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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 Oct 2025 has been entered.
This Office Action is responsive to the amendment filed on 13 Oct 2025. As directed by the amendment: claims 1, 5, 13, and 25 have been amended, no claims have been canceled, and no claims have been added. Thus, claims 1-25 are presently pending in this application.
Response to Arguments
Claim Objection
Applicant’s arguments, see Remarks, filed 13 Oct 2025, with respect to the objections to the claims have been fully considered and are persuasive in light of the claim amendments. The objections to the claims have been withdrawn.
§ 103 Rejection
Applicant’s arguments, see Remarks, filed 13 Oct 2025, with respect to the rejections of claims 1, 13, and 25 under 35 U.S.C. 103 have been fully considered and are persuasive in light of the claim amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Rogers et al. (US 20210113099 A1), hereinafter Rogers, as explained in further detail below.
Claim Objections
Claims 1, 13, and 25 are objected to because of the following informalities: “obtaining, based on the tasks performed by the subject” should read “obtaining, based on the one or more tasks performed by the subject”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 9 and 21 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claims 9 and 21 recite steps that are already recited in parent claims 1 and 13. Therefore, claims 9 and 21 do not specify a further limitation of the subject matter recited in claims 1 and 13.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 7, 9, 11-13, 15-17, 19, 21, and 23-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rogers et al. (US 20210113099 A1), hereinafter Rogers.
Regarding claim 1, Rogers discloses a method comprising:
accessing subject data of the subject (paragraphs [0124], [0165]);
determining a predicted condition for the subject based on the subject data (paragraph [0124], "'Customized machine learning' refers to the analysis of the output from the sensor that is tailored to the individual user. Such a system recognizes the person-to-person variabilities between users, including by medical condition");
sending, to a user device of a subject, first software code (paragraph [0132], electronic instructions) that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0186], "a therapeutic swallow primer that triggers user swallowing"), wherein the one or more tasks are based on the predicted condition (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (paragraph [0174], "When the signal of interested is filtered with the appropriate band of frequency, the specific event of interest (e.g. talking vs coughing vs scratching) is better elucidated from the acoustomechanic sensor's raw output. Using energy information generated from acceleration of the sensor, the information such as the duration of a discrete event or the number or the frequency of the event is better calculated"; paragraph [0186], "a sensor that detects one or more parameters"; paragraph [0147]);
sending, to a base station that is communicatively coupled (paragraph [0165], smartphone, tablet, or laptop) to one or more implantable devices implanted in one or more regions of the subject (paragraph [0126]), based on the instantaneous user characteristic of the subject, second software code, wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (paragraph [0165], "This signal processing and further machine learning based on the output of the sensor can be deployed either on the device itself, a smartphone, or a cloud-based system"), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0186], "In this embodiment to trigger a swallow, we propose a vibratory motor that provides direct haptic feedback. Other trigger mechanisms may include a visual notification (e.g. light emitting diode), an electrical impulse (e.g., electrodes), a temperature notification (e.g., thermistors)");
receiving, from the user device and the base station, a communication that represents one or more inputs, wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0187], "on-body sensing is achieved with an enclosed sensing/stimulating circuit enabled through real-time processing"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0182], "the sensors provided herein can be used to calculate, in a patient's naturalistic environment, scores of swallowing function that are sensitive to small but clinically meaningful changes"; paragraph [0203], "the sensor can be used to assess functional performance of the subject, for example, by assessing physical activity, breathing performance or swallowing performance in these conditions");
determining a treatment recommendation for the subject based on the communication (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger in a feedback loop"); and
outputting the treatment recommendation for the subject (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window").
Regarding claim 3, Rogers discloses the method of claim 1, as explained above. Rogers further discloses that the base station includes:
a magnetic field generator (paragraphs [0170], [0261], NFC hardware); and
a magnetic transceiver (paragraphs [0165], [0229], [0261], Bluetooth communication hardware).
