DETAILED ACTION
This action is filed in response to the amendment filed on 3/30/2026.
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 Arguments
Applicant’s arguments with respect to claims 16-31, and 33-38 have been considered but are moot because of the new grounds of rejection entered in response to Applicant’s amendments.
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
Examiner notes the amended claims filed 3/30/2026 skip from claim 31 to claim 33. Subsequently:
The claim numbered as Claim 33 in the amended claims filed 3/30/2026 should be Claim 32.
The claim numbered as Claim 34 in the amended claims filed 3/30/2026 should be Claim 33.
The claim numbered as Claim 35 in the amended claims filed 3/30/2026 should be Claim 34.
The claim numbered as Claim 36 in the amended claims filed 3/30/2026 should be Claim 35.
The claim numbered as Claim 37 in the amended claims filed 3/30/2026 should be Claim 36.
The claim numbered as Claim 38 in the amended claims filed 3/30/2026 should be Claim 37.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 34 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Examiner notes Claim 34 is disclosed as being dependent on Claim 32, however, in the amended claim set there is no Claim 32. Therefore the meets and bounds of Claim 34 are unclear rendering the claim indefinite. Examiner notes once the numbering is corrected as described above, this rejection will be withdrawn.
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 16-22, 29-30, and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Schabbach (US20130197445 A1) and in further view of Szechinski (US20120233834 A1) and Sjöstedt (US20180197634 A1).
Regarding Claims 16 and 30, Tamtoro teaches a system for evaluating if a batch of medicament delivery devices should be recalled (e.g. see [0002] “systems and methods for use with drug delivery devices and relating to the processing of sensor data collected by the drug delivery devices to determine their condition and/or operational state”), the system comprising:
a batch of medicament delivery devices for self-administration of a medicament (e.g. see [0003] “drugs can be administered through the use of drug delivery devices, such as autoinjectors or on-body injectors or infusers”), wherein each medicament delivery device comprises:
a plurality of device components for delivering a medicament (e.g. see [0008] “The drug delivery device may include a reservoir, a delivery cannula having a proximal end in fluid communication with the reservoir and a distal end to be received within a patient, one or more sensors configured to generate sensor data representative of at least one of a condition or an operational state of the drug delivery device, and a first communication module coupled to the one or more sensors and configured to transmit the sensor data”)
a device processor (e.g. see [0095] “The controller 350 (i.e. device controller located within the device) may include at least one processor 370”);
a device memory comprising batch data related to the batch (e.g. see [0096] “The memory 372 may store the identity information discussed above. The identity information may be stored in the memory 372 prior to the start of execution of any of the methods discussed above. The identity information may include, by way of example and not by way of limitation, a unique identifier, the name of the drug, the dosage, an expiration date, and information regarding the identity of the patient for whom the drug was prescribed”);
a device transceiver (e.g. see [0108] “the local computing device 304 may further include a communication module 430 for wireless communication with the communication module 352 of the drug delivery device 302”); and
a sensor (e.g. see [0008] “The drug delivery device may include a reservoir, a delivery cannula having a proximal end in fluid communication with the reservoir and a distal end to be received within a patient, one or more sensors configured to generate sensor data representative of at least one of a condition or an operational state of the drug delivery device”);
wherein each medicament delivery device is configured to use the sensor to generate sensor data related to an actual value for a parameter from an interaction between some or all of the device components during use of the medicament delivery device (e.g. see [0008] “one or more sensors configured to generate sensor data representative of at least one of a condition or an operational state of the drug delivery device,” and [0027] “the system or method may determine if plunger has been moved from a first end of a bore (defining drug reservoir) to a second end of the ore to determine if the drug delivery device is in the ‘drug delivery complete’ state”)
to generate time data related to a time point of an activation of the medicament delivery device (e.g. see [0065] “the drug delivery system or the one or more computing devices may use the information to make a determination of the time of day (or week, month, etc.) that the patient usually takes their medication. This determination may be based, in part, on the patient record in which time information is associated with operational state information, such as relates to the triggering of the drug delivery device or the completion of the drug delivery”), and to store the sensor data and time data in the device memory (e.g. see [0070] “The determination may also be based in part on condition state information, such as the temperature, shock/vibration exposure, light exposure or color and/or turbidity of the drug, whether determined at a particular time or over a period of time (i.e., a history as established in the drug delivery device record or the drug record),” and [0043] “In this regard, the one or more computing devices adapted or programmed to carry out the method 200 may perform the actions of retrieving the one or more records from storage in one or more memory storage devices, writing the information received from the drug delivery device into the one or more records, and then storing the one or more records in the one or more memory storage devices”),
wherein the sensor data is related to performance of the device when the device is activated and/or in use such that one or more performance reductions can be detected and/or predicted (e.g. see [0010] “method may include: (a) collecting sensor data with the drug delivery device; (b) transmitting the sensor data from the drug delivery device with the first communication module; (c) receiving the sensor data from the drug delivery device at the external computing device with the second communication module; and (d) determining, with the external computing device, at least one of a condition or an operational state of the drug delivery device based on the sensor data”); and
a remote computer (e.g. see [0080] “Fig. 3 illustrates an embodiment of a system 300 including a drug delivery device 302, a local computing device 304 and a remote computing device 306”) comprising:
a remote processor (e.g. see [0106] “the remote computing device 306 may be in the form of at least one computing device including at least one processor 424”);
a remote transceiver (e.g. see [0078] “the systems and methods illustrated in Figs. 3-14 may have an associated controller (which controller may include a processor and memory) that is in communication with a first communication module (which may be a transmitter, a transmitter and receiver pair, or a transceiver)”); and
a remote memory (e.g. see [0106] “Likewise, the remote computing device 306 may be in the form of at least one computing device including at least one processor 424 (e.g., microprocessor) and memory 426 (e.g., a random access memory (RAM), a non-volatile memory such as a hard disk, a flash memory, a removable memory, a non-removable memory, etc.)”);
