DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
Claims 1-20 are pending.
Claims 1, 18 and 20 are independent.
Claims 12 and 14 are objected to.
Claims 1-20 are rejected.
Priority
This US Application 17/872,823 (07/25/2022) is a CON of US Application 16/917,421 (06/30/2020), as reflected in the filing receipt mailed on 08/05/2022. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-20 is 06/30/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/25/2022, 08/08/2024 and 07/31/2025 were considered.
Claim objections
Claims 12 and 14 are objected to because of the following informalities related to grammar/punctuation. Appropriate correction is required.
Claim 12 recites "at least one of ", which should be amended to recite "at least one of:" for proper punctuation preceding a list of items. In contrast, claim 17 does not present the same issue.
Claim 14 recites "a time within range measure", which should be amended to recite "a time within a range measure" to maintain consistent claim language.
Claim 20 recites "a system" versus "a prediction system". The recited "a system" should include a modifier to better distinguish the "a prediction system".
In claim 20, the grammatical construction and punctuation are unclear such that the recited "…period, and predict" is improper. The claims should read "X and Y" not "X, and Y."
Claim interpretation
112(f) interpretation of particular recitations
The recited "wearable glucose monitoring device" (claim 20)
The above recitation includes means (or an equivalent, nonce term, here "device") and function and/or result (here "wearable glucose monitoring"), but the recitation does not invoke 112/f because it is interpreted as well-known. MPEP 2181.I.A,3rd para. pertains with analogy to structures having "sufficiently definite meaning," such as "filters" and "brakes."
The recited "storage device" (claim 20)
The above recitation includes means (or an equivalent, nonce term, here "device") and function and/or result (here "storage" interpreted as the function of storing data), but the recitation does not invoke 112/f because it is interpreted as well-known. MPEP 2181.I.A,3rd para. pertains with analogy to structures having "sufficiently definite meaning," such as "filters" and "brakes."
The recited "prediction system" (claim 20)
The above recitations include means (or an equivalent, nonce term, here "system") and function and/or result (here "prediction" interpreted as the function of evaluation).
The above recitations are not sufficiently well-known and not accompanied by sufficient structure in the claims to prevent invoking. Therefore, each is interpreted as invoking.
Having invoked, each above recitation has been analyzed as clearly linking to sufficient structure in the specification, as supported at ([0108]). Thus, the above recitations have been interpreted as properly invoking 112(f).
Claim Terminology
In claim 20, the recited "to collect glucose measurements of a user" reads on intended use. This interpretation could be prevented by amending to "configured to." Same applies to "to maintain..." to the end of the element and "to obtain..." to the end of the claim, again reading on intended use. In the latter instance, to avoid possible intended use interpretation, the claim might be amended to more directly recite the steps, e.g. obtaining, predicting, processing, extracting, providing. However, to avoid a 112b rejection, it would have to be clear what "system" structure corresponds to any recited process steps, e.g. "system" structure in the form of stored computer readable instructions configured according to the process steps.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-19 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claim 1, the relationship between the "user" in the recited "obtaining glucose measurements of a user; processing the glucose measurements of the user" and the later recited "the user population" is unclear. If both recitations are referring to the same "user," then the claim should recite "glucose measurements of users in a user population" for proper relationship between the recited terms. In contrast, claim 18 does not present the same issue.
In claim 1, is unclear whether the recited step of "...models generated..." ("predicting" step) (i) should be interpreted as product-by-process (PbP) or (ii) requires the step to be performed as part of the claimed "method." (i) If PbP, then the recited "...models" are limited according to any structure clearly required by the recited PbP limitation of having been "generated." The recited process of having been "generated" is not itself claimed and is limiting only to the extent that the structure of the "models" is clearly required to be limited by that process or step. Regarding product-by-process limitations in method claims, MPEP 2113 pertains, as well as, for example, Biogen MA, Inc. v. EMD Serono, Inc. (Fed. Cir. 9-28-2020, precedential). (ii) If not PbP, then it should be clarified that the "generated" step is required as part of the recited "method," for example, by amending to recite "generating models …" Generally, if the language of a claim, given its broadest reasonable interpretation, is such that PHOSITA would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) is appropriate.