Regarding claim 4, Rogers discloses the method of claim 1, as explained above. Rogers further discloses:
receiving an additional communication from one or more sensors that are physically or wirelessly connected to the base station, or from the one or more implantable devices during the execution of the first software code and the second software code (paragraph [0165], "The system may employ any of a range of bidirectional communication systems, including those that correspond to the Bluetooth® standard, to connect to any standard smartphone (FIG. 19), tablet or laptop"; paragraphs [0169]-[0170]); and
determining the treatment recommendation for the subject based on the additional communication (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 5, Rogers discloses the method of claim 1, as explained above. Rogers further discloses:
generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window");
predicting, based on the communication and the additional communication, whether or the degree to which the set of implant stimulation protocols are resulting in a target effect for the instantaneous user characteristic (paragraph [0186], "the system is configured to provide a sensor that detects one or more parameters which are used as the basis of input for a feedback loop"); and
modifying the second software code based on the prediction (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 7, Rogers discloses the method of claim 1, as explained above. Rogers further discloses:
generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window");
predicting, based on the communication and the additional communication, whether or the degree to which the set of implant stimulation protocols are resulting in a target effect for the instantaneous user characteristic (paragraph [0182], "the sensors provided herein can be used to calculate, in a patient's naturalistic environment, scores of swallowing function that are sensitive to small but clinically meaningful changes"; paragraph [0183], "Our sensor could then quantify swallowing events in the context of the respiratory cycle and provide a measure of 'safe swallows.'"); and
determining the treatment recommendation based on the prediction (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 9, Rogers discloses the method of claim 1, as explained above. Rogers further discloses:
accessing subject data of the subject (paragraphs [0124], [0165]);
determining a predicted condition for the subject based on the subject data (paragraph [0124], "'Customized machine learning' refers to the analysis of the output from the sensor that is tailored to the individual user. Such a system recognizes the person-to-person variabilities between users, including by medical condition");
selecting the first software code and the second software code (paragraph [0132], electronic instructions) based on the predicted condition (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger");
Regarding claim 11, Rogers discloses the method of claim 1, as explained above. Rogers further discloses that determining the treatment recommendation includes inputting the communication into a machine-learning model (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger in a feedback loop").
Regarding claim 12, Rogers discloses the method of claim 1, as explained above. Rogers further discloses that the treatment recommendation comprises a modification to a timing of the electrical signal delivered by the one or more implantable devices (paragraph [0186]).
Regarding claim 13, Rogers discloses a system comprising:
one or more data processors (paragraph [0132], programmable circuitry); and
a non-transitory computer readable storage medium (paragraph [0132], electronic instructions) containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions including:
accessing subject data of the subject (paragraphs [0124], [0165]);
determining a predicted condition for the subject based on the subject data (paragraph [0124], "'Customized machine learning' refers to the analysis of the output from the sensor that is tailored to the individual user. Such a system recognizes the person-to-person variabilities between users, including by medical condition");
sending, to a user device of a subject, first software code (paragraph [0132], electronic instructions) that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0186], "a therapeutic swallow primer that triggers user swallowing"), wherein the one or more tasks are based on the predicted condition (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (paragraph [0174], "When the signal of interested is filtered with the appropriate band of frequency, the specific event of interest (e.g. talking vs coughing vs scratching) is better elucidated from the acoustomechanic sensor's raw output. Using energy information generated from acceleration of the sensor, the information such as the duration of a discrete event or the number or the frequency of the event is better calculated"; paragraph [0186], "a sensor that detects one or more parameters"; paragraph [0147]);
sending, to a base station that is communicatively coupled (paragraph [0165], smartphone, tablet, or laptop) to one or more implantable devices implanted in one or more regions of the subject (paragraph [0126]), based on the instantaneous user characteristic of the subject, second software code, wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (paragraph [0165], "This signal processing and further machine learning based on the output of the sensor can be deployed either on the device itself, a smartphone, or a cloud-based system"), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0186], "In this embodiment to trigger a swallow, we propose a vibratory motor that provides direct haptic feedback. Other trigger mechanisms may include a visual notification (e.g. light emitting diode), an electrical impulse (e.g., electrodes), a temperature notification (e.g., thermistors)");
receiving, from the user device and the base station, a communication that represents one or more inputs, wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0187], "on-body sensing is achieved with an enclosed sensing/stimulating circuit enabled through real-time processing"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0182], "the sensors provided herein can be used to calculate, in a patient's naturalistic environment, scores of swallowing function that are sensitive to small but clinically meaningful changes"; paragraph [0203], "the sensor can be used to assess functional performance of the subject, for example, by assessing physical activity, breathing performance or swallowing performance in these conditions");
determining a treatment recommendation for the subject based on the communication (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger in a feedback loop"); and
outputting the treatment recommendation for the subject (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window").