containing regular reference data related to a regular reference value for a parameter from a regular interaction between some or all of the device components of a comparable medicament delivery device (e.g. see [0131] “In some embodiments, the evaluation of the temperature history may be performed by the controller 423 of the local computing device 304 by comparing temperature history received from the drug delivery device 302 with information stored in the memory 422 of the local computing device 304 to determine if the temperature history is acceptable for a patient to use the drug delivery device 302”);
wherein each medicament delivery device is configured to establish a connection to the remote computer over a communication network when the medicament delivery device is activated (e.g. see [0128] “With reference now to Fig. 13, a method 800 carried out by the local computing device 304 starts at block 802 with the receipt by the local computing device 304 (or more particularly, the communication module 430) of the communication transmitted by the drug delivery device 302,” and [0126] “It should be noted that, in an alternative embodiment, some or all of the actions performed by the local computing device 304 with regard to Fig. 13 may be performed by the remote computing device 306,” Examiner notes the method described in Fig. 13 and [0128] teaches the delivery device establishing a connection with the computer, and [0126] teaches that the computer can be the remote computer);
wherein each medicament delivery device is configured to transfer the batch data, the sensor data and the time data to the remote computer via the connection (e.g. see [0010] “the method may include: (a) collecting sensor data with the drug delivery device; (b) transmitting the sensor data from the drug delivery device with the first communication module; (c) receiving the sensor data from the drug delivery device at the external computing device with the second communication module,” and [0044-0045] “system used by the individual patient that is stored in a drug delivery system database. The drug delivery system record may be used to store information regarding the drug delivery system throughout the life of the drug delivery system. The drug delivery system record may be accessed by the drug delivery system manufacturer or the drug provider for quality control purposes…There may also be record for drug used in the drug delivery system that is stored in a drug database. This record may be used in a similar fashion to the drug delivery system record for quality control purposes…This action may require not only the information received in the report and/or stored previously in the record updated at block 204, but may require additional information such as from other patient records, drug system delivery records and/or drug records (i.e. batch data and time data). If this is the case, the determination may be made at block 208 that these other records need to be accessed, and the information retrieved at block 210”);
wherein the remote computer is configured to create a batch data set containing the batch data, the sensor data and the time data for the batch, and to store the batch data set in the remote memory [0043] “At block 204, the report received from the drug delivery system is used to update one or more records. In this regard, the one or more computing devices adapted or programmed to carry out the method 200 may perform the actions of retrieving the one or more records from storage in one or more memory storage devices, writing the information received from the drug delivery device into the one or more records”);.
wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory and to determine, based on the evaluation, if a batch of medicament delivery devices should be recalled (e.g. see [0063] “The determination may be based in part on operational state information, in comparison with information that may be collected and stored regarding conventional norms in operation. Alternatively or additionally, a comparison between the determined, reported or received operational states may permit a determination to be made that the injection was not performed correctly. For example, the determination, reporting or receipt of operational state information indicating that the drug delivery is complete without operation state information indicating that device was triggered, that the device was applied to the patient, and/or that the cannula was inserted may indicate that the drug delivery device has failed to perform correctly, is faulty or was operated incorrectly”).
Tamtoro does not explicitly disclose two acoustic sensors and one of those sensors configured to measure a sound between a signal generating element and a housing. In the same field of endeavor, Schabbach teaches wherein the sensor comprises a group of sensors comprising:
a first acoustic sensor configured to measure sound between a signal generating element and a housing(e.g. see [0053] “According to an embodiment of the present invention, the one or more acoustical sensors may comprise at least one acoustical sensor configured to capture a Sound produced when the medical device is used. The sound may for instance be produced by the medical device (or part thereof) mechanically, for instance when components of the medical device are moved with respect to each other (e.g. a clicking Sound)”); and
a second acoustic sensor (e.g. see [0053] “According to an embodiment of the present invention, the one or more acoustical sensors”) configured to measure sound between a plunger rod and a stopper (e.g. see [0114] “A click Sound caused by injection device 1 when the ejection (which in the exemplary case of the medical device being an injection pen coincides with the injection) is performed may also be sensed by an acoustical sensor of Supplementary device 3 and may serve as an acknowledgement that the recognized dose has actually been ejected/injected”) and [0026] “An acoustical sensor is configured to acoustically determine information related to a condition and/or use of the medical device. Non-limiting examples of an acoustical sensor are a microphone (with or without sound differentiation/ recognition capability), e.g. for capturing one or more sounds caused during the use of the medical device”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the acoustic sensor embodiment of Tamtoro with the multiple acoustic sensors of Schabbach for the purpose of recording sounds made by the injector with the advantage of additional data to indicate the efficacy of the device.
While Tamtoro teaches a plurality of sensors, Tamtoro as modified by Schabbach does not explicitly disclose a force sensor configured to measure a friction force between a holding and a release element and a plunger rod. In the same field of endeavor, Szechinski teaches a force sensor configured to measure a friction force between a holding and a release element and a plunger rod (e.g. see [0352] “the force detected by the force sensor 1616 may be the frictional force with which the friction point in the proximal cap of the automatic injection device resists the insertion of different structural features on the syringe and/or the rigid needle shield during the syringe insertion process. An exemplary force sensor 1616 may include, but is not limited to, a direct piezoelectric load cell manufactured by the Kistler Group.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the multiple sensor embodiment of Tamtoro with the force sensor of Szechinski for the purpose of determining the state of the medicament injector, with the advantage of additional data to ensure the accuracy of any determination.
