broad that it would be directed to insignificant extra-solution activity. If the physical changes were more specifically laid out, examiner may agree that the physical changes make a claim eligible but if the examiner cannot easily identify what physical changes are positively made the claimed physical changes amount to mere extra-solution activity.
In order for an abstract idea to be integrated into a practical application, the improvement in a given technical field must be a byproduct of the additional elements. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself”, as stated in MPEP 2106.5 (1). Applicant should state where within the claim limitations such an improvement is made.
Practical applications must be additional elements, not abstract ideas. Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception.
"It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception." paragraph is on 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022].
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
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 1-7, 10-11, 13-14, 17-18, 20-26, 29-30, 32-33, 36-37 and 134-136 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-7, 10-11, 13-14, 17-18, 20-26, 29-30, 32-33, 36-37, 134-136 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 20:
2A Prong 1:
determining, [at the processor], the blood glucose level for the subject based on the at least one voice biomarker feature value and the blood glucose level prediction model (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data; using AI to predict amounts to a mental process in the same way that a human can predict the weather with or without a computer);
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
computer implemented, system (claim 20), (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
providing, at a memory, a blood glucose level prediction model (reads on transmitting/receiving, adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
blood glucose level prediction model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
receiving, at a processor in communication with the memory, a voice sample from the subject (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
processor in communication with the memory (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
extracting, at the processor, at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
outputting, at an output device, the blood glucose level for the subject or an output based on the blood glucose level(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
computer implemented, system (claim 20), (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
providing, at a memory, a blood glucose level prediction model (reads on transmitting/receiving, adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
blood glucose level prediction model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
receiving, at a processor in communication with the memory, a voice sample from the subject (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
processor in communication with the memory (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
extracting, at the processor, at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
outputting, at an output device, the blood glucose level for the subject or an output based on the blood glucose level(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Further, the receiving/transmitting steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
2, 21. (Original) The method of claim 1, wherein the blood glucose level for the subject is a quantitative level, optionally the quantitative level expressed as mg/dL or mmol/L (further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
3, 22. (Original) The method of claim 1, wherein the blood glucose level for the subject is a category, optionally hypoglycemic, normal or hyperglycemic (covers any blood glucose level, further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
4, 23. (Currently Amended) The method of any one of claims 1 to 3, wherein the predetermined voice biomarker feature is listed in Table 3 or Table 6(further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
5, 24. (Original) The method of claim 4, wherein the method comprises:- extracting, at the processor, at least 5, 10, 25, 50, 75 or 100 voice biomarker feature values from the voice sample for at least 5, 10, 25, 50, 75 or 100 predetermined voice biomarker features listed in Table 3 or Table 6; and - determining, at the processor, the blood glucose level for the subject based on the at least 5, 10, 25, 50, 75 or 100 voice biomarker feature values and the blood glucose level prediction model(further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
6, 25. (Original) The method of claim 4, wherein the method comprises:- extracting, at the processor, voice biomarker feature values from the voice sample for 5, 6, 7, 8, 9, 10, or all of the predetermined voice biomarker features listed in Table 4, Table 7, Table 8 or Table 9; and - determining, at the processor, the blood glucose level for the subject based on the 5, 6, 7, 8, 9, 10, or all of the voice biomarker feature values and the blood glucose level prediction model(further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
7, 26. (Currently Amended) The method of any one of claims 1 to 6, wherein the blood glucose level prediction model comprises a statistical classifier and/or a statistical regressor; and wherein the statistical classifier comprises at least one selected from the group of a perceptron, a naive Bayes classifier, a decision tree, logistic reqression, K-Nearest Neighbor, an artificial neural network, machine learninq, deep learninq, random forest classifier and support vector machine(additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
8. (Cancelled)
9. (Cancelled)
10, 29. (Currently Amended) The method of claim 7 wherein:- the blood glucose level prediction model is an ensemble model, the ensemble model comprising n random forest classifiers(additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)); and - wherein the determining, at the processor, the blood glucose level comprises: - determining a prediction from each of the n random forest classifiers in the ensemble model; and - determining the blood glucose level based on an election of the predictions from the n random forest classifiers in the ensemble model(further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
11, 30. (Currently Amended) The method of any one of claims 1 to 10, further comprising preprocessing, at the processor, the voice sample by at least one selected from the group of:
performing a normalization of the voice sample (math concepts);
performing dynamic compression of the voice sample(math concepts); and
performing voice activity detection (VAD) of the voice sample (human can hear a voice and make a determination if speaker sounds tired with a low blood sugar level or hyper with a high blood glucose level), and the method further comprising: transmitting(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)), to a user device in network communication with the processor, the blood glucose level for the subject, wherein the outputting of the blood glucose level for the subject occurs at the user device.