Regarding claim 15, Rogers discloses the system of claim 13, as explained above. Rogers further discloses that the base station includes:
a magnetic field generator (paragraphs [0170], [0261], NFC hardware); and
a magnetic transceiver (paragraphs [0165], [0229], [0261], Bluetooth communication hardware).
Regarding claim 16, Rogers discloses the system of claim 13, as explained above. Rogers further discloses:
receiving an additional communication from one or more sensors that are physically or wirelessly connected to the base station, or from the one or more implantable devices during the execution of the first software code and the second software code (paragraph [0165], "The system may employ any of a range of bidirectional communication systems, including those that correspond to the Bluetooth® standard, to connect to any standard smartphone (FIG. 19), tablet or laptop"; paragraphs [0169]-[0170]); and
determining the treatment recommendation for the subject based on the additional communication (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 17, Rogers discloses the system of claim 13, as explained above. Rogers further discloses:
generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window");
predicting, based on the communication and the additional communication, whether or the degree to which the set of implant stimulation protocols are resulting in a target effect for the instantaneous user characteristic (paragraph [0186], "the system is configured to provide a sensor that detects one or more parameters which are used as the basis of input for a feedback loop"); and
modifying the second software code based on the prediction (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 19, Rogers discloses the system of claim 13, as explained above. Rogers further discloses:
generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window");
predicting, based on the communication and the additional communication, whether or the degree to which the set of implant stimulation protocols are resulting in a target effect for the instantaneous user characteristic (paragraph [0182], "the sensors provided herein can be used to calculate, in a patient's naturalistic environment, scores of swallowing function that are sensitive to small but clinically meaningful changes"; paragraph [0183], "Our sensor could then quantify swallowing events in the context of the respiratory cycle and provide a measure of 'safe swallows.'"); and
determining the treatment recommendation based on the prediction (paragraph [0022], "One or more machine learning algorithms may be used in a feedback loop for optimization of the haptic signal timing"; paragraph [0180]).
Regarding claim 21, Rogers discloses the system of claim 13, as explained above. Rogers further discloses:
accessing subject data of the subject (paragraphs [0124], [0165]);
determining a predicted condition for the subject based on the subject data (paragraph [0124], "'Customized machine learning' refers to the analysis of the output from the sensor that is tailored to the individual user. Such a system recognizes the person-to-person variabilities between users, including by medical condition");
selecting the first software code and the second software code (paragraph [0132], electronic instructions) based on the predicted condition (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger");
Regarding claim 23, Rogers discloses the system of claim 13, as explained above. Rogers further discloses that determining the treatment recommendation includes inputting the communication into a machine-learning model (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger in a feedback loop").
Regarding claim 24, Rogers discloses the system of claim 13, as explained above. Rogers further discloses that the treatment recommendation comprises a modification to a timing of the electrical signal delivered by the one or more implantable devices (paragraph [0186]).