Tamtoro does not explicitly disclose wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory based on machine learning. In the same field of endeavor, Sjöstedt teaches disclose wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory based on machine learning(e.g. see [0026-0027] “said remote computer and portable medical device adapted to establish a data connection with the use of a cellular network, the portable medical device being configured to use the data connection to transfer the sensor data to the remote computer, the remote computer configured to use the sensor data to determine whether the portable medical device is in need of scrapping, service or replacement. The remote computer of the system may have decision rule software, said decision rule software being generated by applying machine learning to a data set, said data set comprising sensor data previously collected from comparable portable medical devices, said data set further comprising data about any previous failures of such comparable portable medical devices”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the remote computer data evaluation of Tamtoro with the machine learning embodiment of Sjöstedt for the purpose of determining the operating status of the medication delivery device with the advantage of a computerized comparison method to increase accuracy and efficiency.
Regarding Claim 17, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro does not explicitly disclose wherein the machine learning is configured to use the regular reference data from a plurality of comparable medicament delivery devices as data to be compared with the sensor data.
In the same field of endeavor, Sjöstedt teaches wherein the machine learning is configured to use the regular reference data from a plurality of comparable medicament delivery devices as data to be compared with the sensor data (e.g. see [0027] “the remote computer of the system may have decision rule software, said decision rule software being generated by applying machine learning to a data set, said data set comprising sensor data previously collected from comparable portable medical devices”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the remote computer data evaluation of Tamtoro with the machine learning and multiple device embodiment of Sjöstedt for the purpose of determining the operating status of the medication delivery device with the advantage of a computerized comparison method to increase accuracy and efficiency.
Regarding Claim 18, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses, wherein each medicament delivery device comprises a GPS configured to determine a geographical position of the medicament delivery device when the medicament delivery device is activated (e.g. see [0094] “The sensors 360, 362, 364, and 365 may each generate sensor data (e.g., raw or unprocessed data) related to a respective measured property or aspect of the drug delivery device 302. The sensor data may be representative of at least one of a condition or operational state of the drug delivery device 302,” and [0050-0051] “For example, a non-limiting matrix of state and identity information may include the following: Condition State Information:
Temperature
Shock or vibration exposure
Light exposure
Color and/or turbidity (as relates to the drug)
Orientation
Geographic position
Temporal information”), and
wherein each medicament delivery device is configured to store the geographical position in the device memory (e.g. see [0096] “The memory 372 may store the identity information discussed above. The identity information may be stored in the memory 372 prior to the start of execution of any of the methods discussed above. The identity information may include, by way of
example and not by way of limitation, a unique identifier, the name of the drug, the
dosage, an expiration date, and information regarding the identity of the patient for whom
the drug was prescribed”).
Regarding Claim 19, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro does not explicitly disclose wherein the remote memory contains irregular reference data related to an irregular reference value for a parameter from an irregular interaction between some or all of device components of a comparable medicament delivery device, and wherein the remote computer is configured to evaluate the sensor data by comparison with the irregular reference data.
In the same field of endeavor, Sjöstedt teaches wherein the remote memory contains irregular reference data related to an irregular reference value for a parameter from an irregular interaction between some or all of device components of a comparable medicament delivery device (e.g. see [0102] “importantly, the dataset 15 comprises information about previous failures of medical devices 1 or similar medical devices. This data may be entered manually. Alternatively the dataset 15 may automatically receive error codes from the medical devices 1 or comparable medical devices. The size of the dataset 15 will increase over time, which will improve the decision rule”) and wherein the remote computer is configured to evaluate the sensor data by comparison with the irregular reference data (e.g. see [0007] “d) the remote computer using the value from step c) to determine whether the portable medical device is in need of scrapping, service or replacement,” and [0020] “Step d) of the method may comprise the step of the remote computer applying a decision rule, said decision rule being generated by applying machine learning to a data set, said data set comprising data previously collected from comparable portable medical devices, said data set also comprising data about previous failure or error codes of such similar portable medical devices”).
It would have been obvious to one of ordinary skills in the art, before the effective filling date, to combine the data comparisons of Tamtoro with the irregular data embodiment of Sjöstedt for the purpose of evaluating a batch of medicament delivery devices with the advantage of targeting and quickly locating data irregularities in order to efficiently resolve any device failures.
Regarding Claim 20, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein at least one parameter may be indicative of a friction and/or a pressure force (e.g. see [0099] “According to one embodiment, the skin sensor 362 is a pressure sensor. According to other embodiments, the skin sensor 362 may be a capacitance sensor, resistance sensor, or inductance sensor. The skin sensor 362 or the switch 366 (which is attached to or associated with the actuator 340) may be used to determine when the drug delivery device 302 is activated or actuated”) and/or a click sound and/or another specific sound from the interaction between at least two device components and/or a displacement and/or a time of displacement of one device component in relation to another device component.
Regarding Claim 21, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the sensor is at least one of a group of sensors which comprises a force sensor, an optical sensor, an acoustic sensor, a position sensor, a pressure sensor and a friction sensor (e.g. see [0099] “According to one embodiment, the skin sensor 362 is a pressure sensor”).
