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 .
Status of Claims
Claims 1-20 are cancelled.
Claims 21-40 are pending and examined on the merits.
Priority
The instant application is a CON of U.S. Application No. 15/853,574 filed on 12/22/2017, which was granted on 3/22/2022, after the filing date of the instant application on 3/08/2022. Thus, the effective filing date of the claims is 12/22/2017.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Specification
The disclosure is objected to because of the following informalities: in the Abstract, line 17 "a transcutaneously implanted pump), and enables" should read "a transcutaneously implanted pump, and enables". Appropriate correction is required.
Claim Objections
Claim 25 objected to because of the following informalities: page 2 lines 8 and 10-11, "the sensor error correction operation model" should read "the sensor error correction operational model". Appropriate correction is required.
Claim 26 objected to because of the following informalities: page 2 line 17, "the error correction operation model" should read "the error correction operational model". Appropriate correction is required.
Claim 39 objected to because of the following informalities: page 6 lines 9-10, "cause application of the first error correction model to at least a portion of the-current data" should read "cause application of the first error correction model to at least a portion of then-current data" (similar to the next limitation on lines 13-14). Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
"processing apparatus" in claims 21, 25-26, and 39-40
"storage apparatus" in claims 21 and 39
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-40 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 recites: “based at least in part” in lines 14-15; “utilize at least the sensor data and the error correction operational model” in line 17; and “produce at least one compensation” in line 19. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of an error correction model comprises additional, undisclosed and unclaimed data and/or steps.
Claim 25 recites: “based at least in part” in page 2 line 5; and “based at least” in page 2 line 10. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of corrected blood analyte data comprises additional, undisclosed and unclaimed error correction operational models.
Claim 30 recites “based at least in part” in page 3 line 12. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of an error correction operational model comprises additional, undisclosed and unclaimed data.
Claim 32 recites “based at least” in page 3 line 28. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of a second error correction operational model comprises additional, undisclosed and unclaimed data.
Claim 33 recites "wherein the produced at least one compensation for the estimated one or more unmodeled errors associated with the sensor apparatus are further utilized by the computerized apparatus to generate the error correction operational model associated with the medicant delivery device". It is unclear how the computerized apparatus is utilizing the compensatory value(s) to generate a new error correction model, if the computerized apparatus is utilizing the corrected sensor data to produce a new error model, or if the claim is simply explicitly associating the sensor apparatus model with the medicant delivery device. To further prosecution, the claim is interpreted as the computerized apparatus is utilizing the corrected sensor data to produce a new error mode.
Claim 34 recites “based at least in part” in page 4 line 15. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of an error correction operational model comprises additional, undisclosed and unclaimed data.
Regarding claim 35, the limitation reciting "such that the transmitted at least portion of the third data can be applied by the second computerized apparatus to correct for the one or more unmodeled errors associated with the delivery of the medicant by the medicant delivery device" has no active step of applying the transmitted data (the "at least a portion of the third data") to correct for the unmodeled errors, and it is unclear if an active step is performed for model training that results in a compensatory value for error correction, or if the third data is simply being used to adjust the amount of medicant to be delivered. To further prosecution, the limitation is interpreted as the second computerized apparatus applying the resulting compensatory values to the unmodeled errors associated with the delivery of the medicant by the medicant delivery device.
Claim 36 recites “based at least in part” in page 5 line 8. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of an error correction operational model comprises additional, undisclosed and unclaimed data.
Claim 37 recites “based at least” in page 5 line 12. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the generation of the third data comprises additional, undisclosed and unclaimed steps.
Claim 39 recites: “based at least in part” in page 6 lines 3 and 6; and “at least a portion” in page 6 lines 9 and 13. One of ordinary skill in the art cannot ascertain the boundaries of the claim because the term “at least” suggests that the application of the first or second error correction model comprises additional, undisclosed and unclaimed data.
All other claims depend from claims 21, 34, and 39, therefore they are also rejected as being indefinite.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 21: “generation of an error correction operational model based at least in part on the received data” provides mathematical calculations (generating a model based on data) that is considered a mathematical concept, which is an abstract idea (as evidenced by instant specification, page 102 "Moreover, while the curves and data of FIGS. 14A-14C are shown as essentially collections of discrete data points, one or more mathematical functional relationships can be utilized for all or a portion of the expected response modeling.").
