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 .
Response to Amendment
The amendment filed April 6, 2026 has been entered.
Applicant’s amendments have obviated the objection to the Specification presented in the Non-Final Office Action dated April 6, 2026.
Claims 8 and 9 are cancelled, and their corresponding limitations have been incorporated into amended independent claim 1. Claims 17 and 18 are cancelled, and their corresponding limitations have been incorporated into amended independent claim 15. Claims 21 and 22 are new.
Claims 1-7, 10-16, and 19-22 are pending.
Response to Arguments
Applicant's arguments filed April 6, 2026 regarding the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Regarding Applicant’s argument:
“Here, the human mind is not practically capable of predicting a range of future glucose levels over one or more hours based on glucose data and meal information and outputting a visualization of the range of future glucose levels including predicted minimum and maximum values… It is unclear how the user would perform an evaluation of glucose data and meal information to arrive at a predicted range of future glucose levels including predicted minimum and maximum values over a period of one or more hours as claimed. Therefore, claim 1 does not relate to an abstract idea at Step 2A, prong I.”
Examiner disagrees. Firstly, the human mind is capable of predicting a range of values from an observed trend of values – an individual is capable of observing seasonal weather data and predicting maxima and minima temperatures that may arise in future seasons, accuracy notwithstanding. Secondly, Outputting a visualization is not identified as part of the mental process, but extra-solution data output.
Regarding Applicant’s argument:
“Claim 1 should alternatively be found to be patent eligible at Step 2A, Prong II, as the claimed invention provides an improvement to glucose visualization systems by providing a dynamic visualization of the impact of the user's meal choices on future glucose levels and that updates as glucose data is collected by the glucose monitoring device. The Specification discloses that glucose visualizations provided by legacy tools allow users to examine past events, but do not allow the user to analyze the impact of current meals and other events on future glucose levels. Specification, [0006], [0009]. Because a user's choices (e.g., meals or exercise) occur at one time but affect glucose levels at a later time, users may not readily appreciate how their choices impact their glucose levels. The claimed visualization system facilitates understanding of the impact of the user's meal choice on future glucose levels by predicting future glucose levels based on current glucose levels and the user entered meal information and displaying a visualization of current glucose levels and the predicted future glucose levels.”
The alleged improvement of “allow[ing] the user to analyze the impact of current meals and other events on future glucose levels” is not an improvement, but rather the result of post-solution output of the results of data gathering and analysis.
Regarding Applicant’s argument:
“When considering the claim as a whole, the claimed invention does not merely recite an idea of a solution, but recites a particular manner to generate a glucose visualization (including a sensor control device with in vivo glucose sensor, receiving device, processors storing visualization and glucose monitoring applications and that execute a pre-trained machine learning algorithm), and that recites specific details on the contents of the glucose data visualization (displaying received glucose data, a most recent glucose level, range of future glucose levels with predicted minimum and maximum values that are visually distinguishable from the received glucose data). Therefore, claim 1 and its dependent claims should be found to be patent eligible at Step 2A, prong II.”
The particular manner of generating a glucose visualization is merely an abstract process of evaluating gathered data, receiving necessary data from known sensors, and performing the mental process of judgement regarding how data is to be output, then finally outputting such data. The “receiving device,” is directed to a conventional computing device (Specification Paragraph 0073: “a mobile phone, tablet, personal computing device, or other similar computing device…”) utilized to carry out the abstract idea and judgement of how data is output. Data which is received by the “receiving device” comes from a sensor control device in communication with an in vivo glucose sensor, which Applicant demonstrates as conventional (See rejection below; Specification Paragraph 0004-0006), and does nothing more than necessary data gathering for the abstract idea. The specifics regarding how data is output can be considered a decision of the human mind, with its final output being merely post-solution activity. Thus, the combination of elements in the claimed invention cannot be considered directed to a particular machine, but merely the requisite data-gathering tools and a generic computer for carrying out claimed steps.