Regarding Claim 22, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises an activation element and a holding and release element (e.g. see [0089] “According to other embodiments, such as the one illustrated in Fig. 3, the actuator 340 (i.e. an activation element) may be a button that may be manually depressed by the user or patient once the drug delivery device 302 is placed disposed on or against the patient's skin. A lock 341 (i.e. a holding and releasing element) may be coupled to the actuator 340 and configured to limit or prevent movement of the actuator 340 so that the actuator 340 cannot be used to activate the drive 330”), and wherein the parameter is indicative of a friction force between the activation element and the holding and release element (e.g. see [0089] “In some embodiments, the lock 341 may be coupled to a controller (e.g., controller 350 described in more detail below) which can selectively activate or deactivate the lock 341 based on different types of information regarding the drug delivery device 302, including operational state information, condition information, and/or identity information, in accordance with one or more of the methods described above,” and [0099] “According to one embodiment, the skin sensor 362 is a pressure sensor. According to other embodiments, the skin sensor 362 may be a capacitance sensor, resistance sensor or inductance sensor. The skin sensor 362 or the switch 366 (which is attached to or associated with the actuator 340) may be used to determine when the drug delivery device 302 is activated,” Examiner notes the cited sections teach determining a parameter of whether the device has been activated based on pressure, which is indicative of the force used on the activation element (button) needed to release the medication from the holding and releasing element (lock)).
Regarding Claim 29, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device is a pen injector, an auto-injector, an on-body device, a pump, or an inhaler (e.g. see [0029] “Considering the foregoing description of the drug delivery device, the device may be characterized as an autoinjector or an on-body injector or infuser (the reference to injector intended to include also a reference to an infuser, to the extent that a difference is suggested)”).
Regarding Claim 37, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro does not explicitly disclose wherein the machine learning finds one or more parameter values associated with a device component to be unacceptable, wherein the machine learning involves the application of one or more algorithms to detect patterns within the batch data set, wherein the one or more algorithms comprise one or more mathematical models based on regular reference data.
In the same field of endeavor, Sjöstedt teaches wherein the machine learning finds one or more parameter values associated with a device component to be unacceptable (e.g. see [0019-0020] “Step d) of the method comprise the use of a decision rule. Step d) of the method may comprise the step of the remote computer establishing if the value exceeds a threshold value. This is simple and convenient way of monitoring a medical device. [0020] Step d) of the method may comprise the step of the remote computer applying a decision rule, said decision rule being generated by applying machine learning to a data set, said data set comprising data previously collected from comparable portable medical devices, said data set also comprising data about previous failure or error codes of such similar portable medical devices.”), wherein the machine learning involves the application of one or more algorithms to detect patterns within the batch data set, wherein the one or more algorithms comprise one or more mathematical models based on regular reference data (e.g. see [0012] “The data set can be used to build knowledge about usage patterns and sensor data in its relation to malfunctioning of devices. The data set may be used to improve decision rules regarding service or replacement of the medical device. Machine learning or data mining technologies may be used for this purpose”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the remote computer data evaluation of Tamtoro with the machine learning embodiment of Sjöstedt for the purpose of determining the operating status of the medication delivery device with the advantage of a computerized comparison method to increase accuracy and efficiency.
Regarding Claim 38, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 37. Tamtoro does not explicitly disclose wherein the one or more algorithms further comprise one or more mathematical models based on irregular reference data. In the same field of endeavor, Sjöstedt teaches wherein the one or more algorithms further comprise one or more mathematical models based on irregular reference data (e.g. see [0100] “Machine learning, as such, is previously known. Machine learning may for example involve applying Bayesian networks or artificial neural networks. Guidance can be found in US2014/0012784, US2008/0059120 and U.S. Pat. No. 8,819,498. The remote computer 4 may comprise machine learning software 16 that analyses the data set 15 to produce a decision rule that is implemented in the decision rule software 10. The data set 15 may comprise the information in the unit database 11”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the remote computer data evaluation of Tamtoro with the machine learning embodiment of Sjöstedt for the purpose of determining the operating status of the medication delivery device with the advantage of a computerized comparison method to increase accuracy and efficiency.
Claims 23 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Schabbach (US20130197445 A1) and in further view of Szechinski (US20120233834 A1) Sjöstedt (US20180197634 A1), and Jakobsen (WO 2020016313 A1).
Regarding Claim 23, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises a holding and release element and a driven element (e.g. see [0089] “A lock 341 may be coupled to the actuator 340 and configured to limit or prevent movement of the actuator 340 so that the actuator 340 cannot be used to activate the drive 330”).
Tamtoro does not explicitly disclose wherein the parameter is indicative of a friction force between the holding and release element and the driven element. In the same field of endeavor, Jakobsen teaches wherein the parameter is indicative of a friction force between the holding and release element and the driven element. (e.g. see [pg. 3 lines 19-20] “a retaining element of the power base releasably engages the retaining geometry of the plunger to retain the plunger against the force of the drive spring,” and [pg. 5 lines 25-27] “In other embodiments, the sensor of the electronic module comprises one or more of the sensors selected from the group consisting of an optical sensor, a force sensor, a magnetic sensor, an inductive sensor and an electrical conductive sensor” Examiner notes the prior art teaches a retaining element that exerts a force on the plunger, this force is indicative of friction because, as well known in the art resistance is indicative of static friction. Furthermore the prior art teaches a force sensor to measure these forces).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the auto-injector elements of Tamtoro with the friction force parameter of Jakobsen for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Regarding Claim 25, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises an energy accumulating element and a driven element (e.g. see [0085] “According to still other embodiments, the drive 330 may include an electromechanical system, such as may include a motor for example, although such an electromechanical system may be more appropriate for the on- body autoinjector or infuser described above. Other embodiments of the drive 330 are also possible. [0086] In one embodiment, the drive 330 may be coupled to a plunger 331”).