12. (Cancelled)
13, 32. (Currently Amended) The method of any one of claims 1 to 12, further comprising determining the blood glucose level for the subject based on at least one clinicopathological value for the subject, optionally at least one of height, weight, BMI, diabetes status and blood pressure(human can hear a voice and make a determination if speaker sounds tired with a low blood sugar level or hyper with a high blood glucose level).
14, 33. (Currently Amended) The method of any one of claims 1 to 13, wherein the voice sample comprises a predetermined phrase vocalized by the subject(human can hear a voice and make a determination if speaker sounds tired with a low blood sugar level or hyper with a high blood glucose level), optionally wherein the predetermined phrase comprises a date or a time wherein the predetermined phrase is displayed to the subject on the user device (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)) Displaying on an interface: Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;.
15. (Cancelled)
16. (Cancelled)
17, 36. (Currently Amended) The method of any one of claims 1 to 16, wherein the voice sample is received from an audio sensor, optionally a microphone (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
18, 37. (Currently Amended) The method of any one of claims 1 to 17, for monitoring blood glucose levels in a healthy subject or in a subject with diabetes or prediabetes (further expand mental process user can predict/model blood glucose levels listening to a human speak in the same way that a human can predict the weather with or without a computer).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 10-11, 13-14, 17-18, 20-26, 29-30, 32-33, 36-37, 134-136 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In one interpretation of the claims they read on a voice biomarker or a voice signature being input into a prediction module with real world BG levels being output without using any blood samples, glucometer, blood level meters.
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Therefore, the claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
How exactly is voice data converted in BG levels when e.g., a patient is excited, scared, tired, drunk, stoned, depressed? What are the actual inputs to the prediction module? What is output, an exact BG level, a range or an indication that BG is high or low? How exactly does a person speak into a microphone and have an algorithm/model output that their blood glucose level is 187?
The NPL cited by applicant Hamdan et al. Vocal characteristics in patients with type 2 diabetes mellitus, 2012) discloses that there was no significant difference in any of the acoustic variables between patients with diabetes and the control, abstract.
Stating that blood glucose levels are determined from speech data with no further explanation is similar to saying one possesses a perpetual motion machine because the disclosure specifies a machine that spins or outputs energy for an infinite amount of time with no energy inputs. Where in the disclosure does it specifically state how one can make and use the invention of listening to a person’s voice and somehow outputting their blood glucose level?
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) 1-7, 10, 13-14, 17-18, 20-26, 29, 32-33, 36-37, 134-136 are rejected under 35 U.S.C. 103 as being unpatentable over Hadad (US 2019/0290172) in view of Tshope et al. (Estimating Blood Sugar from Voice Samples, A Preliminary Study, 2015 International Conference on Computational Science and Computational Intelligence).
1, 20. (Original) A computer-implemented (Fig. 1) method for determining a blood glucose level for a subject, the method comprising:
providing, at a memory (e.g., 110, 130, Fig. 1), a blood glucose level prediction model (“The GAIA model can use the user's historical data on food consumption as well as blood glucose and insulin levels to predict glucose responses”, 0246, 0269, 0271, 0276; “The insights and recommendation engine 230 can include a number of analytics and deep learning algorithms, including statistical analysis and artificial neural networks (ANN). The ANN can be a mathematical or computational model that is inspired by the structural or functional aspects of biological neural networks.”, 0234; 101, Fig. 60; prediction models, “The insights and recommendation engine 230 can determine various effects of food consumption on the user's body by applying at least one predictive model to a plurality of data sets”, 0233, 0235-9; 0245-6);
receiving, at a network device (Fig. 1), a voice sample from the subject(a voice biomarker is used to initiate communication or authenticate a private, personal health system that has data protected by HIPPA, “GUI-based software interface for voice recognition analysis”, 0028, 0228;
“voice-based food log”, 0221, 0307; voice recognition, 0352;
“user can initiate a communication with the virtual health assistant 280 by providing (e.g., via voice or text messaging) a question (e.g., regarding the user's diet, glucose or additional biomarker level, etc.) to the virtual health assistant 280”, 0348);
extracting, at a processor in communication with the memory and the network device, at least one voice biomarker feature value from the voice sample for at least one predetermined voice biomarker feature (voice recognition analysis, 0028; voice recorder, 0087; using biomarkers, 0089; “perform food tracking by textual and voice recognition analysis”, 0228; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270.”, 0342);
determining, at the processor, the blood glucose level for the subject based on the at least one voice biomarker feature value and the blood glucose level prediction model (based on biomarker reads on a plurality of factors or ways that the voice is used in the process of determining a BG level, does not claim using a closed ended list of factors that contribute to determining a BG level, just that one of a plurality of factors can be voice data, “At least one decision tree learning algorithm can be used to predict how foods previously consumed by a user may affect personalized biomarkers of the user (e.g. glucose level). At least one decision tree learning algorithm can also be used to predict how foods that the user has never consumed or other lifestyle events that may affect the user's biomarkers (e.g. glucose level)”, 0236; “predict a user's general biomarkers. In an example, the insights and recommendation engine 230 can predict a user's glucose metabolism”, 0245; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270.”, 0342, 0348; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284); and
outputting, at an output device, the blood glucose level for the subject or an output based on the blood glucose level(does not claim that a real BG meter is excluded in the process, user initiates communication via voice and informs VA about food they ate that will affect they blood glucose/A1C, “based on changes to the user's glucose level as measured by the glucose level monitor”, abstract, 0005-6, 0039; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270.”, 0342).
