DETAILED ACTION
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 .
This communication is in response to the amendment filed 3/5/26. Claims 1-2, and 4-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/5/26 has been entered.
Information Disclosure Statement
The IDSs filed on 12/15/25 has been considered by the Examiner.
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 1-2 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e, a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these “four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of Section 101 even if the subject matter is otherwise new and useful.” In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 1-13 are drawn to a method, claims 14-19 are drawn to a system; claim 20 is drawn to a product/article of manufacture (non-transitory computer readable medium)
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. While the terms “set forth” and “describe” are thus both equated with “recite”, their different language is intended to indicate that there are different ways in which an exception can be recited in a claim. For instance, the claims in Diehr set forth a mathematical equation in the repetitively calculating step, while the claims in Mayo set forth laws of nature in the wherein clause, meaning that the claims in those cases contained discrete claim language that was identifiable as a judicial exception. The claims in Alice Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
In the instant case, claims 1-20 recite(s) a method, system, and product for certain methods of organizing human activities and mental processes, which is subject matter that falls within the enumerated groupings of abstract ideas described in MPEP 2106.04 (2019 Revised Patent Subject Matter Eligibility Guidance) Certain methods of organizing human activities includes fundamental economic practices, like insurance; commercial interactions (i.e. legal obligations, marketing or sales activities or behaviors, and business relations). Organizing human activity also encompasses managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions.) The recited method and system are drawn to analyzing data and determining an optimal window of time during which a sensor device should be worn to gather user data. (i.e. managing personal behavior)
In particular, the claims 1, 14 and 20 recite a method, system, and product for:
selecting, from a set of population data, a time window of data for a particular user that comprises…;
training…the population model with the subset of data to adapt the population model and derive a personalized model for estimating glucose values, using additional data for the particular user, that are personalized for the particular user.
analyzing performance of the personalized model to determine whether the personalized model satisfies performance criteria that are indicative of performance of that personalized model;
repeating the steps of selecting…and analyzing over a number of iterations to determine a set of personalized models that satisfy each of the performance criteria;
selecting, from the set of the personalized models that have been determined to satisfy each of the performance criteria, one of the personalized models that is determined to have an optimal sensor wear period as the personalized model to be deployed for the particular user; and
deploying the personalized model having the optimal sensor wear period as the personalized model for the particular user.
MENTAL PROCESS-ANALYSIS
Moreover, the language of claims 1, 14 and 20 encompasses performance of the limitations(s) in the mind, but for the recitation of generic computer components.
In the instant case, the limitations of the steps of selecting, training, analyzing, repeating, selecting and deploying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more hardware-based processors configured by machine-readable instructions,” (claim 14) and “[a]t least one non-transient computer-readable medium having instructions stored thereon that are configurable to cause at least one processor to perform” (claim 20) nothing in the claim element precludes the step from practically being performed in the mind.
For example, but for the “processor” language, the steps of selecting, applying, analyzing, repeating, selecting and deploying in the context of this claim encompasses the user manually choosing a time window, calculating the applied subset of datapoints from time window, analyzing the results by comparing them to desired performance criteria, repeating the processes, and selecting the appropriate (personalized) model generated from the steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
As explained in MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). (emphasis added) As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Moreover, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
This judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)). Thus, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Id. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute Sections 102, 103, and 112 inquiries for the better established inquiry under Section 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the Section 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp.,838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
As described in MPEP 2106.05, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. _, 134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)). Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 1, 14 and 20 recites additional limitation(s), including: “using one or more processors;“ “one or more hardware-based processors configured by machine-readable instructions,” (claim 14) and “[a]t least one non-transient computer-readable medium having instructions stored thereon that are configurable to cause at least one processor to perform” (claim 20). However, the additional components is/are generic components that perform functions well-understood, routine and conventional activities that amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “Instructions may be configurable to be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term "processor" as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements." (par. 53)
