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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/09/2026 has been entered.
Formal Matters
Applicant's response, filed 07/09/2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-20 are currently pending and have been examined.
Claims 1, 7, 8, 10, 16, 17, 19, and 20 have been amended.
Claims 1-20 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 09/17/2020 claiming benefit to Parent Application 17/024,557.
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 19-20 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.
Step 1 – Statutory Categories of Invention:
Claims 19-20 are drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 19 recites a method for generating a predictive association model in part performing the steps of obtaining historical data associated with a patient, wherein the historical data comprises real-valued data indicating a geographic location of the patient at a series of time points and occurrences of meal consumption by the patient; determining, based on the real-values data, for the series of time points, a duration of time spent by the patient at the geographic location; transforming, for the seis of time points, the geographic location and the duration of time spent by the patient at the geographic location to categorical values; identifying a plurality of associations between the categorical values, each association including a combination of the patient being at a particular location and a duration of time spent by the patient at the geographic location being within a particular cluster represented by the categorical value; determining for associations of the plurality of associations, co-occurrence information, the co-occurrence information indicating a co-occurrence of an association of the plurality of associations with consumption of a meal; and generating a predictive association model by selecting a subset of the plurality of associations associated with probable meal consumption based on the co-occurrence information, wherein the predictive association model is used to predict an occurrence of the patient consuming a meal and automatically deliver insulin to the patient to compensate for a glycemic response to consumption of the predicted meal.
These steps of collecting and processing patient data to determine a medication adjustment amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)).
Dependent claim 20 recites, in part, wherein selecting the subset of the plurality of associations comprises utilizing a multi-objective genetic algorithm and the co-occurrence information to iteratively generate sets of associations and refine the sets of associations.
Each of these steps of the preceding dependent claim only serve to further limit or specify the features of independent claim 19 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 19 recites one or more processors of a computing device. The specification defines the computer and corresponding interface and input device/controls as a general purpose processor and hardware components (see the instant Detailed Description in ¶ 0047 and ¶ 0043). The use of one or more processors, in this case to generate a predictive model, only recites the computer and corresponding hardware as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 19 recites one or more processors of a computing device. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 19-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. § 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-18 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. Independent claims 1 and 10 have been amended to claim, “presenting a user interface that includes the two or more categorical state values for verification, by the patient, that the two or more categorical state values are predictive of the patient consuming the meal” and “determining, responsive to the verification,…, a current state of the patient”. The specification only provides generally that “For example, a graphical user interface (GUI) display may allow for the nature of a “white box” ontological prediction model to be communicated to a user in a way that allows the user to comprehend, and potentially modify, the reasoning underlying the model used to generate predictions, recommendations, and the like” (see the instant specification in ¶ 0075). Ordinally filed dependent claims 7 and 16 claimed, “causing an indication of a relationship of the two or more categorical state values and prediction of the patient consuming the meal to be presented in a user interface for verification”. While the originally filed claims include an indication of a relationship between the state values, the verification of the values themselves was not supported. The originally filed specification also fails to support a patient verification of the state values, only offering the generic display and “potential modification” for the “reasoning underlying the model” .
Furthermore, Examiner notes that the instant specification states the display is to “a user” generally and not a patient specific graphical user interface display. The instant specification often utilizes the term “user” inconsistently and interchangeable between a provider and a patient – for example in ¶ 0035 the user is a distinct individual from the patient stating, “the computer 108 may provide information to the user that facilitates the patient’s subsequent use of the infusion device” and then the same individual stating, “autonomously control the rate or dose of medication administered into the body of the user” (see MPEP § 2163.05(II) for a discussion on narrowing or subgeneric claim with regards to the written description).
Examiner notes Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitations in the application as filed (see 2163.04(I) regarding the burden on Examiner with regard to the written description requirement).
Claims 2-9 and 11-18 depend on claims 1 or 10 and do not remedy the written description requirement issues of claims 1 or 10. As dependent claims inherit the deficiencies of the claims they depend on, they are also rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Roy et al. (US Patent App No 20180174675)[hereinafter Roy] in view of Vehi et al., Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning, 26(1) Health Informatics Journal 703-718 (March 2020) [hereinafter Vehi].
