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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/21/2025 has been entered.
Status of Claims
This action is in response to the RCE filed 11/21/2025.
Claim 1 was amended 11/03/2025.
Claims 1-3, 6-10 and 19-24 are currently pending and have been examined.
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 1-3, 6-10, and 19-24 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.
Claims 1-3, 6-10, and 19-24 is drawn to a method which is a statutory category of invention (Step 1: YES).
Independent claim 1 recites: determining a baseline in a blood glucose curve, comprising: measuring blood glucose levels of an individual continuously for a period of at least 30 minutes to obtain a blood glucose curve of the individual, obtaining log entries that identify at least one impact factor affecting the blood glucose levels of the individual during the period, determining time intervals associated with the at least one impact factor based on the blood glucose curve and the log entries, computing a plurality of features comprising descriptive statistics derived from transformations of the glucose values from the blood glucose curve within the time intervals; determining a baseline by applying to a plurality of features with training data comprising training blood glucose curves of a plurality of training individuals wherein: each training blood glucose curve is obtained by measuring blood glucose levels in the respective training individual for a period during which the training individual is exposed to at least one training impact factor; each training blood glucose curve comprises a training baseline that accounts for the at least one training impact fact causative for a deviation of a progression of a blood glucose curve from the blood glucose curve progression which would have been obtained in absence of the at least one training impact factor; and each training baseline is identified in the training data and analyzing the blood glucose curve of the individual, with the determined baseline as a reference, to identify a blood glucose response of the individual to the impact factor, wherein the at least one impact factor and the at least one training impact factor are exogenous or endogenous factors, capable to influence blood glucose levels.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between an individual and medical experts, as reflected in the specification, which states that “Thus, in a preferred embodiment, the impact factor-accounting baseline is a baseline that has been obtained by assigning the effect of the at least one impact factor to the progression of the blood glucose curve of the individual and by setting a corresponding baseline not being affected by the at least one impact factor… In another preferred embodiment, the impact factor-accounting baseline is a baseline determined by at least two different qualified experts, in particular selected from a nutritionist, a nutrition scientist and/or a medical doctor” (see: specification pages 4-5). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “The "impact factor-accounting baseline" according to the present invention is a baseline, which is not solely mathematically determined, in particular which is not solely determined based on a blood glucose level measured at a specific time or solely based on averaging of measured blood glucose level. Particularly preferred, the "impact factor-accounting baseline" according to the present invention is a baseline, which is not mathematically determined, in particular which is not determined based on a blood glucose level measured at a specific time or based on averaging of measured blood glucose level. Preferably, the "impact factor-accounting baseline" is a baseline, which has been determined based on expert knowledge, in particular has been determined by a qualified expert, preferably a nutritionist, a nutrition scientist and/or a medical doctor. In a further preferred embodiment, the "impact factor-accounting baseline" is a baseline determined by at least two different qualified experts, in particular selected from a nutritionist, a nutrition scientist and/or a medical doctor. In a further preferred embodiment of the present invention, the "impact factor-accounting baselines" of the blood glucose curves have been determined by a subject or object able to render an independent or new opinion on the accurate progression of the baseline in a blood glucose curve.” (see: specification page 33). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “a machine learning model, wherein the machine learning model is trained”, “computer”, “blood glucose sensor”, “computer program product”, “computer-readable storage medium”, “device” and “display unit”, “input unit”, “memory unit”, “processing unit” are recited at a high level of generality (e.g., that the determining and calculating is performed using generic computer components using generic machine learning models with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2 and
Page 26, where “The invention further pertains to a computer program product, directly loadable into the internal memory of a digital computer, comprising software code portions which, when the program is executed by a computer cause the computer to carry out at least one of the methods according to the present invention”
Page 35, where “In the context of the present invention the term "computer-readable storage medium" includes any machine readable medium, in particular computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed and read by a computer. By way of example, and not limitation, such computer-readable media can comprise random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), or other optical disk storage, semiconductor memory, magnetic disk storage or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL ), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc, wherein disks usually reproduce data magnetically, while discs reproduce data optically with lasers.”