Regarding claim 25, Rogers discloses a computer-program product tangibly embodied in a non-transitory machine-readable storage medium that includes instructions (paragraph [0132]) configured to cause one or more data processors to perform a set of actions including:
accessing subject data of the subject (paragraphs [0124], [0165]);
determining a predicted condition for the subject based on the subject data (paragraph [0124], "'Customized machine learning' refers to the analysis of the output from the sensor that is tailored to the individual user. Such a system recognizes the person-to-person variabilities between users, including by medical condition");
sending, to a user device of a subject, first software code (paragraph [0132], electronic instructions) that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0186], "a therapeutic swallow primer that triggers user swallowing"), wherein the one or more tasks are based on the predicted condition (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (paragraph [0174], "When the signal of interested is filtered with the appropriate band of frequency, the specific event of interest (e.g. talking vs coughing vs scratching) is better elucidated from the acoustomechanic sensor's raw output. Using energy information generated from acceleration of the sensor, the information such as the duration of a discrete event or the number or the frequency of the event is better calculated"; paragraph [0186], "a sensor that detects one or more parameters"; paragraph [0147]);
sending, to a base station that is communicatively coupled (paragraph [0165], smartphone, tablet, or laptop) to one or more implantable devices implanted in one or more regions of the subject (paragraph [0126]), based on the instantaneous user characteristic of the subject, second software code, wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (paragraph [0165], "This signal processing and further machine learning based on the output of the sensor can be deployed either on the device itself, a smartphone, or a cloud-based system"), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0186], "In this embodiment to trigger a swallow, we propose a vibratory motor that provides direct haptic feedback. Other trigger mechanisms may include a visual notification (e.g. light emitting diode), an electrical impulse (e.g., electrodes), a temperature notification (e.g., thermistors)");
receiving, from the user device and the base station, a communication that represents one or more inputs, wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0187], "on-body sensing is achieved with an enclosed sensing/stimulating circuit enabled through real-time processing"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0182], "the sensors provided herein can be used to calculate, in a patient's naturalistic environment, scores of swallowing function that are sensitive to small but clinically meaningful changes"; paragraph [0203], "the sensor can be used to assess functional performance of the subject, for example, by assessing physical activity, breathing performance or swallowing performance in these conditions");
determining a treatment recommendation for the subject based on the communication (paragraph [0186], "machine learning algorithms can be used to optimize the timing of the trigger in a feedback loop"); and
outputting the treatment recommendation for the subject (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window").
Claims 1, 13, and 25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baker et al. (US 20250025698 A1), hereinafter Baker.
Regarding claim 1, Baker discloses a method comprising:
accessing subject data of the subject (Fig. 4B, paragraph [0056], "step 42 may include obtaining data related to the one or more tasks");
determining a predicted condition for the subject based on the subject data (paragraph [0056], medical condition of the patient);
sending, to a user device of a subject (Fig. 1, paragraph [0034], input output device 19), first software code that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0057], "the system 10, the controller 12, and/or the like may output the instruction related to the one or more tasks (e.g., motor tasks, vocal tasks, cognitive tasks, and/or the like) by the one or more input output devices 19, an audio output device, a video device and/or the like"), wherein the one or more tasks are based on the predicted condition (Fig. 4B, paragraph [0056], "the one or more motor tasks may be chosen from predefined listing of motor tasks based on the medical condition of the patient");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (Fig. 4B, paragraph [0058], step 44);
sending, to a base station (Fig. 1, paragraph [0030], controller 12) that is communicatively coupled to one or more implantable devices implanted in one or more regions of the subject (Fig. 1, paragraph [0030], neurostimulator 14), based on the instantaneous user characteristic of the subject, second software code (Fig. 4B, paragraph [0061], step 48), wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (Fig. 4B, paragraph [0061], step 48, determines whether to apply DBS), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0042], "The system 10 may be used to configure a DBS system 200 to stimulate a cerebellar pathway connecting to a brainstem, a diencephalon, a cerebrum, or other location in the brain of a patient to treat a neurological disorder in the patient");
receiving, from the user device and the base station, a communication that represents one or more inputs (Fig. 4B, paragraph [0060], step 44, "neurophysiology activity data may be received by the data acquisition platform"), wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0058], "internal data (e.g., electrophysiology data used in Step 44) and/or external data (e.g., electroencephalography (EEG) data used in Step 46) can be recorded by appropriate electrodes and received (by controller 12) ... the task component 18 of FIG. 1 that can also record data related to the motor task"; paragraph [0031], "the received data may include data received in response to a patient performing a motor task with a task component 18"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0056], "step 42 may include obtaining data relating to evaluation of how stimulation impacts spontaneous neural data as a distinct option from task-related changes");
determining a treatment recommendation for the subject based on the communication (paragraph [0065], "the algorithm may utilize any of the input signals to determine how to apply the DBS by the DBS system 200. In other words, the stimulation parameters for application of the DBS by the DBS system 200"); and
outputting the treatment recommendation for the subject (paragraph [0035], "The one or more input output devices 19 can be configured to provide outputs from the system 10, the DBS system 200, the controller 12, and/or the like via a graphical user interface, including visual information."; paragraph [0047]).