Examiner Note
Applicant discloses “[330] Optionally, the user device may be a smart speaker; the user input may be a voice query for the blood glucose level; the user prompt may be a voice prompt output; and the output device may be a speaker device. For example, a user may ask an Alexa device “Alexa, what is my blood glucose level””.
Hadad discloses “sending a voice message or a text message) “How is my glucose level doing?””, 0346 and using “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284).
However, Hadad fails to particularly call for determining, at the processor, the blood glucose level for the subject based on the at least one voice biomarker feature value and the blood glucose level prediction model.
Tshope teaches determining, at the processor, the blood glucose level for the subject based on the at least one voice biomarker feature value and the blood glucose level prediction model in the sense that voice is sampled and believed to comprise of features that pertain to a person’s BG level (“It is well-known that the human voice carries all kinds of information and depends on various factors. Therefore we conjectured that it may also be influenced by the blood sugar level. This paper presents a preliminary study which shows that a patient’s blood sugar condition actually seems to manifest itself in the voice.; - an extraction unit for extracting at least one voice”, abstract).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and using a patient’s voice to authenticate a private/personal medical system, to indicate what factors a person has experienced that may affect their BG and to hear their general state of mind, in case they are very weak and can barely talk due to a low blood level.
2, 21. (Original) The method of claim 1, wherein the blood glucose level for the subject is a quantitative level, optionally the quantitative level expressed as mg/dL or mmol/L (Figs. 24).
3, 22. (Original) The method of claim 1, wherein the blood glucose level for the subject is a category, optionally hypoglycemic, normal or hyperglycemic (reads on any level 0-600 or infinity, Figs. 23-24, 26).
4, 23. (Currently Amended) The method of any one of claims 1 to 3, wherein the predetermined voice biomarker feature is listed in Table 3 or Table 6 (biomarkers and voice are used, voice recognition analysis, 0028; voice recorder, 0087; using biomarkers, 0089; “perform food tracking by textual and voice recognition analysis”, 0228; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270”, 0342).
5, 24. (Original) The method of claim 4, wherein the method comprises:- extracting, at the processor, at least 5, 10, 25, 50, 75 or 100 voice biomarker feature values from the voice sample for at least 5, 10, 25, 50, 75 or 100 predetermined voice biomarker features listed in Table 3 or Table 6; and - determining, at the processor, the blood glucose level for the subject based on the at least 5, 10, 25, 50, 75 or 100 voice biomarker feature values and the blood glucose level prediction model(biomarkers and voice are used, voice recognition analysis, 0028; voice recorder, 0087; using biomarkers, 0089; “perform food tracking by textual and voice recognition analysis”, 0228; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270”, 0342).
Tshope teaches determining, at the processor, the blood glucose level for the subject based on the at least 5, 10, 25, 50, 75 or 100 voice biomarker feature values and the blood glucose level prediction model in the sense that voice is sampled and believed to comprise of features that pertain to a person’s BG level (“It is well-known that the human voice carries all kinds of information and depends on various factors. Therefore we conjectured that it may also be influenced by the blood sugar level. This paper presents a preliminary study which shows that a patient’s blood sugar condition actually seems to manifest itself in the voice; an extraction unit for extracting at least one voice”, abstract).
