DETAILED ACTION
Response to Amendment
The response filed 11/24/25 amended claims 1-20 with no claims being cancelled, withdrawn or newly added.
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 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: Claims 1-18 recite an apparatus, claim 19 recites a non-transitory computer-readable storage media, and claim 20 recites a computer-implemented method; all of claims 1-20 are within the four statutory categories of invention.
Step 2A Prong 1: The claims recite an abstract idea of receiving data from a subject population, receiving external factor data, generating external factor data, generate external event impact metric initiate performance on a prediction based on metrics which is a certain method of organizing human activity (e.g. fundamental economic principles or practices including hedging, insurance, mitigating risk; commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations; managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions). The following limitations set forth the abstract idea as underlined.
Independent claim 1 and representative claims 19 and 20:
A system comprising one or more processors and memory storing executable instructions that, when executed by the one or more processors perform operations comprising:
receiving from one or more sensors configured to be coupled with a subject body of a subject population comprising a plurality of subject bodies, sensor data, wherein the one or more sensors comprise at least a continuous glucose monitor device
receiving, from a plurality of external sensors, external factor data associated with the subject population;
generating, based on applying a first trained machine learning model to the sensor data, the external factor data, and subject body vectors associated with the subject population, a population-level external event impact metric, wherein the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population;
generating a subject-level external event impact metric, based on applying a second trained machine learning model to the population- level external event impact metric, a subject body vector associated with the subject body, the sensor data, and real-time external factor data, wherein the subject-level external event impact metric represents a predicted impact of the one or more external events on the physiological or other metric associated with the subject body; and
initiating the performance of one or more prediction-based actions based on the subject- level external event impact metric, wherein the one or more prediction-based actions comprise transmission of instructions to an insulin delivery device coupled with the subject body, to control the insulin delivery device.
Dependent claims 2, 3, 9-18 include other limitations that narrow the abstract idea set forth in claim 1, specifying details on the sensor data and external factor data, the subject population, subject body vector, prediction-based actions, types of metrics, types of external events and weather conditions, and the subject body.
Step 2A Prong 2: The claim limitation recites the following additional elements that are beyond the judicial exception:
A system comprising one or more processors and memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:
applying a first trained machine learning model
one or more sensors
a continuous glucose monitor device
a plurality of external sensors
applying a second trained machine learning model
an insulin delivery device coupled with the subject body
These additional elements are not indicative of integration into a practical application because:
the memory and one or more processors, non-transitory computer-readable storage media, a first trained machine learning model and a second trained machine learning model are used in an “apply it” (or an equivalent) manner with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f);
the sensors generally link the abstract idea to a particular technological environment or field of use (see MPEP 2106.05(h)).
When considered as a combination, the combination of additional elements only generally limits the abstract idea to a particular technological environment of networked computing. As such the combination of additional elements does not amount to significantly more than the abstract idea.
Therefore, under step 2A prong II the claims are directed to an abstract idea.
Dependent claims 4-8 further narrow the additional element set forth in claim 1 by specifying various types of models and how the trained machine learning model is trained; this amounts to no more than amount to mere instructions to apply the exception and therefore these dependent claims do not include any additional elements beyond those already cited in independent claim 1 and hence also do not integrate the aforementioned abstract idea into a practical application. Further, some of the claimed models (e.g., the non-machine learning models) are mathematical concepts, which is another enumerated grouping of abstract ideas.
Step 2B: The claim limitations do not recite additional elements, or an ordered combination of additional elements, that are sufficient to amount to significantly more than the judicial exception.
As discussed with respect to step 2A prong 2 above, the additional elements by a memory and one or more processors, non-transitory computer-readable storage media, a first trained machine learning model and a second trained machine learning model are mere instructions to apply an exception, and do not integrate a judicial exception into a practical application at step 2A or provide an inventive concept at step 2B.
the claimed sensors generally link the abstract idea to a particular technological environment or field of use (see MPEP 2106.05(h))
According to MPEP 2106, a conclusion that an additional element is mere instructions to apply an exception under step 2A should be re-evaluated at step 2B. Thus, the recited memory and one or more processors, non-transitory computer-readable storage media, a first trained machine learning model and a second trained machine learning model are simply the use of a computer in its ordinary capacity and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262 and MPEP 2106.05 (f). For example, the additional elements only provide a result-oriented solution and lack details as to how the computer performs the modifications, which is equivalent to “apply it”. See Alice Corp. v. CLS Bank, 134 S. Ct. 2347, 2357 and MPEP 2106.05 (f).