Tamtoro does not explicitly disclose wherein the parameter is indicative of a force between the energy accumulating element and a driven element. In the same field of endeavor, Jakobsen teaches (e.g. see [pg. 7 lines 11-12] “an energy source (i.e. an energy accumulating element) coupled to the plunger and providing a force on the plunger (i.e. the driven element) in a distal direction” and [pg. 5 lines 25-27] “In other embodiments, the sensor of the electronic module comprises one or more of the sensors selected from the group consisting of an optical sensor, a force sensor, a magnetic sensor, an inductive sensor and an electrical conductive sensor” Examiner notes the prior art teaches the parameter of the force between the activation element and the energy accumulating element as well as a force sensor that can be used to measure the force).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the auto-injector elements of Tamtoro with the force parameter of Jakobsen for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Schabbach (US20130197445 A1) and in further view of Szechinski (US20120233834 A1) Sjöstedt (US20180197634 A1), and Jazayeri (WO 2017209899 A1).
Regarding Claim 24 Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises an activation element and a resilient element.
Tamtoro does not explicitly disclose wherein the parameter is indicative of a force between the activation element and the resilient element. In the same field of endeavor, Jazayeri teaches wherein the parameter is indicative of a force between the activation element and the resilient element (e.g. see [0050] “energy source 222 may additionally include an actuator 288 (e.g., a button) which can be pressed or otherwise manually displaced by an operator to release the spring 282 (i.e. the resilient element), thereby activating the energy source 222. In alternative embodiments, an electromechanical component may be substituted for the actuator 288, such that activation of the energy source 222 can be controlled via a computer (e.g., the computing unit 240). Upon release, the spring 282 may expand axially, thereby pushing the piston member 286 as well as the impactor 220 connected to the piston member 286 in the distal direction,” and [0038] “Further, the impact testing apparatus 200 is capable of measuring and evaluating one or more characteristics of each impact event including, but not limited to, force, pressure, and/or velocity,” and [0058] “In general, the monitoring system 230 is configured to measure and analyze various characteristics (e.g., physical properties) of the first and second impacts. As mentioned above, the monitoring system may include various sensors in data communication with the computing unit 240” Examiner notes the prior art teaches two forms of activation elements (i.e. a button or computer control) that exerts a force on the resilient element (i.e. a spring) to push it, the push is a force exerted on the spring and [0038] and [0058] teach utilizing sensors to measure the physical properties (i.e. force) related to the functioning of the apparatus).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the auto-injector elements of Tamtoro with the force parameter of Jazayeri for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Claim 26, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Schabbach (US20130197445 A1) and in further view of Szechinski (US20120233834 A1) Sjöstedt (US20180197634 A1), and Dahmani (WO 2018106475 A1).
Regarding Claim 26, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises a signal generating element (e.g. see [0116] “the drug delivery device 302 may include a noise-making device”) and a housing (e.g. see [0102] “According to this embodiment, the drug delivery device 302 may include the housing 310”) and wherein the parameter is indicative of a click sound from an interaction between the signal generating element and the housing (e.g. see [0116] “the drug delivery device 302 may include a noise-making device, such as a ratchet or clicker, that actuates when the drug delivery is complete”).
Tamtoro does not explicitly disclose wherein the click sound is measured by a sensor of the medicament delivery device which generates corresponding data.
In the same field of endeavor, Schabbach teaches wherein the parameter is indicative of a click sound from an interaction between the signal generating element and the housing, wherein the click sound is measured by a sensor of the medicament delivery device which generates corresponding data ((e.g. see [0053] “According to an embodiment of the present invention, the one or more acoustical sensors may comprise at least one acoustical sensor configured to capture a Sound produced when the medical device is used. The sound may for instance be produced by the medical device (or part thereof) mechanically, for instance when components of the medical device are moved with respect to each other (e.g. a clicking Sound)”).
Also in the same field of endeavor, Dahmani teaches wherein the parameter is indicative of a click sound from an interaction between the signal generating element and the housing, wherein the click sound is measured by a sensor of the medicament delivery device which generates corresponding data (e.g. see [0047] “In some embodiments, a vibration or acoustic sensor may be used to listen or sense when mechanical aspects of the drug delivery device have actuated to deliver the medicament stored therein. For example, the mechanical aspects of the drug delivery device may be configured to click or vibrate when a dosing event occurs”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the click sound of Tamtoro with the acoustic sensor and click sound of Schabbach and Dahmani for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Regarding Claim 28, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16.Tamtoro further discloses wherein the medicament delivery device comprises a driven member and a housing (e.g. see [0086] “the drive 330 may be coupled to a plunger 331 and/or a stopper 332 (e.g., a wall) disposed in the reservoir 312 to move that stopper 332 in a distal direction toward the delivery cannula 314”).
While Tamtoro teaches movement of the driven element (plunger) in relation to the housing (e.g. see [0088] “When the lock 335 is deactivated by the controller 350, the lock 328 may be configured to allow movement of the plunger 331 relative to the housing 310”) Tamtoro does not explicitly disclose wherein the parameter is indicative of a movement of the driven member in relation to the housing. In the same field of endeavor, Dahmani teaches wherein the parameter is indicative of a movement of the driven member in relation to the housing (e.g. see [0050]“In one embodiment, the performance sensors 140 further include a completion sensor 144 for detecting when the drive mechanism 120 reaches its most distal position (i.e. indicative of movement) upon completion of a dosing event”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the auto-injector elements of Tamtoro with the movement parameter of Dahmani for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Schabbach (US20130197445 A1) and in further view of Szechinski (US20120233834 A1) Sjöstedt (US20180197634 A1), and Ursina (CH 713114 A2).