Claim 25: “generation of a sensor error correction operational model” provides mathematical calculations (similar to claim 21, generating a model based on data) that is considered a mathematical concept, which is an abstract idea.
Claim 26: “application of the error correction operation model on at least a portion of then-current blood analyte signal data generated by the implanted sensor apparatus” provides mathematical calculations (applying the compensatory values involves arithmetic or algebraic operations) that is considered a mathematical concept, which is an abstract idea.
Claim 28: “performance of one or more machine learning algorithms in support of the generation of an error correction operational model” provides mathematical calculations (similar to claim 21, generating a model based on data) that is considered a mathematical concept, which is an abstract idea.
Claim 31: “apply the at least one compensation for the estimated one or more unmodeled errors to data generated by the medicant delivery device as part of operation thereof in a second, corrected mode” provides mathematical calculations (applying the compensatory values involves arithmetic or algebraic operations) that is considered a mathematical concept, which is an abstract idea.
Claim 32: “generation of a second error correction operational model” provides mathematical calculations (similar to claim 21, generating a model based on data) that is considered a mathematical concept, which is an abstract idea.
Claim 34: “generation of an error correction operational model based at least in part on the received first data” provides mathematical calculations (similar to claim 21, generating a model based on data) that is considered a mathematical concept, which is an abstract idea.
Claim 35: “applying the resulting compensatory values to the unmodeled errors associated with the delivery of the medicant by the medicant delivery device” ( provides mathematical calculations (applying the compensatory values involves arithmetic or algebraic operations) that is considered a mathematical concept, which is an abstract idea.
Claim 36: “generate the error correction operational model” provides mathematical calculations (similar to claim 21, generating a model based on data) that is considered a mathematical concept, which is an abstract idea.
Claim 39: “generation of a first error correction model” and “generation of a second error correction model” provides mathematical calculations (similar to claim 21, generating models based on data) that is considered a mathematical concept, which is an abstract idea.
“application of the first error correction model to at least a portion of then-current data relating to medicant delivery” and “application of the second error correction model to at least a portion of then-current signal data generated by the sensor apparatus” provides mathematical calculations (applying the compensatory values involves arithmetic or algebraic operations) that is considered a mathematical concept, which is an abstract idea.
Claim 40: “based on a determination that one or more criteria for sensor re-training are met, initiate a repeat of the operation of the sensor apparatus” provides an evaluation (initiating an operation based on a determination) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 21-40 recite performing some aspects of the analysis on “Computerized apparatus comprising: at least one data interface configured to enable at least communication of data with (i) a medicant delivery device configured to deliver medicant to a living being, and (ii) a sensor apparatus implanted within the living being; processing apparatus in data communication with the at least one data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising instructions” (claim 21), “Non-transitory computer readable apparatus comprising a storage medium, the storage medium comprising a plurality of instructions which are configured to, when executed on a computerized apparatus” (claim 34), and “Computerized apparatus, comprising: a data interface in communication with a medicant delivery device and a sensor apparatus; processing apparatus in data communication with the data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising at least one computer program having a plurality of instructions” (claim 39), there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. 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 such, claims 21-40 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 21: “Computerized apparatus comprising: at least one data interface configured to enable at least communication of data with (i) a medicant delivery device configured to deliver medicant to a living being, and (ii) a sensor apparatus implanted within the living being; processing apparatus in data communication with the at least one data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising instructions” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
“receive data relating to operation of the medicant delivery device” and “receipt of sensor data generated by the implanted sensor apparatus” provides insignificant extra-solution activities (receiving data are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“estimate one or more unmodeled errors associated with delivery of the medicant, and produce at least one compensation for the estimated one or more unmodeled errors” provides insignificant extra-solution activities (running a model to produce data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 25: “corrected blood analyte data is generated based at least on the sensor error correction operation model” provides insignificant extra-solution activities (running a model to produce data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 28: “transmission of data relating to the performance of the one or more machine learning algorithms to the application computer program” provides insignificant extra-solution activities (transmitting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 30: “receive data, via at least one network data interface, relating to one or more other living beings” provides insignificant extra-solution activities (receiving data are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 34: “Non-transitory computer readable apparatus comprising a storage medium, the storage medium comprising a plurality of instructions which are configured to, when executed on a computerized apparatus” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