In claim 1, the recited "...measurements provided by..." ("predicting" step) repeats the issue above.
In claim 5, the recited "...measurements... are collected..." repeats the issue above.
In claim 6, the recited "is inserted" repeats the issue above.
In claim 7, the recited "collected" repeats the issue above.
In claim 8, the recited "are collected" repeats the issue above.
In claim 9, the recited "provided" repeats the issue above.
In claim 10, it is unclear how the recited "...independent of..." should be interpreted. In light of this instant specification [0008], such term is being interpreted as comprising HbA1c. Same issue applies to "...includes... labels of traces of..." in claim 11, which is being interpreted as simply glucose measurements data [0088].
Relationship unclear between claim 1 and claim 5 instantiations of "devices" and "device." To overcome this rejection, the claims could be amended to recite something like "first devices" and "second device." Relatedly, in claim 9, the relationship is unclear between this instance of "devices" and its relationship to claim 1.
Relationship unclear between claim 1 and claim 4 "users"; if these terms are related to the same "users" then add "the," if different then maybe "first users" and "second users".
Same issue applies to claims 9-10.
Similarly, in claim 19, the relationship is unclear between "a respective user of the user population" and the recited "user in a user population" and "respective user" in claim 18.
In claim 16, the recited "the machine learning model" refers to a singular model, which lacks antecedent basis. Claim 16 depends from claim 1, which recites "machine learning models."
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 1-20 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Claims 1-17 are directed to a 101 process, here a "method," with process steps such as "obtaining" etc. Claims 18-19 are analyzed similarly.
Claim 20 is directed to a 101 machine or manufacture, here a "system," comprising at least one non-transitory element such as "sensor."
[Step 1: claims 1-20: YES]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
Mathematical concepts recited in instant claims 1, 4, 9, 14, 16, 18 and 20, include the terms:
• "machine learning model(s)" (claims 1, 9, 16, 18 and 20);
• "predict/predicting/predicted/prediction" (claims 1, 4, 16, 18 and 20);
• "generating/adjusting … machine learning model(s)" (claim 9);
• "second glucose level that is less than the first glucose level" claim 14);
• "training a machine learning model/training data" (claim 18); and
• "one or more values" (claim 18).
These terms are interpreted as mathematical concepts. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one having ordinary skill in the art. In this instant specification, [0107] describes the claimed machine learning model(s) using probability models to predict outputs, which indicates the use of math. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm, thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include: "comparing training classifications of diabetes" (claim 9).
Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of making data comparisons.
Dependent claims 2-4, 9 and 13-16 recite further details about "predicting diabetes classification"; dependent claims 5-12 recite further details about the "measurements" obtained; not reciting any additional non-abstract elements; all reciting further aspects of the information being analyzed, the manner in which that analysis is performed.
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The instant claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)).
[Step 2A Prong One: claims 1-20 – Yes ]
Step 2A, 1st prong, 1st Mayo/Alice question: natural law -- MPEP 2106.04
The instant claims recite a natural correlation by correlating the measurement of glucose levels naturally found in the body with a diabetes classification. (see MPEP 2106.04(b).I).
[Step 2A, 1st prong, law of nature: claims 1-20: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
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, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1 and 17-18 recite additional elements that are not abstract ideas:
• "obtain/obtaining … measurements" (claim 1, 9, 18 and 20) and
• "output/outputting" data (claims 1 and 18).
Dependent claim 17 recites further details about the outputting step including a visual representation of the glucose measurements.
Claims 1 and 18 are interpreted as requiring the use of a computer [0034]. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The recited limitations in these claims are interpreted as requiring multiple computer parts (processor/ memory), not requiring specialized hardware other than a generic computer, which does not integrate the abstract idea into a practical application. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions.
Claims directed to "obtaining/outputting," constitutes just necessary data gathering and outputting and therefore correspond to insignificant extra-solution activity.
Claim 17 recites a "visual representation" (claim 17) being interpreted as data output and as such insignificant extra-solution activity.