Regarding Claim 27, Tamtoro, Schabbach, Szechinski, and Sjöstedt teach the limitations of Claim 16. Tamtoro further discloses wherein the medicament delivery device comprises a driven element and a medicament container having a stopper (e.g. see [0086] “the drive 330 may be coupled to a plunger 331 and/or a stopper 332 (e.g., a wall) disposed in the reservoir 312 to move that stopper 332 in a distal direction toward the delivery cannula 314”).
Tamtoro does not explicitly disclose wherein the parameter is indicative of a click sound from an interaction between the driven member and the stopper. In the same field of endeavor, Schabbach teaches teaches wherein the parameter is indicative of a click sound from an interaction between the driven member and the stopper ((e.g. see [0053] “According to an embodiment of the present invention, the one or more acoustical sensors may comprise at least one acoustical sensor configured to capture a Sound produced when the medical device is used. The sound may for instance be produced by the medical device (or part thereof) mechanically, for instance when components of the medical device are moved with respect to each other (e.g. a clicking Sound)”).
Also in the same field of endeavor, Ursina also teaches wherein the parameter is indicative of a click sound from an interaction between the driven member and the stopper (e.g. see [pg. 4 paragraph 2] “The signaling preferably comprises an unbraked acceleration phase of the stop element and a subsequent stop or impact of the stop element to excite vibration in the injection device, which can primarily be perceived by the user as an acoustic or tactile click signal and can also be measured by a suitably placed axial force sensor” and [pg. 4 paragraph 4] “In an alternative further development of the advantageous variant, the additional module has a microphone or acoustic sensor for measuring an acoustic signal during the injection process”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the click sound of Tamtoro with the acoustic sensor and click sound of Schabbach and Ursina for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Claims 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Sjöstedt (US20180197634 A1) and in further view of Dahmani (WO 2018106475 A1).
Regarding Claim 34, Tamtoro and Sjöstedt teach the limitations of Claim 31. Tamtoro further discloses wherein the medicament delivery device comprises a driven member and a housing (e.g. see [0086] “the drive 330 may be coupled to a plunger 331 and/or a stopper 332 (e.g., a wall) disposed in the reservoir 312 to move that stopper 332 in a distal direction toward the delivery cannula 314”). While Tamtoro teaches movement of the driven element (plunger) in relation to the housing (e.g. see [0088] “When the lock 335 is deactivated by the controller 350, the lock 328 may be configured to allow movement of the plunger 331 relative to the housing 310”) Tamtoro does not explicitly disclose wherein the parameter further comprises a movement of the driven member in relation to the housing. In the same field of endeavor, Dahmani teaches wherein the parameter further comprises a movement of the driven member in relation to the housing (e.g. see [0050]“In one embodiment, the performance sensors 140 further include a completion sensor 144 for detecting when the drive mechanism 120 reaches its most distal position (i.e. indicative of movement) upon completion of a dosing event”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the auto-injector elements of Tamtoro with the movement parameter of Dahmani for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Regarding Claim 35, Tamtoro and Sjöstedt teach the limitations of Claim 31. Tamtoro further discloses wherein the medicament delivery device comprises a signal generating element (e.g. see [0116] “the drug delivery device 302 may include a noise-making device”) and a housing (e.g. see [0102] “According to this embodiment, the drug delivery device 302 may include the housing 310”) and wherein the parameter comprises a click sound from an interaction between the signal generating element and the housing (e.g. see [0116] “the drug delivery device 302 may include a noise-making device, such as a ratchet or clicker, that actuates when the drug delivery is complete”).
In the same field of endeavor, Dahmani also teaches wherein the parameter comprises a click sound from an interaction between the signal generating element and the housing (e.g. see [0047] “In some embodiments, a vibration or acoustic sensor may be used to listen or sense when mechanical aspects of the drug delivery device have actuated to deliver the medicament stored therein. For example, the mechanical aspects of the drug delivery device may be configured to click or vibrate when a dosing event occurs”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the click sound of Tamtoro with the acoustic sensor and click sound of Dahmani for the purpose of evaluating the medicament delivery device with the advantage of targeting specific device elements to ensure their continued proper operation.
Claims 31, 33, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Tamtoro (WO2015187799 A1) in view of Sjöstedt (US20180197634 A1).
