DETAILED ACTION
This Office Action is in response to the communication dated 26 January 2026 concerning Application No. 18/412,037 filed on 12 January 2024.
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 .
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 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.
Status of Claims
Claims 1-15 and 17-22 are pending and currently under consideration for patentability; claims 1, 11, 14, 18, and 21 have been amended; claim 16 has been cancelled.
Response to Arguments
Applicant’s arguments dated 26 January 2026 have been fully considered, but they are not persuasive or moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant has amended the independent claims to recite comparing the glucose signal and the representative glucose level in order to evaluate the calibration of the medical system. The Examiner has addressed the amended limitations in the updated text of the rejection below.
Regarding claim 14, Applicant argues against Examiner's reliance on the Kim reference, as Kim describes noninvasive blood glucose sensor whereas Nguyen describes a noninvasive blood glucose sensor. As Kim is no longer relied upon for the teachings of claim 14, the Examiner respectfully submits that Applicant’s argument has been rendered moot.
Applicant’s e-Terminal Disclaimer dated 26 January 2026 has been approved, overcoming the rejections of claims 1-15 and 17-22 on the grounds of non-statutory double patenting.
Claim Objections
Claim 14 is objected to because of the following informality.
Claim 14 contains a minor typographical and/or grammatical error.
Claim 14, lines 1-2: Applicant is advised to change “the glucose sensor” to “a glucose sensor”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-14 and 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen (US 2015/0245780 A1) in view of Simpson et al. (US 2019/0335997 A1).
Regarding claims 1 and 21, Nguyen describes a medical system ([0014]) comprising
sensing circuitry configured to sense a cardiac characteristic of a patient and produce a current cardiac signal indicative of the cardiac characteristic ([0060])
communication circuitry operably connected to the sensing circuitry, wherein the communication circuitry is configured to communicate the current cardiac signal ([0069])
processing circuitry ([0033]) configured to
receive the current cardiac signal communicated by the communication circuitry ([0033])
receive a glucose signal indicative of a glucose level of a patient ([0033] - [0034], [0068])
associate the glucose signal to the current cardiac signal ([0068])
determine a representative glucose level of the patient using the current cardiac signal ([0034], [0055])
Regarding claims 1 and 21, Nguyen does not explicitly disclose wherein the processing circuitry is configured to compare the glucose signal and the representative glucose level and evaluate, based on the comparison, a calibration of the medical system. However, Simpson also describes a glucose sensor ([0004]), including processing circuitry configured to compare a glucose signal to a representative, or expected, glucose level and evaluate, based on the comparison, a calibration of the medical system ([0373]). As Simpson is also directed towards glucose monitoring and is in a similar field of endeavor, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to incorporate a calibration step similar to that described by Simpson when using the system described by Nguyen, as doing so advantageously allows the resulting system to derive more accurate glucose levels of the user.
Regarding claim 2, Nguyen describes wherein the sensing circuitry is supported by a housing of a device configured to contact a body of the patient (figure 6), and wherein at least some portion of the processing circuitry is supported by an external device separate from the device configured to contact the body of the patient ([0069] - [0072], signals may be transmitted to a monitoring system, data may be logged and managed on a PC).
Regarding claim 3, Nguyen describes wherein the sensing circuitry includes one or more electrodes configured to contact the body of the patient, wherein the one or more electrodes are configured to sense the cardiac characteristic ([0060]).
Regarding claim 4, Simpson describes wherein the external device is a server ([0362] - [0363]).
Regarding claim 5, Nguyen describes wherein the external device includes a storage device configured to store data indicative of the representative glucose level ([0072], a PC which continuously logs the relevant data using a data management system), and the system further comprises one or more devices configured to display the data, wherein the external device is configured to provide the data to the one or more devices ([0069] - [0070], the output maybe provided in any appropriate format including a visual output).
Regarding claims 6 and 22, Nguyen describes wherein the external device includes a storage device configured to store an input vector indicative of the current cardiac signal ([0062], the neural network contains one input layer; figure 3, the extracted components of the current cardiac signal are input into the computational intelligent system) and an output vector indicative of the representative glucose level ([0062], the neural network contains one output layer, the response of the neural network will increasingly approximate the blood glucose status given by the available qualitative data; figure 3, the computational intelligent system provides the output).
Regarding claim 7, Nguyen describes wherein the processing circuitry is configured to receive the current cardiac signal from a network configured to receive the current cardiac signal communicated by the communication circuitry and communicate with the processing circuitry ([0069], figure 3; details of network provided in [0061] - [0062]).
Regarding claim 9, Nguyen describes a user interface including a visual display, wherein the visual display is configured to present information indicative of the relative glucose level ([0070] - [0072]).
Regarding claim 8, Simpson describes wherein the communication circuitry is configured to communicate the current cardiac signal to an access point, and wherein the access point is configured to communicate the current cardiac signal to the network ([0359], output module 178 acts as an access point).
Regarding claim 10, Nguyen describes wherein the processing circuitry is configured to map an input vector indicative of the current cardiac signal to an output vector indicative of the representative glucose level ([0062]).
Regarding claim 11, Simpson describes wherein the processing circuitry is configured to calibrate the medical system ([0373]), and Nguyen describes wherein the processing circuitry is configured to formulate one or more training data sets using the cardiac signal and the glucose signal associated with the cardiac signal ([0068]) and determine a subsequent representative glucose level of the patient using a machine learning algorithm trained using the one or more training data sets ([0062], [0068]).