Applicant's arguments filed April 6, 2026 regarding the rejection of the claims under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Regarding Applicant’s argument:
“The Office cites to Galley's disclosure at page 15 as allegedly disclosing the features of original claims 8 and 9. However, the cited passages of Galley disclose that "the method may further comprise receiving user data indicative of a further glucose level influencing event having occurred during and/or subsequent to the prediction time window...." Galley, p. 15, lines 14-18 (emphasis added). Thus, the cited passage refers to receiving event data, not glucose data. Thus, Galley does not disclose updating a range of predicted future glucose levels at an interval at which glucose data is received.
Galley further discloses that "continuous glucose monitoring may be implemented as a nearly real-time or quasi-continuous monitoring procedure frequently or automatically providing/updating analyte values without user interaction." See Galley, p. 15, lines 24-26. This passage provides a general disclosure that a continuous glucose monitor collects analyte data in real-time or near real-time. This passage does not disclose or suggest that a visualization of a range of future glucose levels is updated at the interval at which glucose data is received, or that a glucose data visualization is updated at all.”
Applicant proposes a position wherein the device of Galley appears to only provide a window of predicted glucose values based on an unchanged set of received glucose data. That is, once a continuous glucose monitoring data is entered, the visualization of Galley does not update. Examiner disagrees. Galley states:
“The method comprises: receiving continuous glucose monitoring data indicative of a glucose level in a bodily fluid from a continuous glucose monitoring sensor device coupled to a person having diabetes; determining, using historical data indicative of glucose level influencing events of the person having diabetes and based on at least one predicted glucose level influencing event, a prediction time window; determining, based on the continuous glucose monitoring data, a plurality of predicted glucose values for the prediction time window; and displaying, at least partially, the plurality of predicted glucose values” (Page 2, emphasis added).
“The method may further comprise receiving user data indicative of a further glucose level influencing event having occurred during and/or subsequent to the prediction time window and/or the extended prediction time window and, preferably, modifying the historical data based on the user data. Thus, the method may further comprise updating the statistical model and/or the prediction algorithm(s) based on the modified historical data” (Page 15, emphasis added)
Figs. 3a, 3b: the visualization of Galley includes a time marker for indicating the present time.
Thus, Galley teaches a device which repeatedly evaluates a statistical model (i.e., a pre-trained neural network – see modification by Arunachalam) to obtain historical data including meal events (i.e., carbohydrate intake) to generate a length of a prediction window, then utilizes continuous glucose monitoring values to generate predicted future glucose values within said window. The fact that Galley provides an indication of the current time is further evidence that the visualization is updated (along with any corresponding evaluations performed by the statistical model).
Regarding Applicant’s argument:
“Second, the cited references fail to disclose or suggest, "wherein the range of future glucose levels is visually distinguishable from the line graph of glucose levels received from the sensor control device," as recited in claim 1…Galley does not disclose a line graph and instead shows individual data points as circular dots rather than as a line as shown in FIG. 3a of Galley reproduced below…”
In a first interpretation, the measured glucose values 31 clearly trend in a line of singular points per each point along the X-axis. In a second interpretation, the line marked “now” is part of the graph of measured glucose values, whereby the future predicted values are distinguished by being on the right side of such line.
Regarding Applicant’s argument:
“Further, Galley does not show future glucose levels visually distinguished from a line graph of received glucose levels. Instead, Galley shows all glucose data in the same manner, i.e., as a circular dot. The Office alleges that future glucose values are visually distinguished as they are shown with error bars. See Office Action at p. 18. However, FIG. 3a does not uniformly show future glucose values with error bars and some future glucose levels are shown as circular dots in the same manner as the historical glucose data and thus the error bars cannot be relied on to distinguish between future and historical glucose data.”
Examiner notes that the citation used in the previous rejection was directed to “received glucose levels” being distinguishable from the predicted glucose levels within window 30, and made a typographical error in specifying “after” rather than “after;” the rejection has now been updated to recite the previously intended description of received glucose levels, as follows: (Figs. 3a, 3b: : dots indicating received glucose levels do not have confidence intervals and appear before.
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 1-7, 10-16, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of the claims recites steps or instructions for ascertaining and processing data to measure a blood pressure of a mammal subject, which is grouped as a mental process. Accordingly, each of the claims recites an abstract idea.