As per claim 1, Roy teaches on the following limitations of the claim:
a method performed by one or more processors of a computing device for monitoring a physiological condition of a patient, the method comprising is taught in the Detailed Description ¶ 0031, ¶ 0038, ¶ 0090-91, and ¶ 0127 (teaching on a meal detection event algorithm for processing different categorical input data to generate an insulin adjustment)
obtaining, using the one or more processors of the computing device, a predictive association model associated with the patient, wherein the predictive association model comprises an association of two or more categorical state values that are predictive of the patient consuming a meal is taught in the Detailed Description in ¶ 0095-96 (teaching on a meal detection event predictive algorithm wherein meal event categorical data (treated as categorical state values) are predictive of a patient consuming a meal)
presenting a user interface that includes the two or more categorical state values for verification, by the patient, that the two or more categorical state values are predictive of the patient consuming a meal is taught in the Detailed Description in ¶ 0099-100, ¶ 0163, and in the Figures at fig. 16 (teaching on the remote device or patient user interface device reviewing the historical meal data and determined meal probabilities for their predictive nature for use by the meal detection event predictive algorithm)
obtaining, using the one or more processors of the computing device, real-time data associated with the patient is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data (treated as synonymous to real-time data) including (1) the time and (2) the location of a meal event and associating each data type)
determining, responsive to the verification, and using the one or more processors of the computing device, a current state of the patient based at least in part on the real-time data is taught in the Detailed Description in ¶ 0098-100 and ¶ 0118 (teaching on determining a category for at least one of the raw data input types (here the time of day is associated with breakfast period, lunch period, etc.) and filtering out other contextual patient meal data wherein the categories is indicative of the eating state of the patient)
determining, using the one or more processors of the computing device, whether the two or more current state categorical values match the two or more categorical state values of the association of the predictive association model; predicting consumption of a meal in response to determining the two or more current state categorical values match the two or more categorical state values of the association; and is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0150 (teaching on mapping (treated as synonymous to matching) the contextual data including the categorized data to an expected value to determine if the meal event occurred - Examiner notes in the "activity" prediction model, the term matching is explicitly utilized to compare historical data to certain event characteristics)
in response to predicting the consumption of the meal, automatically delivering insulin to compensate for a glycemic response to consumption of the predicted meal is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values)
Roy fails to teach the following limitation of claim 1. Vehi, however, does teach the following:
transforming the real-time data into two or more current state categorical values, each of the two or more current state categorical values indicative of a current state of the patient is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification wherein the classifications are indicative of a blood glucose state of the patient)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
Independent claim 10 is rejected under the same rational.
As per claim 2, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, wherein the two or more categorical state values comprise a location of the patient is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
Dependent claim 11 is rejected under the same rational.
As per claim 3, the combination of Roy and Vehi discloses all of the limitations of claim 2. Roy also discloses the following:
the method of claim 2, wherein the two or more categorical state values comprise a duration of time the patient has been in the location is taught in the Detailed Description in ¶ 0098-100 and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including concurrent timestamps and locations associated with a previous event)
Dependent claim 12 is rejected under the same rational.
As per claim 4, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, wherein obtaining the real-time data comprises obtaining location data based on global positioning system (GPS) data is taught in the Detailed Description in ¶ 0098-100, ¶ 0089, and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including location determined via a GPS receiver)
Dependent claim 13 is rejected under the same rational.
As per claim 5, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy fails to teach the following; Vehi, however, does disclose:
the method of claim 1, wherein transforming the real-time data into two or more current state categorical values comprises determining a Boolean value for each of the two or more current state categorical values by clustering the real-time data and assigning the Boolean value to the clustered data is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
Dependent claim 14 is rejected under the same rational.