Further, the limitation reciting "using the instances of training data" (claim 18) provides instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f), which provides the following considerations for determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Providing "one or more treatment recommendations for the user based on the diabetes classification" (claim 17) is not interpreted as a practical application because the recitation does not provide an treatment to the patient. The "one or more treatment recommendations for the user based on the diabetes classification" reads on insignificant extra-solution activity (MPEP 2106.05(g) because they merely serve as necessary data gathering/outputting and do not amount to a practical application.
Hence, the identified additional elements are instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore, the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination it is not yet the case that any of the Step 2A, Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the second consideration at MPEP § 2106.04(d)(2), the record, including for example the specification, does not yet clearly disclose an explanation of a particular treatment or prophylaxis for a disease or medical condition. The claims do not yet clearly result in such treatment (e.g. specification: [0041]).
[Step 2A Prong Two: claims 1-20 - No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception.
A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1 and 17-18 recite a computer or computer functions, interpreted as instructions to apply the identified abstract idea(s) using a computer, where the computer does not impose meaningful limitations on the judicial exceptions, which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general purpose computer and the machine learning model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
As discussed above, the treatment limitations cannot integrate a judicial exception as mere recommendations. When the claims are considered as a whole, they do not integrate the abstract idea into a practical application. See MPEP 2106.05(a) and 2106.05(h).
[Step 2B: claims 1-20- No]
Conclusion: Instant claims are directed to non-statutory subject matter
For these reasons, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and not clearly anything significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(l) the claimed invention was patented, described in a printed publication, or in public use, on sale,
or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-8, 11-12, 15-16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ramazi ("Multi-modal predictive models of diabetes progression." Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (2019)) as cited on the attached Form PTO-892.
Ramazi discloses a model to predict the progression of Type 2 diabetes reporting the patients’ diabetic status (i.e. prediction of diabetes classification) (pg. 254 col. 1 para. 2). Bullet points indicate the teachings of the instant elements over the prior art. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 recites:
obtaining glucose measurements of a user; processing the glucose measurements of the user to extract one or more glucose features; predicting a diabetes classification of the user by providing the one or more extracted glucose features to one or more machine learning models as input, the one or more machine learning models generated based on historical glucose measurements provided by glucose monitoring devices worn by users of a user population and historical outcome data of the user population; and outputting the diabetes classification.
• Ramazi teaches a model to predict the progression of Type 2 diabetes reporting the patients’ diabetic status (i.e. predicting/outputting a diabetes classification of the user) (pg. 254 col. 1 para. 2) using continuous glucose monitoring (CGM) (i.e. processing the glucose measurements of the user) and a recurrent neural network model (pg. 255 col. 2 para. 3); wherein demographic measure in a year interval (i.e. historical outcome data of the user population), lab tests, and wearable sensor data (i.e. glucose monitoring devices worn by users) were used to develop the model (pg. 254 col. 1 para. 2); wherein a recurrent neural network is used to boost prediction performance learning the patterns from CGM time-dependent values of the signals (i.e. glucose features to one or more machine learning models as input) (pg. 255 col. 2 para. 3); wherein the output is the estimated change of a patient’s HbA1c (pg. 257 col. 1 para. 1).
Claim 4 recites:
wherein: the diabetes classification is an indication of one or more adverse effects of diabetes the user is predicted to experience; and the historical outcome data describes adverse effects of diabetes observed in users of the user population.
• Ramazi teaches studies on modeling the complex relations between diabetes and other conditions such as high blood pressure and obesity and studies that combined fuzzy logic classifiers and a clustering method to classify diabetic eye damage (i.e. one or more adverse effects of diabetes the user is predicted to experience) (pg. 254 col. 2 para. 1).
Claim 5 recites:
wherein the glucose measurements of the user are collected by a wearable glucose monitoring device during an observation period.
• Ramazi teaches the prediction of patient's diabetic status via the monitoring of the wearable CGM over a short period of time as timeseries signals to predict patients’ diabetic status in one year (i.e. time-sequenced glucose measurements collected by the wearable glucose monitoring device during the observation period) (pg. 254 col. 1 para. 2).