Independent claims 1 and 15 similarly recite limitations comprising:
a sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user to collect information about glucose levels (additional element, data gathering);
a receiving device in wireless communication with the sensor control device via a Bluetooth communication protocol, the receiving device comprising a display, an input component, and a power supply, wherein the receiving device is configured to receive meal information entered by the user (additional element, data gathering); and
one or more processors in communication with the receiving device, wherein the one or more processors are coupled to a memory storing a glucose monitoring application and a visualization application, wherein when the visualization application is executed by the one or more processors, the one or more processors are caused to:
receive glucose data collected by the sensor control device (additional element, data-gathering);
predict, using a pre-trained machine learning model comprising a neural network, a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user;
and output, on the display of the receiving device, a visualization of the glucose data (extra-solution activity),
the visualization comprising:
a line graph of glucose levels based on the glucose data collected by the sensor control device over time;
an indication of a most recent glucose level received from the sensor control device; and
the range of future glucose levels, wherein the range of future glucose levels is bounded by predicted minimum values and predicted maximum values over time,
wherein the range of future glucose levels is determined based on a confidence metric associated with each of the future glucose levels, and
wherein the range of future glucose levels is visually distinguishable from the line graph of glucose levels received from the sensor control device,
update, by the pre-trained machine learning model, the predicted range of future glucose levels at a regular interval based on an interval at which glucose data is received from the sensor control device (evaluation or judgement) (evaluation or judgement and/or extra-solution activity).
As indicated above, the independent claim recites at least one step or instruction grouped as a mental process. Therefore, each of the independent claims recites an abstract idea. Each limitation, aside from language reciting generic computer components, can be grouped as a mental process (see italicized portions above), and is addressed as follows:
The limitation of predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user merely requires a user to obtain requisite data and perform evaluation thereon to yield a prediction of future glucose values. The limitation of using a pre-trained neural network is addressed later.
The limitation of providing specific visualization details (i.e., “the visualization comprising”) is merely a series of judgements as to how obtained should be displayed, whereby their eventual display is merely extra-solution output of results of analysis of received data.
The limitation of update, by the pre-trained machine learning model, the predicted range of future glucose levels at a regular interval based on an interval at which glucose data is received from the sensor control device entails a user merely appending additional data and performing additional evaluations thereon to provide an updated result. The phrase “by the pre-trained machine learning model” merely entails carrying out such steps through implementation of a pre-trained machine learning model.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) (i.e., evaluation of machine learning models) to derive a conclusion based on input of medical data, which corresponds to concepts identified as abstract ideas by the courts, such as in Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989). The concept of the recited steps above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea (e.g. limitations relating to the data gathered, decisions of extra-solution display, or particular steps which are entirely embodied in the mental process) and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial), collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), collection, storage, and recognition of data (Smart Systems Innovations).
Step 2A, Prong 2
The above-identified abstract idea is not integrated into a practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use.
More specifically:
Independent claims 1 and 15 similarly recite the following additional elements:
a sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user;
a receiving device in wireless communication with the sensor control device via a Bluetooth communication protocol, the receiving device comprising a display, an input component, and a power supply;
one or more processors in communication with the receiving device… a memory storing a glucose monitoring application and a visualization application;
a pre-trained machine learning model comprising a neural network.
Such additional elements are generically recited elements which do not improve the functioning of a computer or any other technology or technical field.
The sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user encompasses electronics necessarily found in typical continuous glucose monitoring systems (i.e., a sensor portion and electronics to transfer data from an implanted/invasive sensor). The claim recites merely acquiring data from generically recited sensor components, having no operative connection to the receiving device and one or more processors executing instructions of the memory storing a glucose monitoring application and a visualization application besides communication of obtained data, which amounts to insignificant, extra-solution activity in the form of mere data gathering, which does not constitute an integration into a practical application. Although the sensors may imply particular structure, their use in the mental process is merely extra-solution. See MPEP 2106.05(b).III:
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted)”
The receiving device… and one or more processors are also recited at a high-level of generality (i.e., as a generic processors and memory performing a generic computer function of performing calculations and storing data, respectively) such that it amounts no more than mere instructions to apply the exception using a generic computer component. As per Applicant’s Specification, a receiving device… may be “such as a mobile phone, tablet, personal computing device, or other similar computing device capable of communicating with analyte sensor 110 over a communication link” (Paragraph 0073). Thus, a receiving device is reasonably construed as a number generic computing devices listed by Applicant known to possess each of a display, an input component, power supply and communication protocol capabilities. The limitations of each of a glucose monitoring application and visualization application are merely directed to either abstract ideas or extra-solution data processing carried out via generic computer elements.