“receipt of first data relating to operation of a medicant delivery device in a first uncorrected mode, the medicant delivery device configured to deliver medicant to a living being; receipt of second data relating to operation of a sensor apparatus, the sensor apparatus configured for implantation within the living being” provides insignificant extra-solution activities (receiving data are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“utilization of at least the second data and the error correction operational model to generate third data” provides insignificant extra-solution activities (running a model to produce data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 35: “transmission of at least a portion of the third data to a second computerized apparatus associated with the living being” provides insignificant extra-solution activities (transmitting data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 36: “transmission of at least a portion of the first data to a second computerized apparatus via a network interface, the second computerized apparatus comprising computing apparatus configured to use at least one machine learning (ML) algorithm to generate ML data” provides insignificant extra-solution activities (transmitting and generating data are post-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 37: “receipt of data comprising the generated error correction operational model; the generation of the third data based at least on the received data comprising the generated error correction operational model and the second data; and transmission of at least a portion of the third data to a third computerized apparatus” provides insignificant extra-solution activities (receiving, generating, and transmitting data are post-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 39: “Computerized apparatus, comprising: a data interface in communication with a medicant delivery device and a sensor apparatus; processing apparatus in data communication with the data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising at least one computer program having a plurality of instructions”
The steps for receiving, generating, and transmitting data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering, data manipulation, and sample manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 21-40 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “Computerized apparatus comprising: at least one data interface configured to enable at least communication of data with (i) a medicant delivery device configured to deliver medicant to a living being, and (ii) a sensor apparatus implanted within the living being; processing apparatus in data communication with the at least one data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising instructions” (claim 21), “Non-transitory computer readable apparatus comprising a storage medium, the storage medium comprising a plurality of instructions which are configured to, when executed on a computerized apparatus” (claim 34), and “Computerized apparatus, comprising: a data interface in communication with a medicant delivery device and a sensor apparatus; processing apparatus in data communication with the data interface; and a storage apparatus in data communication with the processing apparatus, the storage apparatus comprising at least one computer program having a plurality of instructions” (claim 39) requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for receiving, generating, and transmitting data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 21-40 are 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.
Claims 21-40 rejected under 35 U.S.C. 103 as being unpatentable over Ternes et al. (US-20170196458) in view of Roy et al. (US-9089292).
Regarding claim 21, 25-26, 29, 34, and 39, Ternes teaches an interface in communication with a medicant delivery device and sensor, a processing unit, a storage device, and a program configured to store an execute instructions such as operation of the medicant delivery device (Para.0077 "The external communication device and/or programmer may also allow subject (e.g., patient) interaction. For example, the external communication device and/or programmer may include a patient interface and allow the patient to input subjective data."; Para.0071 "In embodiments, the EMD [external monitoring device] may include any number of different therapy components such as, for example, [] a drug delivery component"; and Para.0111 "According to embodiments illustrated in FIG. 3, the IMD [implantable medical device] includes a controller, a storage device, a sensing component, a communication component, and a power source. The controller may include, for example, a processing unit, a pulse generator, and/or the like. The controller may be any arrangement of electronic circuits, electronic components, processors, program components and/or the like configured to store and/or execute programming instructions, to direct the operation of the other functional components of the IMD, to perform arrhythmia detection and/or classification algorithms, to store physiologic data obtained by the sensing component, and/or the like, and may be implemented, for example, in the form of any combination of hardware, software, and/or firmware.").
Ternes also teaches receiving data from sensor and delivery devices (Abstract "Embodiments of the disclosure include systems and methods for obtaining high-resolution data from implantable medical devices (IMDs)").
Ternes also teaches a sensing apparatus for a blood analyte level (Para.0072 "the EMD 106 may be configured to measure parameters relating to the human body, such as [] blood characteristics (e.g., glucose levels)").