When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of networked computing devices. Therefore, the combination does not amount to significantly more.
Therefore, when considering all the additional claim elements both individually and as an ordered combination, Examiner finds that the independent claims do not amount to significantly more than the exception.
Dependent Claims 2 – 18 further narrow the abstract idea and/or the additional elements disclosed in the claims have been addressed above. These claims do not integrate the abstract ideas into practical applications or amount to significantly more than the abstract idea, nor do they improve the functioning of a computer or technical field. Therefore, the dependent claims fail to cure this deficiency and are also rejected accordingly.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3 – 7, 9 – 11, 14 – 20 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over US PG Pub 20220361823 – Goldner et al. (hereinafter as GOLDNER) in view of US PG Pub 20210145370 – Hei et al (hereinafter as HEI) in further view of US PG Pub 20150182695 - Rosinko
Regarding Claim 1:
GOLDNER discloses:
A system comprising one or more processors and memory storing executable instructions (processors, memory, para. 0017) that, when executed by the one or more processors perform operations comprising:
receiving, from one or more sensors configured to be coupled (input data, wearable device, para. 0026), with a subject body (patient para. 0026) of a subject population (users or patients, para. 0026) comprising a plurality of subject bodies (users or patients, para. 0026) sensor data, wherein the one or more sensors comprise at least a glucose monitor device (receive input data from one or more user devices, including at least one biosensor (e.g., blood glucose sensors), para. 0026; some or all of the user devices are configured to continuously obtain any of the above data, para. 0027);
receiving, from a plurality of external sensors (sensors can include any suitable combination of sensors for monitoring various health parameters, such as an optical sensor, heart rate sensor, blood pressure sensor… or motion sensor, temperature sensor, location sensor, humidity sensor, para. 0052), external factor data (environmental conditions, air pressure, humidity, temperature, air quality) associated with the subject population and the subject body (users or patients, para. 0026);
generating, based on applying a first trained machine learning model (para. 0083 for a first model) to the sensor data (input data, para. 0026), the external factor data (air quality, para. 0026), and subject body vectors (i.e. demographic data, para. 0019) associated with the subject population (population information and plurality of patients, para. 0031), a population-level external event impact metric (prediction and performance metric based on input data, para. 0085 and 86), wherein the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population (prediction and performance metric based on input data, para. 0085 and 0086);
generating a subject-level external event impact metric (prediction and performance metric based on input data para. 0085 and 0086), based on applying a second trained machine learning model (para. 0083 for a second model) to the population- level external event impact metric (evaluation metrics, para. 0083) a subject body vector (i.e. demographic data, para. 0019) associated with the subject body (patient para. 0031), the sensor data, and real-time external factor data, wherein the subject-level external event impact metric represents a predicted impact of the one or more external events (air quality, para. 0019), on the physiological (physiological, para. 0043) or other metric associated with the subject body (patient, para. 0031); and
initiating the performance of one or more prediction-based actions based on the subject- level external event impact metric (prediction and performance metric based on input data para. 0085 and 0086).
GOLDNER does not explicitly teach:
receiving sensor data from one or more sensors, wherein the one or more sensors comprise at least a continuous glucose monitor device
transmissions of instructions to an insulin delivery device coupled with the subject body to control the insulin delivery device
However HEI teaches the use of an Artificial Pancreas system, which includes a continuous glucose monitor with a sensor, a control algorithm to link blood glucose levels with insulin delivery, which can be run on any number of devices including an insulin pump, and an automatic insulin delivery system. The Artificial Pancreas system has a closed-loop control system that automatically adjusts the insulin delivery rate based on the continuous feedback from the glucose sensor (para. 0029).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicant’s invention for GOLDNER ’s method of receiving sensor data of a wearable device to be modified by utilizing HEI’s method of wherein the sensor data is from blood glucose level measurements obtained using a continuous glucose monitoring device because doing so would allow GOLDNER to improve evaluation metrics (para. 0085 - Goldner) by collecting additional information, measurements and data to be used.