The limitations reciting the use of a pre-trained machine learning model comprising a neural network provide nothing more than mere instructions to implement an abstract idea on a generic computer. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer:
(1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished;
(2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and
(3) the particularity or generality of the application of the judicial exception.
The limitation of “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user” and is performed “using a pre-trained machine learning model comprising a neural network.” The trained machine learning model is used to generally apply the abstract idea without placing any limits on how the pre-trained machine learning model comprising a neural network functions. These limitations recite the outcome of “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user” and do not include any details about how the prediction steps are accomplished. See MPEP 2106.05(f). The recitation of “using a pre-trained machine learning model comprising a neural network” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element of a pre-trained machine learning model comprising a neural network limits the identified judicial exception “predict… a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (pre-trained neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Thus, such additional elements do not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment (processing and display of glucose data via known display devices), such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified generically recited elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea is not integrated into a practical application.
Moreover, the above-identified abstract idea is not integrated into a practical application under because the claimed method and system merely implements the above-identified abstract idea using rules (e.g., computer instructions) executed by a computer (e.g., machine as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract ideas identified above in the independent claims (and their respective dependent claims) are not integrated into a practical application.
Dependent claims 2-4, 10-16, and 18-20 merely recite limitations pertaining to decisions of how data is output post-solution, or are directed to additional steps encompassed by the judicial exception or requisite data-gathering steps.
Dependent claims 5-7 recite an additional element of a trusted computer system, which is also interpretable, without further definition provided in the claims pertaining to the descriptor “trusted,” as a generic computing device which carries out limitations of the abstract idea including generation of visualization data and extra-solution transmission of data.
Accordingly, the claims are each directed to an abstract idea.
Step 2B
None of the claims include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than the abstract idea for at least the following reasons:
Independent claims 1 and 15 similarly recite the following additional elements:
a sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user;
a receiving device in wireless communication with the sensor control device via a Bluetooth communication protocol, the receiving device comprising a display, an input component, and a power supply;
one or more processors in communication with the receiving device… a memory storing a glucose monitoring application and a visualization application;
a pre-trained machine learning model comprising a neural network.
As per Applicant’s discussion of background devices (Paragraphs 0004-0006), the sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user encompasses electronics necessarily found in typical continuous glucose monitoring systems (i.e., a sensor portion and electronics to transfer data from an implanted/invasive sensor). Thus, such components are considered parts of a well-understood, routine, and conventional element (i.e., continuous glucose monitoring systems).
As per Applicant’s Paragraph 0073, a receiving device… comprising a display, an input component, and a power supply may be “such as a mobile phone, tablet, personal computing device, or other similar computing device capable of communicating with analyte sensor 110 over a communication link” (Paragraph 0073). Thus, applicant establishes that a number of known devices are capable of fulfilling the limitations of a receiving device. Applicant’s one or more processors in communication with the receiving device… a memory storing a glucose monitoring application and a visualization application are embodied by generic processors and memory known which are constituent parts of the list of known devices Applicant provides.
Applicant’s disclosure is not particular regarding the details of the pre-trained machine learning model comprising a neural network, and recites “the models described above may be generated via any known machine learning techniques—e.g., unsupervised learning, supervised learning, semi-supervised learning, re-informed learning, clustering, classification, regression, decision tree, neural networks, anomaly detection or any others” (Paragraph 0211). No special programming or algorithms are indicated for how such algorithms operate. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this computer-implemented limitation performs non-specialized functions known by those of ordinary skill in the medical technology arts (i.e., machine learning model processing of medical data). Thus, Applicant's specification essentially admits that this computer-implemented limitation is conventional and performs well understood, routine and conventional activities in the computing or medical technology arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the pre-trained machine learning model comprising a neural network because it describes such an additional element in a manner that indicates that the additional element is 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) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding limitations that perform “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications). Furthermore, as explained in Step 2A, Prong Two, limitations directed to the pre-trained machine learning model comprising a neural network are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Dependent claims 5-7 recite an additional element of a trusted computer system. As per Applicant’s Paragraph 0128, a trusted computer system “may include servers, desktops, and other type of electronic device that support CGM system 800 by processing web-based traffic and HTTP request methods.” Since most generic computer processors are capable of processing web-based traffic and HTTP request methods, such an element is considered well-understood, routine, and conventional.
Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process.
The recitation of the above-identified additional limitations in the claims amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
For at least the above reasons, the claims are directed to applying an abstract idea on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. In other words, none of the claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment (processing of sensor data). That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself, or (ii) provide a technical solution to a problem in a technical field.
Therefore, none of the claims amounts to significantly more than the abstract idea itself.
Accordingly, the claims are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.
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, 5-7, 10-13, 15, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Arunachalam et al. (US 20200098463 A1) (hereinafter – Arunachalam).
Re. Claims 1 and 15: Galley teaches a system for visualizing predicted glucose levels (Abstract; Figs. 3a, 3bL plotted points form a trend line over time),
the system comprising:
a sensor control device comprising sensor electronics coupled to an in vivo glucose sensor comprising a portion configured to be positioned in a body of a user to collect information about glucose levels (Fig. 1: continuous glucose monitoring sensor device 4; Page 17, lines 7-8: “For example, the continuous glucose monitoring sensor device 4 can be a disposable glucose sensor that is, e.g., worn under the skin”);
a receiving device in wireless communication with the sensor control device (Fig. 1: user device 1 possessing processor 1a; Page 16: “The one or more processors 1a may also be separately located within discrete components such as, for example, a glucose meter, a medication delivery device, a mobile phone, a portable digital assistant (PDA), a mobile computing device such as a laptop, a tablet, or a smart phone;” Page 17: “The user device 1 and/or the input device 2 may comprise or be coupled to a continuous glucose monitoring sensor device 4 for providing biological data indicative of properties of an analyte and/or a continuous glucose monitoring system coupled to a person having diabetes. The input device 2 may be configured to receive raw data from the continuous glucose monitoring sensor device 4 and process the raw data into glucose monitoring data and, preferably, transmit the glucose monitoring data to the user device 1… For example, the continuous glucose monitoring sensor device 4 can be a disposable glucose sensor that is, e.g., worn under the skin. The user device 1 may be a remote control for the continuous glucose monitoring sensor device 4;” Examiner notes that transmission from a sensor worn under the skin implies wireless communication; additionally, the user device 1 being a “remote control” also implies wireless communication with an implanted glucose sensor 4).
Galley teaches that an under the skin continuous glucose monitoring sensor 4 transmits data to user device 1 (Pages 16-17; Fig. 1), but is silent regarding whether a Bluetooth communication protocol is used.
Arunachalam teaches analogous art in the technology of glucose monitoring (Abstract), and further teaches communication between a continuous glucose monitor and a user device comprising a smartphone using a Bluetooth network ((Fig. 8: network 810; Paragraph 0086; Paragraph 0088: “For example, in accordance with one embodiment, the network 810 is realized as a Bluetooth network, wherein the medical device 802 and the client device 806 are paired with one another (e.g., by obtaining and storing network identification information for one another) by performing a discovery procedure or another suitable pairing procedure”).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the Bluetooth communication protocol of Arunachalam for the communication protocol of Galley. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Galley as modified by Arunachalam further teaches:
the receiving device comprising a display, an input component, and a power supply (Page 16: “The one or more processors 1a may also be separately located within discrete components such as, for example, a glucose meter, a medication delivery device, a mobile phone, a portable digital assistant (PDA), a mobile computing device such as a laptop, a tablet, or a smart phone;” Examiner notes that any one of a portable digital assistant, mobile phone, laptop, tablet, and smartphone comprise each of the claimed elements of a display, input component, and a power supply),
wherein the receiving device is configured to receive meal information entered by the user (Fig. 1: medical server 5 and input device 2 in communication with user device 1; Page 13: “The statistical model may be trained using the historical data as training data, e.g., using a machine learning algorithm. The historical data may comprise times (of day) and/or dates of the glucose level influencing events (for example, meal consumption time stamps);” Page 15: “The glucose level influencing events may comprise at least one of (or consist of) meal consumption (i.e., carbohydrate intake)…;” Page 18: “The historical data may be stored in the memory 5b of the medical server 5 and/or the memory 1b of the user device 1. The historical data may be received and/or created and/or modified by the medical server 5 and/or the user device 1 , e.g., via the input device 2”); and