Claim 6 recites:
wherein the wearable glucose monitoring device includes a sensor that is inserted subcutaneously into skin of the user during the observation period to collect the glucose measurements.
• Ramazi teaches a device that uses a subcutaneous sensor to measure interstitial fluid glucose levels (pg. 2 col. 1 para. 3).
Claim 7 recites:
wherein the glucose measurements comprise time-sequenced glucose measurements collected by the wearable glucose monitoring device during the observation period.
• Ramazi teaches the recitation above as applied to claim 5.
Claim 8 recites:
wherein the time-sequenced glucose measurements are collected by the wearable glucose monitoring device continuously at predetermined intervals during the observation period.
• Ramazi teaches a CGM wearable device that measures the glucose concentration of the blood in 1 to 5 minutes intervals (i.e. predetermined intervals) to capture the fluctuations in blood glucose levels (pg. 254 col. 1 para. 1).
Claim 11 recites:
wherein the historical outcome data includes labels of traces of the historical glucose measurements that indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures.
• Ramazi teaches the use of clinical data that included the HbA1c (i.e. glucose related measurements for diagnostic measures) of each subject measured twice while separated by an one year interval (i.e. historical outcome data) to indicate the severity of diabetes status (i.e. indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures) (pg. 255 col. 1 para. 2).
Claim 12 recites:
wherein the one or more diagnostic measures include at least one of Hemoglobin A1c (HbA1c), an Oral Glucose Tolerance Test (OGTT), or Fasting Plasma Glucose (FPG).
• Ramazi teaches the recitation above as applied to claim 11.
Claim 15 recites:
wherein the one or more extracted glucose features include a rate-of-change measure corresponding to a difference in glucose measurements over a unit of time.
• Ramazi teaches the use of a neural network model to predict the value of HbA1c over a period of time of one year (i.e. glucose measurements over a unit of time) (pg. 258 col. 1 para. 3) classifying whether the HbA1c value was increased or decreased base of the change in HbA1c values (i.e. rate-of-change measure) (pg. 257 col. 2 para. 1).
Claim 16 recites:
wherein the machine learning model predicts the diabetes classification of the user using at least two extracted glucose features.
• Ramazi teaches the use of blood glucose measurements data (pg. 255 col. 1 para. 3) and a framework for HbA1c estimation (i.e. prediction of diabetes classification of the user using at least two extracted glucose features) (pg. 256 col. 1 para. 1).
Claim 18 recites:
obtaining historical glucose measurements of users in a user population; obtaining historical outcome data of the users in the user population; generating instances of training data that include a training input portion and an expected output portion, the training input portion including one or more features of a respective user's glucose measurements, and the expected output portion including one or more values of outcome data corresponding to the respective user; and training a machine learning model to predict a diabetes classification using the instances of training data.
• Ramazi teaches a CGM time-series and HbA1c input (i.e. one or more features of a respective user's glucose measurements) to train the model (i.e. training input) (pg. 255 col. 1 para. 3); wherein the model architecture used a recurrent neural network for learning patterns of the wearable data (pg. 256 Fig. 3) to output the estimated HbA1c change of a patient (i.e. expected output portion) (pg. 257 col. 1 para. 1) and classify diabetes improvement or deterioration (i.e. machine learning model to predict a diabetes classification using the instances of training data) (pg. 257 col. 2 para. 1).
Claim 19 recites:
wherein the historical outcome data indicates whether or not a respective user of the user population is clinically diagnosed with diabetes.
• Ramazi teaches the use of clinical data that included the HbA1c (i.e. glucose related measurements for diagnostic measures) of each subject measured twice while separated by an one year interval (i.e. historical outcome data) to indicate the severity of diabetes status (i.e. indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures) (pg. 255 col. 1 para. 2).
Claim 20 recites:
a wearable glucose monitoring device comprising a sensor to collect glucose measurements of a user; a storage device to maintain the glucose measurements of the user collected during an observation period; and a prediction system to obtain the glucose measurements of the user collected during the observation period, and predict a diabetes classification of the user by processing the glucose measurements of the user to extract one or more glucose features and providing the one or more extracted glucose features to one or more machine learning models as input.