ROSINKO further teaches the use of remotely initiating delivery of a medicament bolus with a medical device, such as an insulin pump. The bolus amount can be calculated by or programmed or otherwise entered into a smartphone and then transmitted to the medical device (para. 0010). Within the housing of a medical device such as a pump, is a processor that controls the overall functions of the device, along with a transmitter/receiver. The processor may communicate with one or more other processors within the pump and/or one or more processors of other devices, for example, a continuous glucose monitor. The processor may also include programming that may allow the processor to receive signals and/or other data from an input device, such as a sensor that may sense pressure, temperature or other parameters (e.g., external sensor collecting external factor data) (para. 0035). A smartphone can be used to program a bolus delivery by insulin pump. The smartphone is wirelessly or otherwise operatively connected to an insulin pump. The smartphone can calculate the bolus taking into account parameters known through the pump such as insulin on board and blood glucose levels. The bolus request is then sent from the smartphone to the pump to initiate delivery of the bolus (para. 0042).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicant’s invention for GOLDNER ’s method of receiving sensor data to be modified by utilizing ROSINKO’s method of programming and transmitting the bolus to an insulin pump for delivery to a user because doing so would further enhance the ability of GOLDNER to provide recommended insulin intake or dosages and modify operation of user devices (para. 0014, 0086 - Goldner).
Regarding Claim 19:
Claim 19 recites limitations substantially similar to those set forth in claim 1 above; thus the same rejection applies. Further, Goldner teaches one or more non-transitory computer readable storage media including instructions, that when executed by one or more processors (processor 510 can be further configured to process instructions stored in the memory 520 or on the storage device 530; the memory 520 can be a computer-readable medium… in some embodiments, the storage device 530 can be a computer-readable medium; non-transitory computer program products i.e., physically embodied computer program products) are also described that store instructions that, when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein. para. 0098-0099).
Regarding Claim 20:
Claim 20 recites limitations substantially similar to those set forth in claim 1 above; thus the same rejection applies. Further, Goldner teaches a computer implemented method (system 102 can include processors, memory and/or other software and/or hardware components configured to implement the various methods described herein, para. 0017).
Regarding Claim 2:
GOLDNER/HEI/ROSINKO discloses claim 1:
GOLDNER does not disclose:
The system of claim 1, wherein the sensor data comprises at least one of blood glucose level measurements obtained using a continuous glucose monitoring device or physiological data obtained using a wearable device.
HEI discloses:
wherein the sensor data comprises at least one of blood glucose level measurements obtained using a continuous glucose monitoring device (device for continuous glucose monitoring, continuous monitoring para. 0027, 0062) or physiological data obtained using a wearable device (smart wearable device, para. 0060).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicant’s invention for GOLDNER ’s method of receiving sensor data of a wearable device to be modified by utilizing HEI’s method of wherein the sensor data is from blood glucose level measurements obtained using a continuous glucose monitoring device or physiological data obtained using a wearable device because doing so would allow GOLDNER to improve evaluation metrics (para. 0085 - Goldner) by collecting additional information, measurements and data to be used.
Regarding Claim 3:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the external factor data comprises at least one of environmental conditions (air quality para. 0019), weather data (temperature, para. 0019), or temporal data.
Regarding Claim 4:
GOLDNER/HEI/ROSINKO discloses claim 1 and:
The system of claim 1, wherein the first trained machine learning model is at least one of a linear mixed effects model (linear para. 0035) or a non-linear hierarchical model (hierarchical clustering, para. 0035).
Regarding Claim 5:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the second trained machine learning model is at least one of a random forest classifier (random forest, para. 0035), a linear model (linear, para. 0035), a deep neural network (deep learning algo, para. 0035), anelastic net regression model, a clustering model (clustering, para. 0035), a k-means classification model (k-means, para. 0035), a support vector machine (support vector, para. 0035), or a latent Dirichlet allocation model.
Regarding Claim 6:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the first trained machine learning model is trained using historical sensor data (the system 102 can analyze the obtained input data, including historical data, para. 0034) and historical external factor data (Any other historical statistical data, para. 0034)
Regarding Claim 7:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 6, wherein the first trained machine learning model is configured to output one or more of optimized model coefficients or confidence intervals for one or more model coefficients (the system can determine blood pressure monitoring systems for the devices based on output from the machine-learning model to achieve a threshold confidence score, para. 0095).