The disclosure also states: “The processing system can be implemented at any of the devices described herein including those illustrated and described in United States Patent Application Serial Nos.: 16/533,470; 16/987,330; and 17/178,087, and more generically at a computer-based or processor-based device such as a backend server system, a client device, a medical device or other end device, such as the auxiliary sensing arrangement(s), an activity tracker device, ring, necklace, vest, etc. For instance, the processing system can be implemented at a backend server system, where the estimation model can be executed or run as part of a web application at the backend server system, and the backend server system can then communicate the estimated glucose values to a client device (e.g., patient's smartphone), an insulin infusion device or other end device. In another embodiment, the processing system can be implemented at a client device and the estimation model can be executed as part of a mobile application at the client device. In another embodiment, the processing system can be implemented at an insulin infusion device and the estimation model can be executed as part of an application at the insulin infusion device.” (par. 64)
The application explains: “any number of machine learning models can be combined to optimize the ensemble model150. Examples of machine learning algorithms or models that can be implemented at the machine learning model160 can include, but are not limited to: regression models such as linear regression, logistic regression, and K-means clustering; one or more decision tree models (e.g., a random forest model); one or more support vector machines; one or more artificial neural networks; one or more deep learning networks (e.g., at least one recurrent neural network, sequence to sequence mapping using deep learning, sequence encoding using deep learning, etc.); fuzzy logic based models; genetic programming models; Bayesian networks or other Bayesian techniques, probabilistic machine learning models; Gaussian processing models; Hidden Markov models; time series methods such as Autoregressive Moving Average (ARMA) models, Autoregressive Integrated Moving Average (ARIMA) models, Autoregressive conditional heteroskedasticity (ARCH) models; generalized autoregressive conditional heteroskedasticity (GARCH) models; moving-average (MA) models or other models; and heuristically derived combinations of any of the above, etc. The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.” (see par. 75) Such language underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of the additional (generic) components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in claims 1, 14, and 20:
- Receiving 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
- Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
- Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
- Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and
Claims 2, and 4-13 are dependent from Claim 1 and include(s) all the limitations of claim(s) 1. However, the additional limitations of the claims 2, and 4-13 fail to recite significantly more than the abstract idea. More specifically, claims 2, and 4-13 recite additional limitations which further define the abstract idea. Therefore, claim(s) 2, and 4-13 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 15-19 are dependent from Claim 14 and include(s) all the limitations of claim(s) 14. However, the additional limitations of the claims 15-19 fail to recite significantly more than the abstract idea. Claims 15-19 recite additional limitations which further define the abstract idea. Therefore, claim(s) 15-19 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Budiman (US 20250152050 A1) in view of Kida (US 20170178024 A1), and in further view of Albers et al (Albers DJ, Levine M, Gluckman B, Ginsberg H, Hripcsak G, Mamykina L.; “Personalized glucose forecasting for type 2 diabetes using data assimilation.” PLoS Comput Biol. 2017 Apr 27;13(4):e1005232. doi: 10.1371/journal.pcbi.1005232. Erratum in: PLoS Comput Biol. 2021 Aug 20;17(8):e1009325. doi: 10.1371/journal.pcbi.1009325. PMID: 28448498; PMCID: PMC5409456.)
Claims 1, 14, and 20
Budiman discloses a system comprising: one or more hardware-based processors (par. 20, par. 37-38) configured by machine-readable instructions (par. 20-21) to perform a method, comprising:
selecting, using one or more processors, from a set of population data, a time window of data for a particular user that comprises: a subset of data for the particular user to be used for training a population model, wherein the time window of data is recorded over a period that corresponds to a sensor wear period; (par. 22- the sensor data can include a window of sampled data long enough to cover a significant portion of a day, e.g., a 6 to 24 hour window with data points collected every 10 to 20 minutes. However, significant changes in the measurement of an analyte level can occur in less than 10 minutes which such systems may not be able to detect. In addition to filtering noise and artifacts, some of the data points may not be available due to data quality issues; par. 32-34.)
deploying the personalized model having the optimal sensor wear period as the personalized model for the particular user. (par. 16- to monitor an analyte level using a minimized amount of memory and both a maximized time window of available past sensor data and a maximized density of recent sensor data; par. 34-35: optimizing sensor sampling window with memory storage constraints: This allows the amount of memory to be minimized while still providing increased sensor data density when significant events may be occurring; par. 41)
Budiman discloses the system/method substantially as claimed, and further discloses determining an optimal sensor wear period but does not expressly disclose:
the population model generated from data from a plurality of users,
training, using the one or more processors, the population model with the subset of data to adapt the population model and derive a personalized model for estimating glucose values, using additional data for the particular user, that are personalized for the particular user;
analyzing using the one or more processors performance of the personalized model to determine whether the personalized model satisfies performance criteria that are indicative of performance of that personalized model;
repeating the steps of selecting, applying and analyzing over a number of iterations to determine a set of personalized models that satisfy each of the performance criteria;
selecting, using the one or more processors ,from the set of the personalized models that have been determined to satisfy each of the performance criteria, as the personalized model to be deployed for the particular user.