Regarding claim 12, Nguyen describes wherein the processing circuitry is configured to receive the cardiac signal from the sensing circuitry ([0033], [0069]).
Regarding claim 13, Nguyen describes wherein the processing circuitry is configured to formulate a training input vector and a training output vector, wherein the training input vector is representative of the cardiac signal and the training output vector is representative of the glucose signal associated with the cardiac signal, and wherein the one or more training sets include the training input vector and the training output vector ([0062] describes the use of the input layer and output layer, which would contain the input and output vectors; [0064] - [0068] describe the use of inputs (cardiac characteristics) and outputs (glucose status) to train the model).
Regarding claim 14, Simpson describes a glucose sensor configured to determine a glucose signal indicative of a glucose level of the patient, wherein processing circuitry is configured to receive the glucose signal from the glucose sensor ([0062], [0335]).
Regarding claim 17, Nguyen describes wherein the communication circuitry is configured to wirelessly communicate the current cardiac signal ([0069], radio frequency communication, which is wireless).
Regarding claim 18, Nguyen describes a medical system ([0014]) comprising
sensing circuitry ([0060]) supported by a housing of a device configured to contact a body of a patient (figure 6), wherein the sensing circuitry is configured to sense a cardiac characteristic of the patient and produce a current cardiac signal indicative of the cardiac characteristic ([0060])
communication circuitry operably connected to the sensing circuitry, wherein the communication circuitry is configured to communicate the current cardiac signal ([0069])
processing circuitry ([0033]) supported by an external device ([0070] - [0072]), wherein the processing circuitry is configured to
receive the current cardiac signal communicated by the communication circuitry ([0033])
receive a glucose signal indicative of a glucose level of a patient ([0033] - [0034], [0068])
associate the glucose signal to the current cardiac signal ([0068])
determine a representative glucose level of the patient using the current cardiac signal ([0034], [0055])
a network configured to communicate with the processing circuitry, wherein the network is configured to receive the current cardiac signal communicated by the communication circuitry, and wherein the processing circuitry is configured to receive the current cardiac signal from the network ([0069] - [0072], [0078])
Regarding claim 18, Nguyen does not explicitly disclose wherein the processing circuitry is configured to compare the glucose signal and the representative glucose level and evaluate, based on the comparison, a calibration of the medical system. However, Simpson also describes a glucose sensor ([0004]), including processing circuitry configured to compare a glucose signal to a representative, or expected, glucose level and evaluate, based on the comparison, a calibration of the medical system ([0373]). As Simpson is also directed towards glucose monitoring and is in a similar field of endeavor, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to incorporate a calibration step similar to that described by Simpson when using the system described by Nguyen, as doing so advantageously allows the resulting system to derive more accurate glucose levels of the user.
Regarding claim 19, Nguyen describes wherein the processing circuitry is configured to map an input vector indicative of the current cardiac signal to an output vector indicative of the representative glucose level using a machine learning algorithm ([0062]).
Regarding claim 20, Nguyen describes wherein the external device includes a storage device configured to store an input vector indicative of the current cardiac signal ([0062], the neural network contains one input layer; figure 3, the extracted components of the current cardiac signal are input into the computational intelligent system) and an output vector indicative of the representative glucose level ([0062], the neural network contains one output layer, the response of the neural network will increasingly approximate the blood glucose status given by the available qualitative data; figure 3, the computational intelligent system provides the output).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen in view of Simpson, further in view of Kim et al. (US 2016/0367138 A1).
Regarding claim 15, Nguyen in view of Simpson suggests the system of claim 14, but neither Nguyen nor Simpson explicitly disclose wherein the glucose sensor is configured to provide the glucose signal to the processing circuitry in an activated configuration and not provide the glucose signal to the processing circuitry in a deactivated configuration, and wherein the processing circuitry is configured to cause the glucose sensor to establish the activated configuration or the deactivated configuration. However, Kim also describes the method of operation of a glucose sensor, including wherein the glucose sensor is configured to provide the glucose signal to the processing circuitry in an activated configuration and not provide the glucose signal to the processing circuitry in a deactivated configuration ([0080], [0091]), and wherein the processing circuitry is configured to cause the glucose sensor to establish the activated configuration or the deactivated configuration ([0073], [0093] - [0096]). As Kim is also directed towards glucose sensing and is in a similar field of endeavor, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to incorporate activated and deactivated configurations similar to those described by Kim when using the system described by Nguyen and Simpson, as doing so advantageously allows the resulting system to conserve power and only transmit data when a faithful connection has been established.
Statement on Communication via Internet
Communications via Internet e-mail are at the discretion of the applicant. Without a written authorization by applicant in place, the USPTO will not respond via Internet e-mail to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. 122. Where a written authorization is given by the applicant, communications via Internet e-mail, other than those under 35 U.S.C. 132 or which otherwise require a signature, may be used. USPTO employees are NOT permitted to initiate communications with applicants via Internet e-mail unless there is a written authorization of record in the patent application by the applicant. The following is a sample authorization form which may be used by applicant:
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please refer to MPEP 502.03 for guidance on Communications via Internet.
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Ankit D. Tejani, whose telephone number is 571-272-5140. The Examiner may normally be reached on Monday through Friday, 8:30AM through 5:00PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Carl Layno, can be reached by telephone at 571-272-4949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (in USA or Canada) or 571-272-1000.
/Ankit D Tejani/
Primary Examiner, Art Unit 3792