Regarding Claim 8:
GOLDNER/HEI/ROSINKO disclose claim 1
GOLDNER does not disclose
The system of claim 1, wherein the second trained machine learning model is trained using risk labels assigned to subject body vectors based on segmenting the subject population according to the first trained machine learning model.
HEI discloses:
wherein the second trained machine learning model is trained using risk labels assigned to subject body vectors based on segmenting the subject population according to the first trained machine learning model (wherein there is a label for risk para. 0062).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicant’s invention for GOLDNER ’s method of receiving sensor data of a wearable device to be modified by utilizing HEI’s method of wherein the second trained machine learning model is trained using risk labels assigned to subject body vectors based on segmenting the subject population according to the first trained machine learning model such that this would allow GOLDNER to improve evaluation metrics (para. 0085 - Goldner) by using additional information, measurements and data to be used in the training of the machine learning model.
Regarding Claim 9:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 8, wherein the subject population is segmented according to one or more of random slopes or random intercepts (random intervals para. 0028).
Regarding Claim 10:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the subject body vector comprises one or more of demographic data (demographic, para. 0031), medical history data (medical conditions, para. 0019), or other user data associated with the subject body (diagnostic data, para. 0019).
Regarding Claim 11:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the one or more prediction-based actions comprise one or more of transmission of alerts to computing devices, transmission of instructions to insulin delivery devices associated with the subject body, adjustments to medical equipment associated with the subject body (notification para. 0059, adjust device para. 0095) or adjustments to allocations of medical, computing, hospital, facility, and/or human resources to the subject body or the subject population.
Regarding Claim 12:
GOLDNER/HEI/ROSINKO disclose claim 1
GOLDNER does not disclose
The system of claim 1, wherein the physiological or other metric comprises one or more blood glucose metrics.
HEI discloses:
wherein the physiological or other metric comprises one or more blood glucose metrics (measurement para. 0050).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicants invention for GOLDNER ’s method of receiving sensor data of a wearable device to be modified by utilizing HEI’s method of wherein physiological or other metric comprises one or more blood glucose metrics such that this would allow GOLDNER to improve evaluation metrics (para. 0085 - Goldner) by collecting additional information, measurements and data to be used.
Regarding Claim 13:
GOLDNER/HEI/ROSINKO disclose claim 1
GOLDNER does not disclose
The system of claim 12, wherein the one or more blood glucose metrics comprise time in range (TIR), interquartile range (IQR), mean amplitude of glycemic excursions (IMAGE), percent coefficient of variation (%CV), or number of peaks.
HEI discloses:
wherein the one or more blood glucose metrics comprise time in range (TIR)(fig. 6), interquartile range (IQR), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), or number of peaks (peak, para. 0095).
It would be obvious to one of ordinary skill in the art before the effective filling date of the applicants invention for GOLDNER ’s method of receiving sensor data of a wearable device and utilizing HEI’s method of time in range such that this would allow GOLDNER to improve evaluation metrics (para. 0085 - Goldner) by collecting additional information, measurements and data to be used.
Regarding Claim 14:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The computing apparatus of claim 1, wherein the subject body is a human (patient, para. 0031).
Regarding Claim 15:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the one or more external events are future external events (wherein values are future values, para. 0054).
Regarding Claim 16:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 15, wherein the future external events comprise one or more of a weekend, a holiday, a change in weather conditions, a birthday, or a change in economic conditions (weekend, para. 0013).
Regarding Claim 17:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 16, wherein the weather conditions comprise precipitation, air quality index (AQI) (air quality, para. 0019), temperature, snowfall, snow depth, or humidity.
Regarding Claim 18:
GOLDNER/HEI/ROSINKO discloses claim 1 and GOLDNER teaches:
The system of claim 1, wherein the subject body vectors associated with the subject population (patients, para. 0019) comprise one or more of clinical data or demographic information for subject bodies of the subject population (demographic para. 0019).
Response to Arguments
Applicant's arguments filed 11/24/25 have been fully considered but they are not persuasive.