6, 25. (Original) The method of claim 4, wherein the method comprises:- extracting, at the processor, voice biomarker feature values from the voice sample for 5, 6, 7, 8, 9, 10, or all of the predetermined voice biomarker features listed in Table 4, Table 7, Table 8 or Table 9(biomarkers and voice are used, voice recognition analysis, 0028; voice recorder, 0087; using biomarkers, 0089; “perform food tracking by textual and voice recognition analysis”, 0228; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270”, 0342); and - determining, at the processor, the blood glucose level for the subject based on the 5, 6, 7, 8, 9, 10, or all of the voice biomarker feature values and the blood glucose level prediction model.
Tshope teaches determining, at the processor, the blood glucose level for the subject based on the 5, 6, 7, 8, 9, 10, or all of the voice biomarker feature values and the blood glucose level prediction model in the sense that voice is sampled and believed to comprise of features that pertain to a person’s BG level (“It is well-known that the human voice carries all kinds of information and depends on various factors. Therefore we conjectured that it may also be influenced by the blood sugar level. This paper presents a preliminary study which shows that a patient’s blood sugar condition actually seems to manifest itself in the voice; an extraction unit for extracting at least one voice”, abstract).
7, 26. (Currently Amended) The method of any one of claims 1 to 6, wherein the blood glucose level prediction model comprises a statistical classifier and/or a statistical regressor; and wherein the statistical classifier comprises at least one selected from the group of
a perceptron,
a naive Bayes classifier,
a decision tree,
logistic reqression (The model can include tfidf transformation and a logistic regression classifier.),
K-Nearest Neighbor,
an artificial neural network(“The GAIA model can use the user's historical data on food consumption as well as blood glucose and insulin levels to predict glucose responses”, 0246, 0269, 0271, 0276; “The insights and recommendation engine 230 can include a number of analytics and deep learning algorithms, including statistical analysis and artificial neural networks (ANN). The ANN can be a mathematical or computational model that is inspired by the structural or functional aspects of biological neural networks.”, 0234),
machine learning,
deep learning,
random forest classifier and
support vector machine.
8. (Cancelled)
9. (Cancelled)
10, 29. (Currently Amended) The method of claim 7 wherein:- the blood glucose level prediction model is an ensemble model(using a plurality of models, “The insights and recommendation engine 230 can use one or more biomathematical models described in the present disclosure to predict a user's general biomarkers. In an example, the insights and recommendation engine 230 can predict a user's glucose metabolism”, 0245; “The GAIA model can be defined by using one or more models for glucose G(t) and insulin I(t) in the blood”, 0247; “The training data set(s) can callow the machine learning algorithm(s) to learn a plurality of parameters to generate one or more models (e.g., mathematical models, classifiers)”, 0345;
“the food categories can be classified into a number of different models (e.g. 4 different models), each of which builds upon the results of the previous model”, 0168-0170; “As an alternative or in addition to the ANN, the insights and recommendation engine 230 can include biomathematical predictive models that use metric spaces, decision trees, and decision tree learning algorithms”, 0235; examiner takes official notice that using an ensemble of classifiers/models is well-known and be used to compare/combine their outputs), the ensemble model comprising n random forest classifiers (“Logistic Regression, Decision Tree, or Random Forest”, 0131); and - wherein the determining, at the processor, the blood glucose level comprises: - determining a prediction from each of the n random forest classifiers in the ensemble model; and - determining the blood glucose level based on an election of the predictions from the n random forest classifiers in the ensemble model (“The GAIA model can be defined by using one or more models for glucose G(t) and insulin I(t) in the blood”, 0247).
12. (Cancelled)
13, 32. (Currently Amended) The method of any one of claims 1 to 12, further comprising determining the blood glucose level for the subject based on at least one clinicopathological value for the subject, optionally at least one of height, weight, BMI, diabetes status and blood pressure (based on biomarker reads on a plurality of factors or ways that the voice is used in the process of determining a BG level, does not claim using a closed ended list of factors that contribute to determining a BG level, just that one of a plurality of factors can be voice data, “At least one decision tree learning algorithm can be used to predict how foods previously consumed by a user may affect personalized biomarkers of the user (e.g. glucose level). At least one decision tree learning algorithm can also be used to predict how foods that the user has never consumed or other lifestyle events that may affect the user's biomarkers (e.g. glucose level)”, 0236; “One or more personalized digital signatures can be unique to a number of other factors related to the individual, including gender, age, race, genetics, microbiome, religious dietary restrictions, geography, height, weight, and time of the day, month, or year”, 0232;
“the user's glucose metabolism can depend on one or more factors, including, but are not limited to, the user's glucose and insulin production levels, blood glucose level prior to food consumption, carbohydrate content in the food, insulin level in the body, blood pressure, physical activity, the user's insulin sensitivity, time of the day, stress, illness, pregnancy, medications, etc.”, 0245, 0275, 0283, 0290;
“predict a user's general biomarkers. In an example, the insights and recommendation engine 230 can predict a user's glucose metabolism”, 0245; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270.”, 0342, 0348; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284).