Ternes does not explicitly teach generation of a first error correction model relating to the medicant delivery device, generation of a second error correction model relating to the sensor apparatus, application of the first error correction model to then-current data relating to medicant delivery enabling predictive correction of one or more errors associated with delivery of the medicant, nor application of the second error correction model to then-current signal data generated by the sensor apparatus enabling predictive correction of one or more sensor-related errors.
However, Roy teaches a first or second error correction model, that may then be combined with the multiple devices of Ternes for the application of the models to the data from the medicant delivery device or the sensing device (Page 36 col 1 lines 32-39 "However, embodiments may implement alternative probability model(s) without departing from claimed subject matter. Example Linear Kalman Filter (KF): A linear Kalman Filter (KF) may have two stages: a prediction stage, in which a current stage of a system is predicted given a previous stage; and an update stage, in which a current predicted stage of the system is updated/corrected based on a weighted error generated between a model prediction and a true measurement" provides for a first prediction model, determining the error, and applying the weight of error to a second model).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Ternes as taught by Roy in order to better estimate the blood-glucose concentration in a patient (Page 1 Abstract "Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, one or more parameters of a probability model may be estimated based on blood glucose reference sample-sensor measurement pairs. Based on such information, function(s) for estimating a blood-glucose concentration in a patient may be determined."). One skilled in the art would have a reasonable expectation of success because both approaches use a system of devices for monitoring patient parameters, modeling them, and delivering a medicant.
Regarding claim 22, Ternes in view of Roy teach the methods of Claim 21 on which this claim depends/these claims depend, respectively. Roy also teaches the at least one compensation for one or more unmodeled errors associated with the delivery comprises compensation for one or more physiological error sources particular to the living being to which the medicant is to be delivered (Page 29 col 2 lines 37-40 "A difference between a desired basal blood glucose level G.sub.B and an estimate of a present blood glucose level G is the glucose level error G.sub.E that may be corrected").
Regarding claim 23, Ternes in view of Roy teach the methods of Claim 22 on which this claim depends/these claims depend, respectively. Ternes also teaches the one or more errors vary as a function of at least an ambulatory context of the living being [ambulatory in its broadest reasonable interpretation being medical care or monitoring that occurs while a patient is mobile and not confined to a hospital bed] (Para.0122 "Additionally, machine-learning techniques may be employed to adapt trigger criteria to more rapidly-changing scenarios such as, for example, the impact of adjusting to a new medication, the impact of a temporary adjustment in sleep schedule, the impact of the air quality in a particular location (e.g., outside vs. inside, downtown vs. at home, one city vs. another, etc.), the impact of an allergic reaction to an environmental stimulus, the impact of a psychological response to an increase or decrease in an amount of sunlight over the course of one or more days, the impact of a rapid change in barometric pressure, and/or the like.").
Regarding claim 24, Ternes in view of Roy teach the methods of Claim 22 on which this claim depends/these claims depend, respectively. Ternes also teaches the one or more errors vary as a function of at least a dietary or food ingestion context of the living being (Para.0122 "A set of trigger criteria also may be dynamically adapted over time, using a machine-learning process. That is, for example, as a patient ages, adopts changes to daily routines (e.g., diet, exercise, sleep habits, etc.), and/or the like, the trigger component 318 may dynamically adapt trigger criteria so that, for example, a smaller increase in heart rate may be detected as a trigger event when the patient is older than when the patient was younger.").
Regarding claim 27, Ternes in view of Roy teach the methods of Claim 21 on which this claim depends/these claims depend, respectively. Ternes also teaches the implanted sensor apparatus comprises a BLE (Bluetooth Low Energy) wireless data interface, the at least one data interface of the computerized apparatus configured to wirelessly communicate with the implanted sensor apparatus using the BLE wireless data interface (Para.0063 "the communication link may be, or include, a wireless communication link such as, for example, a short-range radio link, such as Bluetooth, []. In embodiments, for example, the communication link may utilize Bluetooth Low Energy radio (Bluetooth 4.1), or a similar protocol").