Regarding claim 13, Baker discloses a system comprising:
one or more data processors (Fig. 2, paragraph [0047], processor 24); and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions (Fig. 2, paragraph [0045], memory 22) including:
accessing subject data of the subject (Fig. 4B, paragraph [0056], "step 42 may include obtaining data related to the one or more tasks");
determining a predicted condition for the subject based on the subject data (paragraph [0056], medical condition of the patient);
sending, to a user device of a subject (Fig. 1, paragraph [0034], input output device 19), first software code that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0057], "the system 10, the controller 12, and/or the like may output the instruction related to the one or more tasks (e.g., motor tasks, vocal tasks, cognitive tasks, and/or the like) by the one or more input output devices 19, an audio output device, a video device and/or the like"), wherein the one or more tasks are based on the predicted condition (Fig. 4B, paragraph [0056], "the one or more motor tasks may be chosen from predefined listing of motor tasks based on the medical condition of the patient");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (Fig. 4B, paragraph [0058], step 44);
sending, to a base station (Fig. 1, paragraph [0030], controller 12) that is communicatively coupled to one or more implantable devices implanted in one or more regions of the subject (Fig. 1, paragraph [0030], neurostimulator 14), based on the instantaneous user characteristic of the subject, second software code (Fig. 4B, paragraph [0061], step 48), wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (Fig. 4B, paragraph [0061], step 48, determines whether to apply DBS), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0042], "The system 10 may be used to configure a DBS system 200 to stimulate a cerebellar pathway connecting to a brainstem, a diencephalon, a cerebrum, or other location in the brain of a patient to treat a neurological disorder in the patient");
receiving, from the user device and the base station, a communication that represents one or more inputs (Fig. 4B, paragraph [0060], step 44, "neurophysiology activity data may be received by the data acquisition platform"), wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0058], "internal data (e.g., electrophysiology data used in Step 44) and/or external data (e.g., electroencephalography (EEG) data used in Step 46) can be recorded by appropriate electrodes and received (by controller 12) ... the task component 18 of FIG. 1 that can also record data related to the motor task"; paragraph [0031], "the received data may include data received in response to a patient performing a motor task with a task component 18"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0056], "step 42 may include obtaining data relating to evaluation of how stimulation impacts spontaneous neural data as a distinct option from task-related changes");
determining a treatment recommendation for the subject based on the communication (paragraph [0065], "the algorithm may utilize any of the input signals to determine how to apply the DBS by the DBS system 200. In other words, the stimulation parameters for application of the DBS by the DBS system 200"); and
outputting the treatment recommendation for the subject (paragraph [0035], "The one or more input output devices 19 can be configured to provide outputs from the system 10, the DBS system 200, the controller 12, and/or the like via a graphical user interface, including visual information."; paragraph [0047]).
Regarding claim 25, Baker discloses a computer-program product tangibly embodied in a non-transitory machine-readable storage medium that includes instructions configured to cause one or more data processors to perform a set of actions (Fig. 2, paragraph [0045], memory 22) including:
accessing subject data of the subject (Fig. 4B, paragraph [0056], "step 42 may include obtaining data related to the one or more tasks");
determining a predicted condition for the subject based on the subject data (paragraph [0056], medical condition of the patient);
sending, to a user device of a subject (Fig. 1, paragraph [0034], input output device 19), first software code that is configured to present, at a user interface of the user device, one or more tasks to be performed by the subject using the user device (paragraph [0057], "the system 10, the controller 12, and/or the like may output the instruction related to the one or more tasks (e.g., motor tasks, vocal tasks, cognitive tasks, and/or the like) by the one or more input output devices 19, an audio output device, a video device and/or the like"), wherein the one or more tasks are based on the predicted condition (Fig. 4B, paragraph [0056], "the one or more motor tasks may be chosen from predefined listing of motor tasks based on the medical condition of the patient");