• Ramazi teaches a model to predict the progression of Type 2 diabetes and patients’ diabetic status by CGM (i.e. predict a diabetes classification of the user by processing the glucose measurements of the user to extract one or more glucose features) using wearable sensor data (i.e. wearable glucose monitoring device comprising a sensor) (pg. 254 col. 1 para. 2); wherein recurrent neural network to boost prediction performance (i.e. prediction system) learning the patterns from CGM time-dependent values of the signals (i.e. one or more extracted glucose features to one or more machine learning models as input) (pg. 255 col. 2 para. 3); wherein collected data by the end of the seven-day period (i.e. storage device) contained between 1445 to 2016 consecutive CGM measurements for each patient (i.e. obtain the glucose measurements of the user collected during the observation period) (pg. 255 col. 1 para. 3).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claim 2 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ramazi as applied to claim 1 in the 102 rejection above further in view of Acciaroli ("Diabetes and prediabetes classification using glycemic variability indices from continuous glucose monitoring data." Journal of diabetes science and technology 12(1):105-113 (2018)), as cited on the attached Form PTO-892.
Claim 2 recites:
wherein the diabetes classification is an indication describing a state of the user as having one of diabetes, prediabetes, or no diabetes.
• Ramazi does not teach the classification of the state of the user as diabetes, prediabetes, or no diabetes. • However, Acciaroli teaches a machine learning approach to distinguish healthy (i.e. user with no diabetes) from impaired glucose tolerance (IGT) and type 2 diabetes subjects (i.e. user with diabetes) using a set of 25 well established CGM-based measurements (i.e. user based glucose features) (pg. 106 col. 1 para. 1); wherein state of prediabetes is measured as IGT (i.e. user with prediabetes) (pg. 106 col. 1 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claim 2, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ramazi in view of Acciaroli because all references disclose methods for the prediction of diabetes classification using glucose monitoring data. The motivation would have been to permit the construction of more robust classifiers and improve the glycemic variability indices-based classification performance (pg. 110 col. 1 para. 4 Acciaroli). Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Ramazi to the methods by Acciaroli because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the prediction of diabetes classification using glucose monitoring data.
B. Claim 3 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ramazi as applied to claim 1 in the 102 rejection above further in view of Yoffe ("Early diagnosis of gestational diabetes mellitus using circulating microRNAs." European journal of endocrinology 181(5):565-577 (2019)), as cited on the attached Form PTO-892.
Claim 3 recites:
wherein the diabetes classification is an indication describing a state of the user as having gestational diabetes or no gestational diabetes.
• Ramazi does not teach the recitation above. • However, Yoffe teaches sample classification using machine learning models that were trained on datasets (pg. 4 col. 1 para. 3) to distinguish gestational diabetes mellitus from healthy samples by evaluating predictive values (pg. 1 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 3, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ramazi in view of Yoffe because all references disclose methods for applying machine learning models to sample classification problems. The motivation would have been to reduce the risk of gestational diabetes mellitus for mother and offspring (pg. 10 col. 2 para. 1 Yoffe). Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Ramazi to the methods by Yoffe because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for applying machine learning models to sample classification problems.
C. Claims 9-10 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ramazi as applied to claim 1 in the 102 rejection above further in view of Miranda "Multi-objective optimization for self-adjusting weighted gradient in machine learning tasks." arXiv preprint arXiv:1506.01113 (2015)), as cited on the attached Form PTO-892.
Claim 9 recites:
obtaining the historical glucose measurements and the historical outcome data of the user population, the historical glucose measurements provided by glucose monitoring devices worn by users of the user population; and generating the one or more machine learning models by providing the historical glucose measurements to the one or more machine learning models, comparing training classifications of diabetes received from the one or more machine learning models to diabetes classifications indicated by the historical outcome data, and adjusting weights of the one or more machine learning models.