Regarding claim 28, Ternes in view of Roy teach the methods of Claim 21 on which this claim depends/these claims depend, respectively. Ternes also teaches the computerized apparatus comprises a portable user device further comprising at least one wireless network data interface, the at least one wireless network data interface configured to allow wireless data communication between the portable user device and at least one wireless access node of a wireless network so as to enable data communication between an application computer program configured to execute on the portable user device and at least one networked server apparatus, the at least one networked server apparatus configured to cause: performance of one or more machine learning algorithms in support of the generation of an error correction operational model; and transmission of data relating to the performance of the one or more machine learning algorithms to the application computer program (Para.0077 "The external communication device and/or programmer may also allow subject (e.g., patient) interaction. For example, the external communication device and/or programmer may include a patient interface and allow the patient to input subjective data." and para.0091 "For example, before a study prescription is provided to the IMD 102, the management server 114 may provide a notification of the study prescription to a clinician or other user via the user device 116, mobile device 118, and/or the like. The user (e.g., clinician), in response to receiving the notification, may request a description of the study prescription. In embodiments, the notification of the study prescription may include a description thereof, and may include an indication of a longevity impact associated with the study prescription. As is explained in further detail below, a value may be determined that reflects an impact on the longevity of one or more components of the IMD 102 that is likely to result from execution of a particular study prescription. By presenting this longevity impact value to a user, along with a description of the study prescription, the user is provided with an opportunity to allow the study prescription to be executed or to prevent execution thereof, depending on whether the user believes that the impact on the longevity of the device is outweighed by the potential benefits of executing the study prescription. According to embodiments, the system 100 may include a component that performs this analysis in an automated fashion, based on criteria that may be provided by users and/or learned using a machine-learning technique.").
Regarding claim 30, Ternes in view of Roy teach the methods of Claim 21 on which this claim depends/these claims depend, respectively. Roy also teaches the computerized apparatus is further configured to receive data, via at least one network data interface, relating to one or more other living beings; and the generation of the error correction operational model is based at least in part on the received data relating to the one or more other living beings (Abstract "In certain other example embodiments, one or more parameters of a probability model may be estimated based on blood glucose reference sample-sensor measurement pairs" implies data from other living beings used for modeling, as reference samples would not be from the same patient or device wearer/user).
Regarding claims 31 and 32, Ternes in view of Roy teach the methods of Claim 21 on which this claim depends/these claims depend, respectively. Roy also teaches the received sensor data generated by the implanted sensor apparatus comprises at least one of (i) data generated by a calibration data source, or (ii) uncorrected data that has been corrected via a calibration source; and the plurality of instructions are further configured to, when executed, cause the computerized apparatus to apply the compensation for the estimated one or more unmodeled errors to data generated by the medicant delivery device as part of operation thereof in a second, corrected mode (Page 35 col 2 last paragraph "Input data to estimator 1902 may comprise sensor current (isig) and reference blood glucose concentration (BG). Output data of estimator 1902 may comprise an estimated parameter vector, P (e.g., P=[CF; OS] for Equation (16)), and a likelihood value, LH. A likelihood value LH, or more generally a quality indicator, may indicate a performance level of a calibration model. A parameter vector, P, along with sensor current, isig, may be fed to a calibration model unit 1904 in order to calculate sensor glucose concentration (SG). Although one estimator 1902 and one calibration model unit 1904 are shown in block diagram 1900, more than one of either or both may be implemented.").
Specifically for claim 32, Ternes teaches the plurality of instructions are further configured to, when executed, cause the computerized apparatus to cause generation of a second error correction operational model based at least on the received (i) uncorrected sensor data and (ii) data generated by the calibration data source, the second error correction operational model configured to estimate one or more unmodeled errors associated with the sensor apparatus, and produce at least one compensation for the estimated one or more unmodeled errors associated with the sensor apparatus (Para.0122 "Additionally, machine-learning techniques may be employed to adapt trigger criteria to more rapidly-changing scenarios such as, for example, the impact of adjusting to a new medication, the impact of a temporary adjustment in sleep schedule, the impact of the air quality in a particular location (e.g., outside vs. inside, downtown vs. at home, one city vs. another, etc.), the impact of an allergic reaction to an environmental stimulus, the impact of a psychological response to an increase or decrease in an amount of sunlight over the course of one or more days, the impact of a rapid change in barometric pressure, and/or the like.").