obtaining, based on the tasks performed by the subject, information regarding an instantaneous user characteristic of the subject with respect to the predicted condition (Fig. 4B, paragraph [0058], step 44);
sending, to a base station (Fig. 1, paragraph [0030], controller 12) that is communicatively coupled to one or more implantable devices implanted in one or more regions of the subject (Fig. 1, paragraph [0030], neurostimulator 14), based on the instantaneous user characteristic of the subject, second software code (Fig. 4B, paragraph [0061], step 48), wherein:
the second software code includes an instruction to cause the one or more implantable devices to execute a set of stimulation protocols of a plurality of stimulation protocols (Fig. 4B, paragraph [0061], step 48, determines whether to apply DBS), and
the one or more implantable devices are configured to generate an electrical signal, according to the set of stimulation protocols, to stimulate the one or more regions of the subject (paragraph [0042], "The system 10 may be used to configure a DBS system 200 to stimulate a cerebellar pathway connecting to a brainstem, a diencephalon, a cerebrum, or other location in the brain of a patient to treat a neurological disorder in the patient");
receiving, from the user device and the base station, a communication that represents one or more inputs (Fig. 4B, paragraph [0060], step 44, "neurophysiology activity data may be received by the data acquisition platform"), wherein:
the one or more inputs were detected at the user interface while or after the electrical signal was applied to the subject (paragraph [0058], "internal data (e.g., electrophysiology data used in Step 44) and/or external data (e.g., electroencephalography (EEG) data used in Step 46) can be recorded by appropriate electrodes and received (by controller 12) ... the task component 18 of FIG. 1 that can also record data related to the motor task"; paragraph [0031], "the received data may include data received in response to a patient performing a motor task with a task component 18"), and
the one or more inputs indicate a degree to which the instantaneous user characteristic is affected while or after the electrical signal was applied to the subject (paragraph [0056], "step 42 may include obtaining data relating to evaluation of how stimulation impacts spontaneous neural data as a distinct option from task-related changes");
determining a treatment recommendation for the subject based on the communication (paragraph [0065], "the algorithm may utilize any of the input signals to determine how to apply the DBS by the DBS system 200. In other words, the stimulation parameters for application of the DBS by the DBS system 200"); and
outputting the treatment recommendation for the subject (paragraph [0035], "The one or more input output devices 19 can be configured to provide outputs from the system 10, the DBS system 200, the controller 12, and/or the like via a graphical user interface, including visual information."; paragraph [0047]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 20210113099 A1), hereinafter Rogers, in view of Sun et al. (US Publication No. 20200144480 A1, previously cited), hereinafter Sun.
Regarding claim 2, Rogers discloses the method of claim 1, as explained above. Rogers further discloses that at least one of the one or more implantable devices includes one or more electrodes (paragraphs [0019], [0186]), but does not disclose that the one or more implantable devices includes a magnetoelectric film and an electrical circuit coupled to the magnetoelectric film.
However, Sun discloses an implantable system for recording and manipulating neural activity (paragraph [0009]) wherein the implantable system includes:
a magnetoelectric film (Fig. 4A, paragraph [0058], "The ME antenna 102 may be an ME thin-film heterostructure 400, comprising thin-film piezoelectric elements 402 (e.g., 500 nm thick aluminum nitride (AlN)), and a thin-film magnetorestrictive element 404 (500 nm thick FeGaB)."); and
an electrical circuit coupled to the magnetoelectric film (Fig. 1A, paragraph [0052], implantable system 100 comprises ME antenna 102 and integrated circuit 104; Fig. 1B, paragraphs [0066]-[0067]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Sun to include a magnetic field generator because doing so allows wireless power transfer to the implantable device (Sun, paragraph [0050]), which eliminates the need for a battery (Sun, paragraph [0052]), and allows data transfer from the implantable device to the base station (Sun, paragraph [0066]).
Regarding claim 14, Rogers discloses the system of claim 13, as explained above. Rogers further discloses that at least one of the one or more implantable devices includes one or more electrodes (paragraphs [0019], [0186]), but does not disclose that the one or more implantable devices includes a magnetoelectric film and an electrical circuit coupled to the magnetoelectric film.