Regarding Claim 31, Tamtoro teaches a system for evaluating if a batch of medicament delivery devices should be recalled (e.g. see [0002] “systems and methods for use with drug delivery devices and relating to the processing of sensor data collected by the drug delivery devices to determine their condition and/or operational state”), the system comprising:
a batch of medicament delivery devices for self-administration of a medicament (e.g. see [0003] “drugs can be administered through the use of drug delivery devices, such as autoinjectors or on-body injectors or infusers”), wherein each medicament delivery device comprises:
a plurality of device components for delivering a medicament (e.g. see [0008] “The drug delivery device may include a reservoir, a delivery cannula having a proximal end in fluid communication with the reservoir and a distal end to be received within a patient, one or more sensors configured to generate sensor data representative of at least one of a condition or an operational state of the drug delivery device, and a first communication module coupled to the one or more sensors and configured to transmit the sensor data”)
a device processor (e.g. see [0095] “The controller 350 (i.e. device controller located within the device) may include at least one processor 370”);
a device memory comprising batch data related to the batch (e.g. see [0096] “The memory 372 may store the identity information discussed above. The identity information may be stored in the memory 372 prior to the start of execution of any of the methods discussed above. The identity information may include, by way of example and not by way of limitation, a unique identifier, the name of the drug, the dosage, an expiration date, and information regarding the identity of the patient for whom the drug was prescribed”);
a GPS configured to determine a geographical position of the medicament delivery device when the medicament delivery device is activated (e.g. see [0094] “The sensors 360, 362, 364, and 365 may each generate sensor data (e.g., raw or unprocessed data) related to a respective measured property or aspect of the drug delivery device 302. The sensor data may be representative of at least one of a condition or operational state of the drug delivery device 302,” and [0050-0051] “For example, a non-limiting matrix of state and identity information may include the following: Condition State Information:
Temperature
Shock or vibration exposure
Light exposure
Color and/or turbidity (as relates to the drug)
Orientation
Geographic position
Temporal information”)
a device transceiver (e.g. see [0108] “the local computing device 304 may further include a communication module 430 for wireless communication with the communication module 352 of the drug delivery device 302”); and
a sensor comprising at least one of a group of sensors which comprises a force sensor, an optical sensor, an acoustic sensor, a position sensor, a pressure sensor and a friction sensor (e.g. see [0099] “According to one embodiment, the skin sensor 362 is a pressure sensor”);
wherein each medicament delivery device is configured to use the sensor to generate sensor data related to an actual value for a parameter from an interaction between some or all of the device components during use of the medicament delivery device (e.g. see [0008] “one or more sensors configured to generate sensor data representative of at least one of a condition or an operational state of the drug delivery device,” and [0027] “the system or method may determine if plunger has been moved from a first end of a bore (defining drug reservoir) to a second end of the ore to determine if the drug delivery device is in the ‘drug delivery complete’ state”)
to generate time data related to a time point of an activation of the medicament delivery device (e.g. see [0065] “the drug delivery system or the one or more computing devices may use the information to make a determination of the time of day (or week, month, etc.) that the patient usually takes their medication. This determination may be based, in part, on the patient record in which time information is associated with operational state information, such as relates to the triggering of the drug delivery device or the completion of the drug delivery”), and to store the sensor data and time data in the device memory (e.g. see [0070] “The determination may also be based in part on condition state information, such as the temperature, shock/vibration exposure, light exposure or color and/or turbidity of the drug, whether determined at a particular time or over a period of time (i.e., a history as established in the drug delivery device record or the drug record),” and [0043] “In this regard, the one or more computing devices adapted or programmed to carry out the method 200 may perform the actions of retrieving the one or more records from storage in one or more memory storage devices, writing the information received from the drug delivery device into the one or more records, and then storing the one or more records in the one or more memory storage devices”); and
a remote computer (e.g. see [0080] “Fig. 3 illustrates an embodiment of a system 300 including a drug delivery device 302, a local computing device 304 and a remote computing device 306”) comprising:
a remote processor (e.g. see [0106] “the remote computing device 306 may be in the form of at least one computing device including at least one processor 424”);
a remote transceiver (e.g. see [0078] “the systems and methods illustrated in Figs. 3-14 may have an associated controller (which controller may include a processor and memory) that is in communication with a first communication module (which may be a transmitter, a transmitter and receiver pair, or a transceiver)”); and
a remote memory (e.g. see [0106] “Likewise, the remote computing device 306 may be in the form of at least one computing device including at least one processor 424 (e.g., microprocessor) and memory 426 (e.g., a random access memory (RAM), a non-volatile memory such as a hard disk, a flash memory, a removable memory, a non-removable memory, etc.)”);
containing regular reference data related to a regular reference value for a parameter from a regular interaction between some or all of the device components of a comparable medicament delivery device (e.g. see [0131] “In some embodiments, the evaluation of the temperature history may be performed by the controller 423 of the local computing device 304 by comparing temperature history received from the drug delivery device 302 with information stored in the memory 422 of the local computing device 304 to determine if the temperature history is acceptable for a patient to use the drug delivery device 302”);
wherein each medicament delivery device is configured to establish a connection to the remote computer over a communication network when the medicament delivery device is activated (e.g. see [0128] “With reference now to Fig. 13, a method 800 carried out by the local computing device 304 starts at block 802 with the receipt by the local computing device 304 (or more particularly, the communication module 430) of the communication transmitted by the drug delivery device 302,” and [0126] “It should be noted that, in an alternative embodiment, some or all of the actions performed by the local computing device 304 with regard to Fig. 13 may be performed by the remote computing device 306,” Examiner notes the method described in Fig. 13 and [0128] teaches the delivery device establishing a connection with the computer, and [0126] teaches that the computer can be the remote computer);
wherein each medicament delivery device is configured to transfer the batch data, the sensor data and the time data to the remote computer via the connection (e.g. see [0010] “the method may include: (a) collecting sensor data with the drug delivery device; (b) transmitting the sensor data from the drug delivery device with the first communication module; (c) receiving the sensor data from the drug delivery device at the external computing device with the second communication module,” and [0044-0045] “system used by the individual patient that is stored in a drug delivery system database. The drug delivery system record may be used to store information regarding the drug delivery system throughout the life of the drug delivery system. The drug delivery system record may be accessed by the drug delivery system manufacturer or the drug provider for quality control purposes…There may also be record for drug used in the drug delivery system that is stored in a drug database. This record may be used in a similar fashion to the drug delivery system record for quality control purposes…This action may require not only the information received in the report and/or stored previously in the record updated at block 204, but may require additional information such as from other patient records, drug system delivery records and/or drug records (i.e. batch data and time data). If this is the case, the determination may be made at block 208 that these other records need to be accessed, and the information retrieved at block 210”);
wherein the remote computer is configured to create a batch data set containing the batch data, the sensor data and the time data for the batch, and to store the batch data set in the remote memory [0043] “At block 204, the report received from the drug delivery system is used to update one or more records. In this regard, the one or more computing devices adapted or programmed to carry out the method 200 may perform the actions of retrieving the one or more records from storage in one or more memory storage devices, writing the information received from the drug delivery device into the one or more records”);.
wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory and to determine, based on the evaluation, if a batch of medicament delivery devices should be recalled (e.g. see [0063] “The determination may be based in part on operational state information, in comparison with information that may be collected and stored regarding conventional norms in operation. Alternatively or additionally, a comparison between the determined, reported or received operational states may permit a determination to be made that the injection was not performed correctly. For example, the determination, reporting or receipt of operational state information indicating that the drug delivery is complete without operation state information indicating that device was triggered, that the device was applied to the patient, and/or that the cannula was inserted may indicate that the drug delivery device has failed to perform correctly, is faulty or was operated incorrectly”).
Tamtoro does not explicitly disclose wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory based on machine learning, and to determine, based on the evaluation, if a batch of medicament delivery devices should be recalled-, as determined by the machine learning analysis finding one or more parameter values associated with a device component to be unacceptable, wherein the machine learning analysis involves the application of one or more algorithms to detect patterns within the batch data set, wherein the one or more algorithms comprise one or more mathematical models based on regular reference data.
In the same field of endeavor, Sjöstedt teaches disclose wherein the remote computer is configured to evaluate the batch data set and the regular reference data stored in the remote memory based on machine learning(e.g. see [0026-0027] “said remote computer and portable medical device adapted to establish a data connection with the use of a cellular network, the portable medical device being configured to use the data connection to transfer the sensor data to the remote computer, the remote computer configured to use the sensor data to determine whether the portable medical device is in need of scrapping, service or replacement. The remote computer of the system may have decision rule software, said decision rule software being generated by applying machine learning to a data set, said data set comprising sensor data previously collected from comparable portable medical devices, said data set further comprising data about any previous failures of such comparable portable medical devices”), and to determine, based on the evaluation, if a batch of medicament delivery devices should be recalled (e.g. see [0009] “One advantage of the invention is that sensor values are reported to and analysed by a remote computer. The remote computer determines whether the medical device should, for example, be scrapped, serviced or replaced”), as determined by the machine learning analysis finding one or more parameter values associated with a device component to be unacceptable (e.g. see [0019-0020] “Step d) of the method comprise the use of a decision rule. Step d) of the method may comprise the step of the remote computer establishing if the value exceeds a threshold value. This is simple and convenient way of monitoring a medical device. [0020] Step d) of the method may comprise the step of the remote computer applying a decision rule, said decision rule being generated by applying machine learning to a data set, said data set comprising data previously collected from comparable portable medical devices, said data set also comprising data about previous failure or error codes of such similar portable medical devices.”),
wherein the machine learning analysis involves the application of one or more algorithms to detect patterns within the batch data set (e.g. see [0012] “The data set can be used to build knowledge about usage patterns and sensor data in its relation to malfunctioning of devices. The data set may be used to improve decision rules regarding service or replacement of the medical device. Machine learning or data mining technologies may be used for this purpose”), wherein the one or more algorithms comprise one or more mathematical models based on regular reference data (e.g. see [0100] “Machine learning, as such, is previously known. Machine learning may for example involve applying Bayesian networks or artificial neural networks. Guidance can be found in US2014/0012784, US2008/0059120 and U.S. Pat. No. 8,819,498. The remote computer 4 may comprise machine learning software 16 that analyses the data set 15 to produce a decision rule that is implemented in the decision rule software 10. The data set 15 may comprise the information in the unit database 11”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the remote computer data evaluation of Tamtoro with the machine learning embodiment of Sjöstedt for the purpose of determining the operating status of the medication delivery device with the advantage of a computerized comparison method to increase accuracy and efficiency.
Regarding Claim 33, Tamtoro and Sjöstedt teach the limitations of Claim 31. Tamtoro further discloses wherein at least one parameter comprises:
a friction force;
a pressure force (e.g. see [0099] “According to one embodiment, the skin sensor 362 is a pressure sensor. According to other embodiments, the skin sensor 362 may be a capacitance sensor, resistance sensor, or inductance sensor. The skin sensor 362 or the switch 366 (which is attached to or associated with the actuator 340) may be used to determine when the drug delivery device 302 is activated or actuated, depending on the design and operation of the drug delivery device 302 that is used to actuate the drive 330”);
a click sound; or
a sound generated from the interaction between at least two device components or a displacement of one device component in relation to another device component.
Regarding Claim 36, Tamtoro, and Sjöstedt teach the limitations of Claim 31.Tamtoro further discloses wherein the medicament delivery device comprises a needle, a cannula or a nozzle configured to expel medicament from the medicament delivery device (e.g. see [0082] “The delivery cannula 314 may be, for example, a rigid needle having a beveled edge that may be sized such that the second end 318 of the needle 314 is received under the skin so as to deliver a subcutaneous injection of the medicament within the reservoir 312”).
Conclusion
THIS ACTION IS MADE FINAL. 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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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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