• Ramazi teaches the use of a recurrent neural network to analyze and classify a sequence of temporal data signals of any length consisting of input and output gates and neuron nodes with weighted connections (pg. 256 col. 2 para. 3) to process and learn time-series signals where the sequential data points are correlated (pg. 257 col. 1 para. 1); wherein demographic data and lab test results of the patients were added to our deep model (i.e. classifications indicated by the historical outcome data) (pg. 257 col. 1 para. 2) and used for comparison of diabetes classification using CGM only to classifications involving demographic data and lab results (i.e. historical outcome data) (pg. 258 Table 1 lines 1 and 3)
does not teach the classification of the state of the user as diabetes, prediabetes, or no diabetes. • Ramazi does not teach adjusting weights of the machine leaning model.
• However, Miranda teaches the use of gradient as the operator for self-adjusting weights to achieve the correct prediction of samples' labels (pg. 4 para. 4).
Claim 10 recites:
wherein the historical outcome data is associated with one or more diagnostic measures independent of the historical glucose measurements provided by the glucose monitoring devices worn by users of the user population.
• Ramazi teaches the use of biomedical indexes collected twice while separated by an one year interval (i.e. historical outcome data), wherein the data consisted of cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, non-LDL cholesterol, and VLDL cholesterol (i.e. diagnostic measures independent of the historical glucose measurements provided by the glucose monitoring devices worn by users) (pg. 255 col. 1 para. 2)
Rationale for combining (MPEP §2142-2143)
Regarding claims 9-10, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ramazi in view of Miranda because all references disclose methods for the use of machine learning methods to process data. The motivation would have been to improve the error expressed by optimization methods (pg. 110 col. 1 para. 4 Miranda). Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Ramazi to the methods by Miranda because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the use of machine learning methods to process data.
C. Claims 13-14 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ramazi as applied to claim 1 in the 102 rejection above further in view of Cappon ("Continuous glucose monitoring sensors for diabetes management: a review of technologies and applications." Diabetes & metabolism journal 43(4):383 (2019)), as cited on the attached Form PTO-892.
Claim 13 recites:
wherein the one or more extracted glucose features include a time over threshold measure corresponding to an amount of time during an observation period that the glucose measurements of the user are above a glucose threshold.
• Cappon teaches real-time CGM systems for personal use wherein the system monitors glucose measurements every 1-5 minutes (pg. 383 col. 2 para. 2); wherein data obtained by the CGM system revealed hyperglycemic episodes and its duration for data points above 180 mg/dL blood glucose (i.e. glucose measurement above a threshold measure) (pg. 384 Fig. 1A).
Claim 14 recites:
wherein one or more extracted glucose features include a time within range measure corresponding to an amount of time during an observation period that the glucose measurements of the user are between a first glucose level and a second glucose level that is less than the first glucose level.
• Cappon teaches real-time CGM systems for personal use wherein the system monitors glucose measurements every 1-5 minutes (pg. 383 col. 2 para. 2); wherein data obtained by the CGM system revealed hyperglycemic episodes and its duration for data points above 180 mg/dL blood glucose (i.e. a first glucose measurement level) and hypoglycemic episodes and its duration for data points below 70 mg/dL blood glucose (i.e. a second glucose measurement level less than the first) with remaining data points shown in between the first and second levels (pg. 384 Fig. 1A).
Claim 17 recites:
wherein the outputting comprises outputting a glucose observation report that includes the diabetes classification and at least one of: one or more treatment recommendations for the user based on the diabetes classification; a visual representation of the glucose measurements; or one or more glucose statistics of the user generated based on the glucose measurements.
• Cappon teaches real-time CGM systems wherein a report with summary statistics and daily glycemic patterns (i.e. outputting a glucose observation report) has been included for visualization of CGM data (i.e. visual representation of the glucose measurements) (pg. 387 col. 2 para. 2); wherein decision support systems based on CGM systems improve diabetes treatments via a bolus calculator algorithm based on neural networks providing personalized insulin recommendations (pg. 390 col. 2 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claims 13-14 and 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Ramazi in view of Cappon because all references disclose methods for diabetes management using glucose monitoring data. The motivation would have been to incorporate a better tailoring of diabetes therapy to the patient’s lifestyle and habits (pg. 392 col. 2 para. 1 Cappon). Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Ramazi to the methods by Cappon because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for diabetes management using glucose monitoring data.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-8, 10-14, 16, 26, and 34 of US Patent No. 11,426,102 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are anticipated by the claims of the reference patent, as set forth in the following table.