Kida discloses a system and method including developing a personalized model for an individual including:
the population model generated from data from a plurality of users, (par. 19: The personalization training system 100 includes the wearable device 104 with a generic model with good enough accuracy (e.g., a model that covers a majority of users or a majority of use case) and sufficient inferences (e.g., in a heart monitor, determining when a heart rate is too high or too low) for market requirements ; par. 32-machine learning algorithm to a model designed to model the general population)
training, using the one or more processors, the population model with the subset of data to generate adapt the population model and derive a personalized model, using additional data for the particular user, that are personalized for the particular user. (par. 19-20; par. 31-32; par. 38-40; Fig. 5(506, 508))
analyzing, using one or more processors, performance of the personalized model to determine whether the personalized model satisfies performance criteria that are indicative of performance of that personalized model; (Tables 1-2; par. 33-35- assessing the performance of a personal model for a feature/ criterion; comparing personalized model performance; par. 49-52: the technique 700 may determine whether the new model is more accurate than the old model by using the received data as test data. For example, the new model more closely follows the receive data than the old model when an average error or cumulative error is less for the new model than the old model)
repeating the steps of selecting, training and analyzing over a number of iterations to determine a set of personalized models that satisfy each of the performance criteria; (Fig. 7; par. 48- 49 a technique 700 for iteratively building increasingly accurate personalized models with received data in accordance with some embodiments. The technique 700 includes an operation 702 to receive data from a device. The technique 700 includes an operation 704 to compare the received data to stored data at a predefined feature)
selecting, using one or more processors, from the set of the personalized models that have been determined to satisfy each of the performance criteria, as the personalized model to be deployed for the particular user, (Fig. 7-8; par. 34- selecting a best fit personalized model …based on a desired similarity or tolerance margin and based on the amount of data desired for training; par. 35; par. 51-52)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
Budiman and Kida in combination teaches the method/system of claims 1, 14, and 20 substantially as claimed but do not expressly disclose personalized model for estimating glucose values for an individual.
Albers discloses a system/method for developing and selecting a personalized model for estimating glucose values for an individual. (abstract: Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes; pg. 4, par. 2; pg. 20, par. 1-2) At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system and method of Budiman and Kida in combination with the teaching of Albers, with the motivation of finding an optimal model for anticipating the health state and metrics of diabetic patients.
Claim 2, 15 Budiman teaches the method/system wherein the time window that is selected during each iteration of the method is adjusted to encompass a different subset of data for the particular user to be used for training the population model, and wherein repeating comprises: during each iteration of the selecting step: selecting a different time window of data for the particular user; and during each iteration of the applying step: applying a subset of data from the different time window of data that was selected during that iteration to the population model to generate a different personalized model that is personalized for the particular user. (par. 22- the sensor data can include a window of sampled data long enough to cover a significant portion of a day, e.g., a 6 to 24 hour window with data points collected every 10 to 20 minutes. However, significant changes in the measurement of an analyte level can occur in less than 10 minutes which such systems may not be able to detect. In addition to filtering noise and artifacts, some of the data points may not be available due to data quality issues; (par. 16- to monitor an analyte level using a minimized amount of memory and both a maximized time window of available past sensor data and a maximized density of recent sensor data; par. 34-35: optimizing sensor sampling window with memory storage constraints: This allows the amount of memory to be minimized while still providing increased sensor data density when significant events may be occurring; par. 41)
Claims 4, 16 Budiman does not disclose, but Kida teaches wherein analyzing comprises: for each of the performance criteria that are indicative of performance of that personalized model: determining whether the personalized model satisfies performance requirements specified by that performance criteria; and adding the personalized model to the set of the personalized models that have been determined to satisfy each of the performance criteria when the personalized model satisfies performance requirements specified by each of the performance criteria. (par. 49-50; fig. 7-8)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
Claims 5, 17 Budiman does not disclose, but Kida teaches wherein the performance criteria used to evaluate each personalized model comprise: performance metrics that are required to be satisfied by the personalized model, performance thresholds that are required to be satisfied by the personalized model, or performance that are required to be satisfied by the personalized model. (par. 49-50; Fig 8)