Applicant argues that claim 1 as amended recites real-time determination of a condition of a patient that triggers automated action that is neither a mathematical concept nor organizing human activity.
The examiner disagrees that claim 1 is not organizing human activity. Per MPEP 2106.04(a)(2)(II)(C), the subgrouping “managing personal behavior or relationships or interactions between people” include social activities, teaching and following rules and instructions. One such example of managing personal behavior recited in a claim includes 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).
The examiner agrees that claim 1 does not recite a mathematical concept; however, mathematical concept is not an abstract idea grouping asserted as being applicable to claim 1. However, dependent claims 4-8 narrow claim 1 by specifying the various embodiments that the first and second trained machine learning models can take. Linear mixed effects model, non-linear hierarchical model, random forest classifier, linear model, elastic net regression model, clustering model, k-means classification model, support vector machine and latent Dirichlet allocation models are all mathematical techniques, algorithms and/or models and applied to collected data to generate a predicted impact metric. Examiner notes that paragraphs 66-67 of the specification note that the ML models use algorithms, “such algorithms may be prediction (e.g., linear regression) algorithms, classification (e.g., decision trees, k-nearest neighbors) algorithms, time-series forecasting (e.g., regression-based) algorithms, association algorithms, clustering algorithms (e.g., K-means clustering, Gaussian mixture models, DBscan), or Bayesian methods (e.g., Naive Bayes, Bayesian model averaging, Bayesian adaptive trials), image to image models (e.g., FCN, PSPNet, U-Net) sequence to sequence models (e.g., RNNs, LSTMs, BERT, Autoencoders) or Generative models (e.g., GANs)” and further that “ML models may implement statistical algorithms, such as dimensionality reduction, hypothesis testing, one-way analysis of variance (ANOVA) testing, principal component analysis, conjoint analysis, neural networks, support vector machines, decision trees (including random forest methods), ensemble methods, and other techniques”
Per MPEP 2106.04(a)(2)(I)(A), it is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). Examples of mathematical relationships recited in a claim include organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.
Applicant argues the amended elements of claim 1 constitute an improvement in a technical field and computer functionality such that the claim, as a whole, integrates any alleged abstract idea into a practical application.
Examiner disagrees. Although the claims have been amended to specify that the received sensor data is received from one or more sensors, including at least a continuous glucose monitor device, and receiving external factor data from a plurality of external sensors, the claims do not recite any detail on how the sensors collect or gather data, what type of data is being collected beyond being “of a subject body” or “external factor data associated with the subject population and the subject body”. The sensors do not appear to be improved, in that they do not collect or gather data in an improved way, or collect or gather data that is otherwise unable to be captured using other sensors. Furthermore, there is no improvement to the underlying processors executing the instructions. Similarly, there is no functional improvement to the continuous glucose monitor, as there are no details claimed in an improved way of how the continuous glucose monitor collects or gathers data, or collects or gathers new data that is otherwise unable to be captured by continuous glucose monitors. Likewise, though instructions are transmitted to an insulin delivery device to control said insulin delivery device, there is no indication that the instructions provide a new mode of operation or functionality or that the insulin delivery device operates in an unconventional way. Thus, there is no improvement to the functioning of the computer or any of the recited sensors or devices recited in the claim, and the abstract idea is not integrated into a practical application.
Applicant argues that neither Goldner nor Hei, whether taken alone or in any proper combination teaches or suggests the amended feature of “initiating the performance of one or more prediction-based actions based on the subject-level external impact metric, wherein the one or more prediction-based actions comprise transmission of instructions to an insulin delivery device coupled with the subject body”.
Goldner teaches initiating prediction-based actions based on an impact metric (adjusting data collection settings, utilizing new user devices, modify operation of user devices, notifying the user to provide validation blood pressures, para. 0086) but does not teach the amended feature of “transmission of instructions to an insulin delivery device coupled with the subject body”. However, the prior art rejection has been updated above to incorporate Rosinko, which teaches transmission of instructions to an insulin delivery device coupled with the subject body.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
King (US PGPub 20090036753)
Yodfat (US PGPub 20080319384, 20080319381, 20080234663)
Wessel (US PGPub 20080015422)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER H CHOI whose telephone number is (469)295-9171. The examiner can normally be reached M-Th 9am-7pm.
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/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