Page 27, where “In a further preferred embodiment of the present invention the device, preferably mobile device, in particular battery-powered wireless mobile device, is able to establish a connection, in particular wireless connection, to a server on which a database, in particular a database comprising blood glucose responses of an individual to different impact factors”
Page 27, where “The invention further relates to a device comprising: a display unit, displaying a user interface, an input unit, a memory unit, and a processing unit, wherein the memory unit comprises a computer program product according to the present invention, in particular a computer program product comprising software code portions which, when the program is executed by the processing unit cause the derive to carry out at least one of the methods according to the present invention”
Page 19, where “In a further preferred embodiment of the present invention, the nutritype classification model is obtained by a machine learning procedure, preferably by a supervised machine learning procedure, preferably by an unsupervised machine learning procedure. Preferably, the machine learning procedure is based on an algorithm selected from the group consisting of linear regression, logistic regression, support vector machine, decision tree, random forest, K-nearest neighbors (kNN), K-means clustering, naive Bayes, principal component analysis (PCA), supersparse linear integer model (SLIJ\1), neural network, gradient boosted tree regression.”
Page 11, where “In a preferred embodiment of the present invention, the blood glucose level of the individual is measured, preferably constantly measured, in particular by using a blood glucose sensor. As blood glucose sensor any suitable device can be used. Preferably, the blood glucose sensor is a continuous glucose monitoring (CGM) sensor, such as a Dexcom G6, Freestyle Libre or a similar device.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-3, 6-10, and 19-24 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. The dependent claims recite calculating and analyzing glucose data based on training a generic machine learning model as shown in the specification above. Claim 3 further recites a supervised machine learning procedure which do not provide significantly more than the abstract idea as the machine learning model is generically recited on page 19. Claim 22 further recites a machine learning model that is a random forest or gradient boosted tree regression which does not provide significantly more to the abstract idea as the machine learning is recited generically in page 19. These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
The arguments filed 11/03/2025 have been fully considered.
The arguments pertaining to the 112 rejections are persuasive. The claims have been sufficiently amended and the rejections have been withdrawn.
The arguments pertaining to the 101 rejection are not persuasive. Applicant argues that the invention is related to a specific single-processing problem in continuous glucose monitoring by accounting for overlapping exogenous and endogenous impact factors and this provides a specific technological improvement to a technical problem. Examiner respectfully disagrees. Using generic machine learning models and generic computing systems to train specific data to output specific data does not provide a technological improvement to a technical problem. Outputting PPGR quantification is not a technical problem, but is related to the abstract idea of organizing human activity. Quantification of health data of a patients to be used for patients being treated by healthcare providers as evidenced in the specification (pages 4-5, 33) is part of the abstract idea. The “medical field” is not necessarily a “technical field”, nor is a treatment effected. Classen is an example of adding a meaningful limitation to the claims that create a practical application, however Classen integrated the results of the analysis into a specific and tangible method that resulted in the method “moving from abstract scientific principle to specific application” (Classen Immunotherapies Inc. v. Biogen IDEC).
The technology recited in the claims is generically recited without significantly more. The sensors are merely input/output devices and do not provide a practical application to overcome the abstract idea. The thresholding of time interval data on a generic machine learning model does not provide a technological improvement for a technical problem, it is merely changing data output related to the abstract idea of treating a patient. The training of a specific algorithm does not provide significantly more than the abstract idea, as the algorithm is running on a generic computing device and the algorithm is a generic machine learning model as shown in the specification in the rejection above. The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
The current claimed limitations fail to provide this practical application and the 101 rejection is maintained as the dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nordisk (WO 2018099912 A1) teaches calculating glycemic trends using statistical processes using time intervals.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686