Claim 6. Budiman teaches the method of claim 1, wherein the set of population data comprises data for each user comprises: historical data that is collected over time comprising one or more of: data from a glucose monitoring device associated with the particular user; data regarding consumption of macronutrients by the particular user; and contextual information comprising contextual activity data associated with the particular user. (par. 24; par. 31-32)
Claims 7, 18 Budiman teaches wherein each sensor wear period is a duration that a user is required to wear a sensor for in order to acquire a sufficient amount of data needed to calibrate a personalized model of that user such that it satisfies any required performance criteria, and wherein the personalized model that has the optimal sensor wear period is the personalized model that requires a minimum wear duration to acquire data needed to satisfy any required performance criteria. (par. 16)
Claims 8, 19 Budiman does not teach, but Kida discloses a method further comprising: determining metrics that measure model longevity of each personalized model over time; comparing the model longevity of each personalized model to determine which personalized model has an optimal model longevity of maximum duration; from the set of the personalized models that have been determined to satisfy each of the performance criteria: selecting one of the personalized models that is determined to have an optimized balance between the sensor wear period and model longevity as the personalized model to be deployed to the particular user; and wherein deploying comprises: deploying the personalized model having the optimized balance between the sensor wear period and model longevity as the personalized model for the particular user. (par. 26-28)
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
Claim 9 Budiman does not teaches, but Kida discloses the method of claim 8, wherein the model longevity is a duration of time after calibration that a personalized model continues to perform within specified performance criteria when a glucose sensor is no longer available. (par. 20-21; 24-27) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
Claim 10 Budiman does not teach, but Kida discloses the method of claim 1, further comprising: deriving the population model from the set of population data prior to selecting the time window of data. (par. 24) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Budiman with the teaching of Kida to include steps of evaluating and iteratively refining a personalized model based on performance criteria, to generate a personalized model optimizing sensor wear time. One would have been motivated to include these features to avoid common issues of “over-fitting” (e.g., generally, machine learning systems attempt to avoid “over-fitting”) and generate a beneficial “customization” of the model for an individual subject in a population. (Kida: par. 12)
Claim 11. Budiman does not teach, but Kida discloses method of claim 10, further comprising: prior to each iteration of repeating the steps of selecting, applying and analyzing: updating the population model to derive a new updated population model. (Fig. 8, par. 50-52)
Claim 12. Budiman does not teach, but Kida discloses the method, wherein updating the population model comprises: during each iteration of updating the population model performing at least one of: selecting a new subset of the population data to derive the new updated population model; and selecting one or more machine learning models that are used to derive the new updated population model by applying the new subset of the population data to the one or more selected machine learning models. (par. 50-52; fig. 8)
Claim 13. Budiman does not teach, but Kida discloses The method of claim 1, wherein repeating comprises: for each user of a plurality of users: repeating the steps of selecting, applying, and analyzing over a number of iterations to determine a set of personalized models, for each user of the plurality of users, that satisfy each of the performance criteria; and wherein selecting one of the personalized models comprises: performing a statistical analysis to determine which one of the set of personalized models that satisfy each of the performance criteria for each of the plurality of users has the optimal sensor wear period; and selecting the one of the set of personalized models that satisfies each of the performance criteria for each of the plurality of users as the personalized model that has the optimal sensor wear period. (par. 48-52)
Response to Arguments
Applicant's arguments filed 3/5/26 have been fully considered but they are not persuasive.
(A) Applicant argues the claim rejections under 35 USC 101. In particular, applicant argues that the claims do not recite a mental process, and recite significantly more than an abstract idea.
In response, the examiner disagrees. The claims remain drawn to an abstract idea, as explained in the current rejection under 35 USC 101. Moreover, the claims fail to recite substantially more than the abstract idea/judicial exception.
The claimed invention, as recited in claims 1, 14, and 20 are drawn to certain method of organizing human activity, and mental processes. Applicant is reminded that a mental process analysis may include steps that can be performed with paper or pencil, or generic computer components.
As explained above, the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
(B) Applicant argues that claims integrate any recited abstract idea into a practical application.
In response, the examiner disagrees. The judicial exception (i.e. abstract idea) is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e).
While applicant argues that the claimed invention is drawn to an improvement in the technology of glucose monitoring, it is noted that the claims, particularly claims 1, 14, and 20, fail to recite and are not drawn to glucose monitors, glucose meters, or glucose monitoring. Instead, the claims are drawn determining the optimal amount of time a sensor should be worn. The applicant’s disclosure fails to describe the technical feature(s) or improvement offered by the invention.