14, 33. (Currently Amended) The method of any one of claims 1 to 13, wherein the voice sample comprises a predetermined phrase vocalized by the subject, optionally wherein the predetermined phrase comprises a date or a time wherein the predetermined phrase is displayed to the subject on the user device (“Exemplary windows of the GUI-based software interface for the voice recognition analysis function are illustrated in FIGS. 18A-18C. The GUI-based software interface window can display example sentence structures that users may use to record a voice message to the device/data hub 220, as shown in FIG. 18A.”, 0228; “The window can display a change in the user's blood glucose level at one specific time point following consumption of the food item. Additionally, the window can display a report on the user's blood glucose level profile within a time period.”, 0243).
15. (Cancelled)
16. (Cancelled)
17, 36. (Currently Amended) The method of any one of claims 1 to 16, wherein the voice sample is received from an audio sensor, optionally a microphone (“The devices 110 can include a wearable device 112 (e.g., a smart watch, fitness tracker, etc.), a mobile device 114 (e.g., a cell phone, a smart phone, a voice recorder, etc.),”, 0087).
18, 37. (Currently Amended) The method of any one of claims 1 to 17, for monitoring blood glucose levels in a “healthy subject” (subjective, not defined) or in a subject with diabetes or prediabetes (0106, 0229, 0242, 0282).
134. (New) The method of claim 7, wherein the audio sensor is a microphone(“The devices 110 can include a wearable device 112 (e.g., a smart watch, fitness tracker, etc.), a mobile device 114 (e.g., a cell phone, a smart phone, a voice recorder, etc.), can’t be good”, 0087 a voice biomarker is used to initiate communication or authenticate a private, personal health system that has data protected by HIPPA, “GUI-based software interface for voice recognition analysis”, 0028, 0228;
“voice-based food log”, 0221, 0307; voice recognition, 0352).
135. (New) The system of claim 33, wherein the user device is a mobile device(“The devices 110 can include a wearable device 112 (e.g., a smart watch, fitness tracker, etc.), a mobile device 114 (e.g., a cell phone, a smart phone, a voice recorder, etc.), can’t be good”, 0087).
136. (New) The system of claim 36, wherein the audio sensor is a microphone(“The devices 110 can include a wearable device 112 (e.g., a smart watch, fitness tracker, etc.), a mobile device 114 (e.g., a cell phone, a smart phone, a voice recorder, etc.), can’t be good”, 0087 a voice biomarker is used to initiate communication or authenticate a private, personal health system that has data protected by HIPPA, “GUI-based software interface for voice recognition analysis”, 0028, 0228;
“voice-based food log”, 0221, 0307; voice recognition, 0352).
Claim Rejections - 35 USC § 103
Claims 11 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hadad and Tshope above in view of Fujita (US 2022/0157293).
11, 30. (Currently Amended) The method of any one of claims 1 to 10, further comprising preprocessing, at the processor, the voice sample by at least one selected from the group of:
performing a normalization of the voice sample;
performing dynamic compression of the voice sample; and
performing voice activity detection (VAD) of the voice sample, and the method further comprising:
- transmitting, to a user device in network communication with the processor, the blood glucose level for the subject, wherein the outputting of the blood glucose level for the subject occurs at the user device(does not claim that a real BG meter is excluded in the process, user initiates communication via voice and informs VA about food they ate that will affect they blood glucose/A1C, “based on changes to the user's glucose level as measured by the glucose level monitor”, abstract, 0005-6, 0039; “additional biomarkers (e.g. glucose level, insulin level, heart rate, etc.”, 0284; “The DGM 101 can provide the GUI-based software interface for the user to talk to the healthcare or fitness specialist 270.”, 0342).
Hadad fails to disclose the details of how the voice recognition operates and pre-processing the voice data.
Fujita (US 2022/0157293) teaches preprocessing voice data using VAD (“For example, as preprocessing, the response generation unit 50 decomposes an input signal of voice or the like into frames and subjects the frames to Fourier transform to obtain a spectrum for each frame. Thereafter, the response generation unit 50 detects a non-voice section using voice section detection (voice activity detection (VAD)),”, 0116).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and preprocessing voice data can filter out noise or other anomalies.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hallock US 2020/0053558) teaches obtaining a plurality of voice biomarkers (signature 0074).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123
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