one or more processors in communication with the receiving device (Fig. 1: processor 1a),
wherein the one or more processors are coupled to
a memory storing a glucose monitoring application (Page 2: “The method comprises: transmitting, from the user device to a medical server, continuous glucose monitoring data indicative of a glucose level in a bodily fluid; receiving, in the user device from the medical server, a plurality of predicted glucose values for a prediction time window, wherein the prediction time window has been determined using historical data indicative of glucose level influencing events of the person having diabetes and based on at least one predicted glucose level influencing event, wherein the plurality of predicted glucose values has been determined based on the continuous glucose monitoring data… Alternatively, the continuous glucose monitoring data may be determined, preferably measured, by the user device, in particular by a continuous glucose monitoring sensor device that is part of the user device”) and
a visualization application (Figs. 3a, 3b; Page 2: “… displaying, by the user device, the plurality of predicted glucose values at least partially”),
wherein when the visualization application is executed by the one or more processors, the one or more processors are caused to:
receive glucose data collected by the sensor control device (Fig. 1: user device 1 and medical server 5 in communication with continuous glucose monitoring sensor device 4).
Galley discloses an invention which can predict, using a pre-trained machine learning model (Page 13), a range of future glucose levels over a period of one or more hours based on the glucose data collected by the sensor control device and the meal information entered by the user (Figs. 3a, 3b: visualizations including predictions of glucose trends provided by statistical model).
Galley is deficient in reciting that the statistical model comprises a neural network.
Use of a trained neural network to predict future glucose values based on input data including meal information is known from Arunachalam (Paragraphs 0102, 0141).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the neural network of Arunachalam for the non-descript statistical model of Galley. Thus, the simple substitution of one known element for another producing a predictable result (providing a predictive algorithm to predict future glucose values) renders the claim obvious.
Galley as modified by Arunachalam further teaches:
output, on the display of the receiving device, a visualization of the glucose data,
the visualization comprising:
a line graph of glucose levels based on the glucose data collected by the sensor control device over time (Figs. 3a, 3b);
an indication of a most recent glucose level received from the sensor control device (Figs. 3a, 3b: see vertical line labeled “now” on x-axis); and
the range of future glucose levels, wherein the range of future glucose levels is bounded by predicted minimum values and predicted maximum values over time (Figs. 3a, 3b: see vertical bars 33 for predicted glucose levels 32),
wherein the range of future glucose levels is determined based on a confidence metric associated with each of the future glucose levels (Page 18, lines 19-20: “Confidence intervals for the predicted glucose values 32 are represented by bars 33”), and
wherein the range of future glucose levels is visually distinguishable from the line graph of glucose levels received from the sensor control device (Figs. 3a, 3b: : dots indicating received glucose levels do not have confidence intervals and appear before vertical line marked “now”); and
update, by the pre-trained machine learning model, the predicted range of future glucose levels at a regular interval based on an interval at which glucose data is received from the sensor control device (Page 15; “A glucose level and/or glucose values may be determined by continuous glucose monitoring via a fully or partially implanted sensor and/or worn sensor… Continuous glucose monitoring may be implemented as a… quasi-continuous monitoring procedure frequently or automatically providing/updating analyte values without user interaction;” Examiner notes that the statistical model is responsive to such data in providing visualizations shown in Figs. 3a, 3b).
Claim 15 recites limitations of claim 1 mutatis mutandis embodied in a method; thus, the rejection of claim 1 encompasses each limitation required by claim 15.
Re. Claim 5: Galley as modified by Arunachalam teaches the invention according to claim 1. Galley further teaches the invention further comprising a trusted computer system, wherein the trusted computer system is in wireless communication with the receiving device (Fig. 1: medical server 5 in communication with user device 1).