Instant application
US Patent No. 11,426,102 B2
Claim
Limitation
Claim
Limitation
1
obtaining glucose measurements of a user; processing the glucose measurements of the user to extract one or more glucose features; predicting a diabetes classification of the user by providing the one or more extracted glucose features to one or more machine learning models as input, the one or more machine learning models generated based on historical glucose measurements provided by glucose monitoring devices worn by users of a user population and historical outcome data of the user population; and outputting the diabetes classification
1
obtaining glucose measurements of a user, …; processing the glucose measurements of the user to extract one or more glucose features; predicting a diabetes classification of the user by providing the one or more extracted glucose features to one or more machine learning models as input, the one or more machine learning models generated based on historical glucose measurements provided by glucose monitoring devices worn by users of a user population over respective observation periods spanning multiple days and historical outcome data of the user population; and outputting the diabetes classification
2
wherein the diabetes classification is an indication describing a state of the user as having one of diabetes, prediabetes, or no diabetes
2
wherein the diabetes classification is an indication describing a state of
the user during the observation period as having one of diabetes, prediabetes, or no diabetes
3
wherein the diabetes classification is an indication describing a state of the user as having gestational diabetes or no gestational diabetes
3
wherein the diabetes classification is an indication describing a state of
the user during the observation period as having gestational diabetes or no gestational diabetes
4
wherein: the diabetes classification is an indication of one or more adverse effects of diabetes the user is predicted to experience; and the historical outcome data describes adverse effects of diabetes observed in users of the user population.
4
wherein the diabetes classification is an indication of one or more
adverse effects of diabetes the user is predicted to experience; and the historical outcome data describes adverse effects of diabetes observed in users of the user population.
5
wherein the glucose measurements of the user are collected by a wearable glucose monitoring device during an observation period
1
the glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days
6
wherein the wearable glucose monitoring device includes a sensor that is inserted subcutaneously into skin of the user during the observation period to collect the glucose measurements.
5
wherein the wearable glucose monitoring device includes a sensor that is inserted subcutaneously into skin of the user during the observation period to collect the glucose measurements
7
wherein the glucose measurements comprise time-sequenced glucose measurements collected by the wearable glucose monitoring device during the observation period
6
wherein the glucose measurements comprise time-sequenced glucose
measurements collected by the wearable glucose monitoring
device during the observation period
8
wherein the time-sequenced glucose measurements are collected by the wearable glucose monitoring device continuously at predetermined intervals during the observation period
7
wherein the time-sequenced glucose measurements are collected by the
wearable glucose monitoring device continuously at predetermined
intervals during the observation period
9
obtaining the historical glucose measurements and the historical outcome data of the user population, the historical glucose measurements provided by glucose monitoring devices worn by users of the user population; and generating the one or more machine learning models by providing the historical glucose measurements to the one or more machine learning models, comparing training classifications of diabetes received from the one or more machine learning models to diabetes classifications indicated by the historical outcome data, and adjusting weights of the one or more machine learning models.
8
obtaining the historical glucose measurements and the historical outcome data of the user population, the historical glucose measurements provided by glucose monitoring devices worn by users of the user population; and generating the one or more machine learning models by providing the historical glucose measurements to the one or more machine learning models, comparing
training classifications of diabetes received from the one or more machine learning models to diabetes classifications indicated by the historical outcome data, and adjusting weights of the one or more machine earning models.
10
wherein the historical outcome data is associated with one or more diagnostic measures independent of the historical glucose measurements provided by the glucose monitoring devices worn by users of the user population
10
wherein the historical outcome data is associated with one or more
diagnostic measures independent of the historical glucose measurements provided by the glucose monitoring devices worn by users of the user population
11
wherein the historical outcome data includes labels of traces of the historical glucose measurements that indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures
11
wherein the historical outcome data includes labels of traces of the historical glucose measurements that indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures.