Instead, the disclosure explains that “It would be desirable to provide users with alternative solutions that can achieve the same or similar benefits of full CGM-type therapy solutions without requiring users to wear a CGM device on a regular basis and without requiring users to absorb the costs associated with wearing a CGM device on a regular basis.” (PG-Pub par. 9)
The specification explains that the sensor data can come from a variety of off-the shelf health devices: “the inputs include discrete glucose measurements 110 from a blood glucose meter (BGM), contextual activity data 120 from an activity tracker or equivalent source of activity data such as a smartphone or other health related device, and optionally contextual data 130 from other sources.” (par. 65)
Applicant’s disclosure also fails to provide specificity for the “population model” or personal models generated/applied in the inventive system and method. While the disclosure states that ensemble modeling is used, it describes that a variety of algorithms may be applied : “Depending on the implementation, any number of machine learning models can be combined to optimize the ensemble model 150. Examples of machine learning algorithms or models that can be implemented at the machine learning model 160 can include, but are not limited to: regression models such as linear regression, logistic regression, and K-means clustering; one or more decision tree models (e.g., a random forest model); one or more support vector machines; one or more artificial neural networks; one or more deep learning networks (e.g., at least one recurrent neural network, sequence to sequence mapping using deep learning, sequence encoding using deep learning, etc.); fuzzy logic based models; genetic programming models; Bayesian networks or other Bayesian techniques, probabilistic machine learning models; Gaussian processing models; Hidden Markov models; time series methods such as Autoregressive Moving Average (ARMA) models, Autoregressive Integrated Moving Average (ARIMA) models, Autoregressive conditional heteroskedasticity (ARCH) models; generalized autoregressive conditional heteroskedasticity (GARCH) models; moving-average (MA) models or other models; and heuristically derived combinations of any of the above, etc. The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.” (par. 75-79.)
As explained “the ensemble model 150 can include one or more deep learning algorithms. It should be noted that any number of different machine learning techniques may also be utilized. Depending on the implementation, the ensemble model 150 can be implemented as a bootstrap aggregating ensemble algorithm (also referred to as a bagging classifier method), as a boosting ensemble algorithm or classifier algorithm, as a stacking ensemble algorithm or classifier algorithm, as bucket of models, ensemble algorithms, as Bayes optimal classifier algorithms, as Bayesian parameter averaging algorithms, as Bayesian model combination algorithms.” (par. 80-87)
Therefore applicant’s disclosure fails to describe an improvement in a technological field. Applicant’s arguments regarding the “unique combination of the operations recited” not being well-understood, routine and conventional are not persuasive. The disclosure describes the use of known machine learning algorithms and techniques. It is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology.
Moreover, this is analysis applies to the additional elements, not to the abstract idea itself. The identified additional elements include the computer hardware components and computer readable medium used to store instructions for performing the claimed method. Examiner has provided citations from the applicant’s specification to explain the generic nature of the computer hardware and computer readable medium used to perform the claimed invention.
(C) Applicant argues that the prior art does not disclose or make obvious features of the independent claim as recited. More specifically, applicant argues that the Budiman reference does not disclose “selecting form a set of population data;” and “training…the population model to generate a personalized model” and
In response, the Examiner has updated the grounds of rejection to address the claim limitations. An additional reference, Albers, in combination with Budiman and Kida has been provided to address the limitation, and the limitations of claims 1, 14, and 20. Moreover, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
(D) Applicant argues that the prior art does not disclose “deploying the personalized model having the optimal sensor wear period as the personalized model for the particular user.”
In response, the Examiner disagrees, and notes that the claim language recited is broader than the interpretation argued by the applicant. Moreover, while applicant argues that the optimal sensor wear period in the Budiman reference is distinct from that claimed by the applicant in the instant invention, the applicant’s disclosure explains that an optimal wear window is “the duration of the time window corresponds to a sensor wear period that a user wears a sensor to acquire data that is used to calibrate the personalized model of that user.” (par. 238 of US Pg-Pub US 20230317292 A1) This is analogous to the optimal window described in the Budiman reference.
Additionally, it is not clear from the claim language what “deployment” of the personalized model entails. It is not clear if the step is generating a prediction of the optimal wear window, or if the deployment means that data captured during the optimal window is used to perform an additional prediction, calculation or projection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RACHEL L. PORTER
Primary Examiner
Art Unit 3684
/Rachel L. Porter/Primary Examiner, Art Unit 3684
Prosecution Insights