As per claim 6, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising determining a bolus dosage of insulin based on a predicted nutritional content of the predicted meal, wherein delivering the insulin comprises delivering the determined bolus dosage is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values including nutritional components of the meal)
Dependent claim 15 is rejected under the same rational.
As per claim 7, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising obtaining, after delivering the insulin, an updated predicted association model, wherein the updated predicted association model was generated using updated training data that includes at least the real-time data associated with the patient is taught in the Detailed Description in ¶ 0100-102 (teaching on automatically adjusting/updating the meal detection event predictive algorithm with new (treated as synonymous to "real-time") patient training data continuously during closed loop control of the insulin pump)
Dependent claim 16 is rejected under the same rational.
As per claim 8, the combination of Roy and Vehi discloses all of the limitations of claim 7. Roy also discloses the following:
the method of claim 1, wherein the user interface comprises controls that allow modification, by the patient, of the relationship of the two or more categorical state values and prediction of consumption of the meal is taught in the Detailed Description in ¶ 0118-119 (teaching on presenting the identified meal event to the user on a user interface for verification wherein the patient may modify the event thus necessarily altering the relationship between the variables and the prediction outcome)
Dependent claim 17 is rejected under the same rational.
As per claim 9, the combination of Roy and Vehi discloses all of the limitations of claim 1. Roy also discloses the following:
the method of claim 1, further comprising combining real-time data associated with a plurality of data sources based on timing information prior to transforming the real-time data into two or more current state categorical values is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type and combining as historical event data for the user before preprocessing the training data via filtering or categorization)
Dependent claim 18 is rejected under the same rational.
As per claim 19, Roy teaches on the following limitations of the claim:
a method performed by one or more processors of a computing device for generating a predictive association model, comprising is taught in the Detailed Description ¶ 0031, ¶ 0038, ¶ 0090-91, ¶ 0115, and ¶ 0127 (teaching on training a meal detection event algorithm for processing different categorical input data to generate an insulin adjustment)
obtaining, using the one or more processors of the computing device, historical data associated with a patient, wherein the historical data comprises real-valued data indicating a geographical location of the patient at a series of time points and occurrences of meal consumption by the patient is taught in the Detailed Description in ¶ 0095-96 (teaching on receiving historical raw contextual patient meal data including (1) the time and (2) the location of a meal event and associating each data type)
determining, based on the real-valued data, for the series of time points, a duration of time spent by the patient at the geographic location is taught in the Detailed Description in ¶ 0098-100 and ¶ 0113 (teaching on the context data that is filtered (treated as synonymous to categorical state values) including concurrent timestamps and locations associated with a previous event)
transforming, using the one or more processors of the computing device, for the series of time points, the geographic location and the duration of time spent by the patient at the geographic location to categorical values for a plurality of fields of patient data is taught in the Detailed Description in ¶ 0098-100 (teaching on determining a category for at least one of the historical raw data input types - here the time of day is associated with breakfast period, lunch period, etc. and filtering out (treated as synonymous to transforming) other contextual patient meal data)
identifying, using the one or more processors of the computing device, a plurality of associations between the categorical values, each association including a combination of the patient being at a particular geographic location and a duration of time spent by the patient at the geographic location is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0150 (teaching on mapping (treated as synonymous to matching) the contextual data including the categorized data to an expected value to determine if the meal event occurred - Examiner notes in the "activity" prediction model, the term matching is explicitly utilized to compare historical data to certain event characteristics)
determining, using the one or more processors of the computing device, for associations of the plurality of associations, co-occurrence information, the co-occurrence information indicating a co-occurrence of an association of the plurality of associations with consumption of a meal; and is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0115 (teaching on training a model to map the contextual data including the categorized data to an expected value (treated as synonymous to identifying and determining a co-occurrence association) to determine if the meal event occurred)
generating a predictive association model based by selecting a subset of the plurality of associations associated with probable meal consumption based on the co-occurrence information is taught in the Detailed Description in ¶ 0100, ¶ 0113, and ¶ 0115 (teaching on training the predictive model to map new contextual data including the categorized data to an expected historical value to determine if the meal event occurred)
wherein the predictive association model is used to predict an occurrence of the patient consuming a meal and automatically deliver insulin to the patient to compensate for a glycemic response to consumption of the predicted meal is taught in the Detailed Description in ¶ 0078, ¶ 0107, and ¶ 0127 (teaching on adjusting a bolus dosage of an automated insulin pump after a meal event is determined based in part on the processed input variable values)
Roy fails to teach the following limitation of claim 1. Vehi, however, does teach the following:
being within a particular cluster represented by the categorical value is taught in the § Patient condition assessment on p. 710 (teaching on normalizing the time series input data for a medical predictive model via hierarchical clustering for binary classification wherein the classifications are indicative of a blood glucose state of the patient)
It would have been obvious to one of ordinary still in the art to include in the meal detection event algorithm of Roy with the hierarchical clustering for binary classification data normalization as taught by Vehi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably converting time series based data into normalized initial classification values for use in an insulin event prediction model.