12
wherein the one or more diagnostic measures include at least one of Hemoglobin A1c (HbA1c), an Oral Glucose Tolerance Test (OGTT), or Fasting Plasma Glucose (FPG)
12
wherein the one or more diagnostic measures include at least one of Hemoglobin A1c (HbAlc), an Oral Glucose Tolerance Test (OGTT), or Fasting Plasma Glucose (FPG).
13
wherein the one or more extracted glucose features include a time over threshold measure corresponding to an amount of time during an observation period that the glucose measurements of the user are above a glucose threshold
13
wherein the one or more extracted glucose features include at least one of: a time over threshold measure corresponding to an amount of time during the observation period that the glucose measurements of the user are above a glucose threshold;
14
wherein the one or more extracted glucose features include a time within range measure corresponding to an amount of time during an observation period that the glucose measurements of the user are between a first glucose level and a second glucose level that is less than the first glucose level
13
wherein the one or more extracted glucose features include at least one of: … a time within range measure corresponding to an amount of time during the observation period that the user's glucose measurements of the user are between a first glucose level and a second glucose level that is less than the first glucose level;
15
wherein the one or more extracted glucose features include a rate-of-change measure corresponding to a difference in glucose measurements over a unit of time
13
wherein the one or more extracted glucose features include at least one of: … a rate-of-change measure corresponding to a difference in glucose measurements over a unit of time;
16
wherein the machine learning model predicts the diabetes classification of the user using at least two extracted glucose features
14
wherein the machine learning model predicts the diabetes classification
of the user using at least two extracted glucose features.
17
wherein the outputting comprises outputting a glucose observation report that includes the diabetes classification and at least one of: one or more treatment recommendations for the user based on the diabetes classification; a visual representation of the glucose measurements; or one or more glucose statistics of the user generated based on the glucose measurements
16
wherein the outputting comprises outputting a glucose observation
report that includes the diabetes classification and at least one of: one or more treatment recommendations for the user based on the diabetes classification; a visual representation of the glucose measurements collected by the glucose monitoring device during the observation period; or one or more glucose statistics of the user generated based on the glucose measurements collected by the glucose monitoring device during the observation period
18
obtaining historical glucose measurements of users in a user population; obtaining historical outcome data of the users in the user population; generating instances of training data that include a training input portion and an expected output portion, the training input portion including one or more features of a respective user's glucose measurements, and the expected output portion including one or more values of outcome data corresponding to the respective user; and training a machine learning model to predict a diabetes classification using the instances of training data
34
obtaining glucose measurements collected by wearable devices worn by users of a user population over observation periods spanning multiple days; obtaining outcome data of the user population describing one or more aspects of users of the user population that relate to diabetes; generating instances of training data that include a training input portion and an expected output portion, the training input portion including at least a feature of a user's glucose measurements, and the expected output portion including one or more values of outcome data corresponding to the user; and training a machine learning model to predict a diabetes classification by: providing a training input portion of an instance of training data as input to the machine learning model…
19
wherein the historical outcome data indicates whether or not a respective user of the user population is clinically diagnosed with diabetes
11
wherein the historical outcome data includes labels of traces of the historical glucose measurements that indicate whether a respective user of the user population is clinically diagnosed with diabetes based on one or more diagnostic measures.
20
a wearable glucose monitoring device comprising a sensor to collect glucose measurements of a user; a storage device to maintain the glucose measurements of the user collected during an observation period; and a prediction system to obtain the glucose measurements of the user collected during the observation period, and predict a diabetes classification of the user by processing the glucose measurements of the user to extract one or more glucose features and providing the one or more extracted glucose features to one or more machine learning models as input
26
a wearable glucose monitoring device comprising a sensor to collect glucose measurements of a user during an observation period spanning a plurality of days, the sensor inserted subcutaneously into skin of the user during the observation period spanning the plurality of days; a storage device to maintain the glucose measurements of
the user collected during the observation period; and a prediction system to obtain the glucose measurements of the user collected during the observation period, and predict a diabetes classification of the user by processing the glucose measurements of the user to extract one or more glucose features and providing the one or more extracted glucose features to one or more machine learning models as input …
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685