Regarding claim 33, Ternes in view of Roy teach the methods of Claim 32 on which this claim depends/these claims depend, respectively. Ternes also teaches the produced compensation for the estimated one or more unmodeled errors associated with the sensor apparatus are further utilized by the computerized apparatus to generate the error correction operational model associated with the medicant delivery device (Para.0141 "the analysis component may continue to learn and adjust as the advanced patient management system functions (i.e., in real time), or the analysis component may remain at a given level of learning and only advanced to a higher level of understanding when manually allowed to do so.").
Regarding claim 35, Ternes in view of Roy teach the methods of Claim 34 on which this claim depends/these claims depend, respectively. Roy also teaches transmission of at least a portion of the third data to a second computerized apparatus associated with the living being, such that the transmitted at least portion of the third data can be applied by the second computerized apparatus to correct for the one or more unmodeled errors associated with the delivery of the medicant by the medicant delivery device; and wherein the second computerized apparatus associated with the living being comprises a portable computerized device in wireless data communication with both the sensor apparatus and the medicant delivery device (Page 35 col 2 last paragraph "Input data to estimator 1902 may comprise sensor current (isig) and reference blood glucose concentration (BG). Output data of estimator 1902 may comprise an estimated parameter vector, P (e.g., P=[CF; OS] for Equation (16)), and a likelihood value, LH. A likelihood value LH, or more generally a quality indicator, may indicate a performance level of a calibration model. A parameter vector, P, along with sensor current, isig, may be fed to a calibration model unit 1904 in order to calculate sensor glucose concentration (SG). Although one estimator 1902 and one calibration model unit 1904 are shown in block diagram 1900, more than one of either or both may be implemented.").
Regarding claim 36, Ternes in view of Roy teach the methods of Claim 34 on which this claim depends/these claims depend, respectively. Ternes also teaches the plurality of instructions are further configured to cause, when executed, transmission of at least a portion of the first data to a second computerized apparatus via a network interface, the second computerized apparatus comprising computing apparatus configured to use at least one machine learning (ML) algorithm to generate ML data; and the causation of the generation of the error correction operational model comprises causing the computing apparatus to utilize the transmitted at least portion of the first data to generate the error correction operational model, the generation of the error correction operational model based at least in part on the ML data (Para.0122 "Additionally, machine-learning techniques may be employed to adapt trigger criteria to more rapidly-changing scenarios such as, for example, the impact of adjusting to a new medication, the impact of a temporary adjustment in sleep schedule, the impact of the air quality in a particular location (e.g., outside vs. inside, downtown vs. at home, one city vs. another, etc.), the impact of an allergic reaction to an environmental stimulus, the impact of a psychological response to an increase or decrease in an amount of sunlight over the course of one or more days, the impact of a rapid change in barometric pressure, and/or the like.", combined with the multiple calibration units of Roy from claim 35).
Regarding claim 37, Ternes in view of Roy teach the methods of Claim 36 on which this claim depends/these claims depend, respectively. Ternes also teaches receipt of data comprising the generated error correction operational model; the generation of the third data based at least on the received data comprising the generated error correction operational model and the second data; and transmission of at least a portion of the third data to a third computerized apparatus associated with the living being, the third data configured to enable the correction for the one or more unmodeled errors (Para.0141 "the analysis component may continue to learn and adjust as the advanced patient management system functions (i.e., in real time), or the analysis component may remain at a given level of learning and only advanced to a higher level of understanding when manually allowed to do so.").
Regarding claim 38, Ternes in view of Roy teach the methods of Claim 36 on which this claim depends/these claims depend, respectively. Roy also teaches the third computerized apparatus associated with the living being comprises at least one of an implanted or transcutaneous medicant delivery device of the living being (Page 28 col 1 lines 13-17 "As part of infusion set 38, a cannula may extend through skin and terminate in subcutaneous tissue to complete fluid communication between a reservoir (e.g., of FIG. 5) and subcutaneous tissue of a user's body").
Regarding claim 40, Ternes in view of Roy teach the methods of Claim 39 on which this claim depends/these claims depend, respecti