As per claim 20, the combination of Roy and Vehi discloses all of the limitations of claim 19. Roy also discloses the following:
the method of claim 19, wherein selecting the subset of the plurality of associations comprises utilizing a multi-objective genetic algorithm and the co-occurrence information to iteratively generate sets of associations and refine the sets of associations is taught in the Detailed Description in ¶ 0091 (teaching on utilizing genetic programming for the machine learning feature selection of input variables)
Response to Arguments
Applicant's arguments filed for claims 19 and 20 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that certain steps “are nothing like a mental process that a neurologist should follow when testing a patient” or “are not at all like instructions for a human” but instead are instructions specifically be performed by a computing device to generate a predictive association model. Examiner disagrees. Examiner notes that, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the use of a computer to train a model, including a classification algorithm, utilizing the training embodiments offered in the instant specification (see at least ¶ 00103) amount to applying data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) and therefore are mere instructions to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or mental processes of labeling and fitting data to a particular model representation. Because the claim is directed towards preparing insulin dosages via a medical association model, Examiner has identified the instant claims are best categorized as a method of organizing human activity.
Next, Applicant asserts the amended features integrate the alleged abstract idea into a practical application by generating a more accurate meal consumption model. Examiner disagrees. Analyzing a patient’s location and corresponding duration in said location to generate a model for predicting a meal event is not an improvement to technology. An improvement to the abstract idea does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Applicant finally asserts, in the § Response to Advisory Action beginning on p. 8, that the newly added limitation “wherein the predictive association model is used to predict an occurrence of the patient consuming a meal and automatically deliver insulin to the patient to compensate for a glycemic response to consumption of the predicted meal" integrates any alleged abstract idea into a practical application that recites a particular method of treatment. Examiner disagrees. The intended use of the model does not amount to a positively recited treatment step. As there is no positively recited administration step of the treatment, but only “recommending a treatment”, the claim does not qualify as a prophylaxis step under Step 2A Prong 2.
In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.
Applicant’s arguments with respect to 35 USC § 102 of claims 19-20 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Vehi, as per the rejection above.
Applicant's arguments with respect to 35 USC 103 have been fully considered have been fully considered but they are not persuasive.
Regarding the 35 USC § 103 rejection of claims 1-18 Applicant asserts that Roy fails to teach on the newly amended limitations involving the verification of the categorical states values as predictive for the patient consuming the meal. Examiner disagrees. Roy in ¶ 0099-100 and Fig. 16 teach on the remote device or patient user interface device reviewing the historical meal data and determined meal probabilities for their predictive nature for use by the meal detection event predictive algorithm, stating (emphasis added) specifically “the remote device 814 may analyze the historical meal data , historical sensor glucose measurement data , historical insulin delivery data , historical auxiliary measurement data , historical geographic location data , and any other historical data associated with the patient in the database 816 to identify or otherwise determine the subset of the patient's historical data that is predictive of or correlative to meal occurrence” and maintaining the machine learning model accordingly. Therefore, the rejection has been sustained by Examiner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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