Re. Claim 6: Galley as modified by Arunachalam teaches the invention according to claim 5. Galley further teaches the invention wherein the receiving device receives data from trusted computer system to generate the visualization of the glucose data (Page 2: “… transmitting, from the user device to a medical server, continuous glucose monitoring data indicative of a glucose level in a bodily fluid; receiving, in the user device from the medical server, a plurality of predicted glucose values for a prediction time window…;” see visualizations of prediction time windows generated at Figs. 3a, 3b).
Re. Claim 7: Galley as modified by Arunachalam teaches the invention according to claim 1. Galley further teaches the invention wherein the trusted computer system is configured to generate the visualization of the glucose data and to transmit the visualization of the glucose data to the receiving device for display (see rejection of claim 6).
Re. Claim 10: Galley as modified by Arunachalam teaches the invention according to claim 1. Galley further teaches the invention wherein the predicted range of future glucose levels is updated in real-time (Page 15; “Continuous glucose monitoring may be implemented as a nearly real-time… monitoring procedure frequently or automatically providing/updating analyte values without user interaction;” Examiner notes that the statistical model is responsive to such data in providing visualizations shown in Figs. 3a, 3b).
Re. Claims 11 and 19: Galley as modified by Arunachalam teaches the invention according to claims 1 and 15. Galley further teaches the invention wherein the confidence metric is based in part on a time since the glucose data was received (Figs. 3a, 3b: see confidence intervals increasing as they move further from “now” indicator; furthermore, see differing prediction time window lengths 30 which necessarily affect confidence interval generation; Page 9: “The shortened display time interval may for example exclude predicted glucose values subsequent to a time distance after the expected time of the at least one predicted glucose level influencing event occurring, in particular subsequent to a time distance after a confidence (time) interval around the expected time. The confidence interval may be a p confidence interval with p being at least 70 %, preferably at least 85 %, more preferably at least 95 %. The shortened time interval may thus exclude, e.g., predicted glucose values impacted by meal consumption;” Pages 18-19: discussion of time window lengths dependent on received plurality of predicted glucose values and glucose influencing event data;).
Re. Claims 12 and 20: Galley as modified by Arunachalam teaches the invention according to claim 1. Galley further teaches the invention wherein each future glucose level of the range of future glucose levels comprises a predicted maximum value and a predicted minimum value (Figs. 3a, 3b: upper and lower bounds of confidence intervals; A confidence interval is a range of values that is likely to contain a true value).
Re. Claim 13: Galley as modified by Arunachalam teaches the invention according to claim 1. Galley further teaches the invention wherein the meal information comprises a user selection of one or more foods the user plans to eat (Page 7: “Determining the plurality of predicted glucose values may further be based on at least one of the following: meal event information, insulin bolus information, insulin basal amounts, physical activity event information, stress event information, illness event information. In particular, the predicted glucose values may be determined based on a recent or planned meal consumption (i.e. , carbohydrate intake)…”).
Claims 2-4, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Arunachalam et al. (US 20200098463 A1) (hereinafter – Arunachalam) in further view of
Mazlish et al. (US 20190274624 A1) (hereinafter – Mazlish).
Re. Claims 2-4 and 16: Galley as modified by Arunachalam teaches the invention according to claims 1 and 15. Galley teaches a software implemented solution which processes and analyzes glucose data (Page 2), thus implicitly teaching a glucose monitoring application, and also teaches particular display of data (Figs. 3a, 3b), thus teaching what can be considered an application which performs visualization of data, i.e., a “visualization application” as claimed. Galley does not disclose whether the visualization application is visually separate from a user interface of the glucose monitoring application.
Mazlish teaches analogous art in the technology of glucose monitoring (Abstract). Mazlish further teaches the invention wherein the visualization application is visually separate from a user interface of the glucose monitoring application (Figs. 5A-14B: visualization is visually separate from alarm messages and indicator screen providing data regarding glucose monitoring analysis).
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley as modified by Arunachalam to include the application user interfaces as taught by Mazlish, the motivation being that doing so allows a user to view glucose trend data as well as pertinent alarm messages and visual indicators as to when to take an interventive action (Paragraphs 0094, 0095).