However, Sun discloses an implantable system for recording and manipulating neural activity (paragraph [0009]) wherein the implantable system includes:
a magnetoelectric film (Fig. 4A, paragraph [0058], "The ME antenna 102 may be an ME thin-film heterostructure 400, comprising thin-film piezoelectric elements 402 (e.g., 500 nm thick aluminum nitride (AlN)), and a thin-film magnetorestrictive element 404 (500 nm thick FeGaB)."); and
an electrical circuit coupled to the magnetoelectric film (Fig. 1A, paragraph [0052], implantable system 100 comprises ME antenna 102 and integrated circuit 104; Fig. 1B, paragraphs [0066]-[0067]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Sun to include a magnetic field generator because doing so allows wireless power transfer to the implantable device (Sun, paragraph [0050]), which eliminates the need for a battery (Sun, paragraph [0052]), and allows data transfer from the implantable device to the base station (Sun, paragraph [0066]).
Claims 6, 8, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 20210113099 A1), hereinafter Rogers, in view of Lorincz et al. (US Publication No. 20070239211 A1, previously cited), hereinafter Lorincz.
Regarding claim 6, Rogers discloses the method of claim 5, as explained above. Rogers does not explicitly disclose that modifying the second software code includes: determining a subset of the one or more implantable devices that are associated with delivering the electrical signal that results in the target effect; and modifying the second software code to cause the subset of the one or more implantable devices to deliver the electrical signal while remaining implantable devices are inactive.
However, Lorincz teaches a neural prosthesis comprised of a network of component devices (Abstract), configured to:
determine a subset of one or more implantable devices that are associated with delivering stimulation that results in a target effect (paragraph [0020], “The component devices, i.e., peer devices, within a peer group may be further formed into sub-peer groups comprised of sub-peer devices”); and
cause the subset of the one or more implantable devices to deliver stimulation while remaining implantable devices are inactive (paragraph [0020], “The various levels of peer groups are thus logical groups of peer devices that may be created in, on or about a patient in order to … deliver a therapeutic effect based on such data.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Lorincz to determine a subset of the one or more implantable devices that are associated with delivering stimulation that results in the target effect because using a combination of devices can result in a larger effect than using a single device (Lorincz, paragraph [0008]), and using several devices can measure physiological parameters in many different locations in the patient's body, as well as delivery of a therapeutic effect in different locations in a patient's body (Lorincz, paragraph [0009]).
Regarding claim 8, Rogers discloses the method of claim 1, as explained above. Rogers further discloses: generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window").
Rogers does not explicitly disclose determining, based on the communication and the additional communication, a subset of the one or more implantable devices that are associated with delivering the electrical signal that results in a target effect for the instantaneous user characteristic; and generating the treatment recommendation to cause the subset of the one or more implantable devices to deliver the electrical signal while remaining implantable devices are inactive.
However, Lorincz teaches a neural prosthesis comprised of a network of component device (Abstract), configured to:
determine a subset of one or more implantable devices that are associated with delivering stimulation that results in a target effect (paragraph [0020], “The component devices, i.e., peer devices, within a peer group may be further formed into sub-peer groups comprised of sub-peer devices”); and
cause the subset of the one or more implantable devices to deliver stimulation while remaining implantable devices are inactive (paragraph [0020], “The various levels of peer groups are thus logical groups of peer devices that may be created in, on or about a patient in order to … deliver a therapeutic effect based on such data.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Lorincz to determine a subset of the one or more implantable devices that are associated with delivering stimulation that results in the target effect because using a combination of devices can result in a larger effect than using a single device (Lorincz, paragraph [0008]), and using several devices can measure physiological parameters in many different locations in the patient's body, as well as delivery of a therapeutic effect in different locations in a patient's body (Lorincz, paragraph [0009]).
Regarding claim 18, Rogers discloses the system of claim 17, as explained above. Rogers does not explicitly disclose that modifying the second software code includes: determining a subset of the one or more implantable devices that are associated with delivering the electrical signal that results in the target effect; and modifying the second software code to cause the subset of the one or more implantable devices to deliver the electrical signal while remaining implantable devices are inactive.
However, Lorincz teaches a neural prosthesis comprised of a network of component devices (Abstract), configured to:
determine a subset of one or more implantable devices that are associated with delivering stimulation that results in a target effect (paragraph [0020], “The component devices, i.e., peer devices, within a peer group may be further formed into sub-peer groups comprised of sub-peer devices”); and
cause the subset of the one or more implantable devices to deliver stimulation while remaining implantable devices are inactive (paragraph [0020], “The various levels of peer groups are thus logical groups of peer devices that may be created in, on or about a patient in order to … deliver a therapeutic effect based on such data.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Lorincz to determine a subset of the one or more implantable devices that are associated with delivering stimulation that results in the target effect because using a combination of devices can result in a larger effect than using a single device (Lorincz, paragraph [0008]), and using several devices can measure physiological parameters in many different locations in the patient's body, as well as delivery of a therapeutic effect in different locations in a patient's body (Lorincz, paragraph [0009]).