Regarding claim 3, the incorporated application user interfaces of Mazlish also teaches a visualization application being a sub-module of the glucose monitoring application (a distinct sub-module of visualization program code is required to provide the visualization of blood glucose data and trend line 310).
Regarding claim 4, the incorporated application user interfaces of Mazlish also teaches wherein the visualization application is visually embedded in a user interface of the glucose monitoring application (Fig. 5B, 5C: trend line embedded within a user interface of an application which provides glucose monitoring capabilities and analysis).
Claim 16 recites limitations of claim 2 mutatis mutandis embodied in a method; thus, the rejection of claim 2 encompasses each limitation required by claim 16.
Re. Claim 14: Galley as modified by Arunachalam teaches the invention according to claim 1, but does not teach the invention wherein the visualization of the glucose data further comprises an interactive visual object configured to receive a user adjustment to the meal information, wherein the predicted range of future glucose levels is updated based on the user adjustment to the meal information.
Mazlish further teaches the invention wherein the visualization of the glucose data further comprises an interactive visual object configured to receive a user adjustment to the meal information (Fig. 5B, 5C; Paragraph 0010: “For example, in the case of a person with diabetes (PWD) using an insulin delivery system provided herein, the remote user-interface device can be configured to permit the PWD to enter a meal (e.g., enter an amount of carbohydrates consumed by the PWD). In some cases, a PWD can issue a command for the insulin delivery device to deliver a bolus of insulin for a meal;” Paragraph 0047: “In one or more methods, systems, or devices of the present disclosure, the alarm or alert condition can be a notice about… a possible missed meal announcement… and the audible, visual, haptic alarm or alert on the insulin delivery device can include the illumination of an icon or next to an icon indicating that the user should check the remote user-interface device for information about the alert;” Paragraph 0097: “The non-disruptive notifications, however, are things that a user can use to better manage or optimize their treatment of diabetes, but are not urgent, thus they can remain accessible upon demand by the user on the remote user-interface device. Such information might be data about past, current, or predicted blood glucose values that are in a safe range, past and current insulin doses and basal delivery rates, previously entered meal sizes, etc. Additionally, there is system information unrelated to the treatment of diabetes, which can be hidden from the user”).
It would have been obvious to one having skill in the art before the effective filing date to have modified the user interface of Galley as modified by Arunachalam as taught by Mazlish, the motivation being that doing so allows a user to address meal alarms while still observing glucose levels concurrently.
Galley as modified by Arunachalam and Mazlish further teaches the invention wherein the predicted range of future glucose levels is updated based on the user adjustment to the meal information (Examiner notes that the statistical model of Galley as modified by Arunachalam receives historical data which is updated as modified by Mazlish, and thus provides predicted ranges of future glucose levels based on such updated information).
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over:
Galley et al. (WO 2024200408 A1) (disclosed by Applicant) (hereinafter – Galley) in view of
Arunachalam et al. (US 20200098463 A1) (hereinafter – Arunachalam) in further view of
Kamath et al. (US 20210260289 A1) (hereinafter – Kamath).
Re. Claims 21 and 22: Galley as modified by Arunachalam teaches the invention according to claims 1 and 15, but do not teach the invention wherein the meal information comprises an amount of carbohydrates.
Kamath teaches analogous art in the technology of artificial machine learning applications for glucose maintenance (Abstract). Kamath further teaches that a suitable input for a machine learning algorithm may be user input of grams of carbohydrates in an upcoming meal (Paragraph 0028: “…the artificial pancreas algorithm determine and improve bolus insulin doses for a person over time based on user input of upcoming meals (e.g., grams of carbohydrates)…;”Paragraph 170: “As noted above and below, this amount of carbohydrates may be received via one or more user interfaces”).
It would have been obvious to one having skill in the art before the effective filing date to have modified Galley as modified by Arunachalam to include entering meal information comprising an amount of carbohydrate into a machine learning algorithm as taught by Kamath, the motivation being that doing so allows for an improved determination of bolus insulin doses for a person over time (Paragraph 0028) and provides greater specificity of data input into the machine learning model of Galley.
Conclusion
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.
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/JUSTIN XU/Primary Examiner, Art Unit 3791
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