Regarding claim 20, Rogers discloses the system of claim 13, as explained above. Rogers further discloses: generating, by the base station, an additional communication by recording one or more stimulation times at which the one or more implantable devices deliver the electrical signal to the one or more regions (paragraph [0186], "A trigger is delivered that is timed to lead to a swallow event within an ideal respiratory timing window").
Rogers does not explicitly disclose determining, based on the communication and the additional communication, a subset of the one or more implantable devices that are associated with delivering the electrical signal that results in a target effect for the instantaneous user characteristic; and generating the treatment recommendation to cause the subset of the one or more implantable devices to deliver the electrical signal while remaining implantable devices are inactive.
However, Lorincz teaches a neural prosthesis comprised of a network of component device (Abstract), configured to:
determine a subset of one or more implantable devices that are associated with delivering stimulation that results in a target effect (paragraph [0020], “The component devices, i.e., peer devices, within a peer group may be further formed into sub-peer groups comprised of sub-peer devices”); and
cause the subset of the one or more implantable devices to deliver stimulation while remaining implantable devices are inactive (paragraph [0020], “The various levels of peer groups are thus logical groups of peer devices that may be created in, on or about a patient in order to … deliver a therapeutic effect based on such data.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Lorincz to determine a subset of the one or more implantable devices that are associated with delivering stimulation that results in the target effect because using a combination of devices can result in a larger effect than using a single device (Lorincz, paragraph [0008]), and using several devices can measure physiological parameters in many different locations in the patient's body, as well as delivery of a therapeutic effect in different locations in a patient's body (Lorincz, paragraph [0009]).
Claims 10 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 20210113099 A1), hereinafter Rogers, in view of Choi et al. (US 20230123383 A1, previously cited), hereinafter Choi.
Regarding claim 10, Rogers discloses the method of claim 1, as explained above. Rogers does not explicitly disclose receiving an additional communication from an eye tracking device and/or a facial recognition device during the execution of the first software code and the second software code; and determining the treatment recommendation for the subject based on the additional communication.
However, Choi teaches systems and methods for providing neurostimulation therapy (Abstract) comprising:
receiving communication from a facial recognition device during delivery of electrical stimulation (paragraph [0125], "Other inputs may include proxies for facial expressions as rigidity may also correlate with blank affect."); and
determining the treatment recommendation for the subject based on the communication (Fig. 13, paragraphs [0126]-[0129], steps 1307-1309).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Choi to receive an additional communication from an eye tracking device and/or a facial recognition device during the execution of the first software code and the second software code; and determine the treatment recommendation for the subject based on the additional communication, because doing so allows healthcare providers to provide patient health remotely, which increases access to care and decreases healthcare delivery costs (Choi, paragraph [0005]).
Regarding claim 22, Rogers discloses the system of claim 13, as explained above. Rogers does not explicitly disclose receiving an additional communication from an eye tracking device and/or a facial recognition device during the execution of the first software code and the second software code; and determining the treatment recommendation for the subject based on the additional communication.
However, Choi teaches systems and methods for providing neurostimulation therapy (Abstract) comprising:
receiving communication from a facial recognition device during delivery of electrical stimulation (paragraph [0125], "Other inputs may include proxies for facial expressions as rigidity may also correlate with blank affect."); and
determining the treatment recommendation for the subject based on the communication (Fig. 13, paragraphs [0126]-[0129], steps 1307-1309).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rogers with the teachings of Choi to receive an additional communication from an eye tracking device and/or a facial recognition device during the execution of the first software code and the second software code; and determine the treatment recommendation for the subject based on the additional communication, because doing so allows healthcare providers to provide patient health remotely, which increases access to care and decreases healthcare delivery costs (Choi, paragraph [0005]).
Conclusion
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/CHRISTINE SISON/Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796