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 .
This communication is in response to the amendment received on 03/02/2026. Claims 1, 7-8 and 10-11 remain pending in this application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 7-8 and 10-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The added material which is not supported by the original disclosure is as follows: The newly added recitation of:
during execution of the hand-eye coordination training task, receive, in real time, physiological signals generated by the wearable electronic device (the specification recites “the wearable electronic device 13 to collect a training data by its multiple sensors” in [0034]-missing: receive in real-time, physiological signals),
process the training execution data using the pre-trained machine learning model to generate training control parameters defining device-level execution constraints for the hand-eye coordination training task (the specification recites “compare the predicted outcome with one corresponding categorical label, and then generate a comparison data, thereby adaptively modulating at least one model parameter of the machine learning model according to the comparison data” in [0027]-missing: control parameters defining device-level execution constraints),
dynamically control execution of the hand-eye coordination training task by modifying at least one device-level training parameter in real time based on the received training control parameters and the physiological signals, such that execution behavior of the training task on the first electronic device is adaptively modulated during runtime in response to the physiological signals (the specification recites “compare the predicted outcome with one corresponding categorical label, and then generate a comparison data, thereby adaptively modulating at least one model parameter of the machine learning model according to the comparison data” in [0027]-missing: dynamically control…training task, modifying at least one device-level training parameter in real time, device is adaptively modulated during runtime) within claim 1,
“generate, based on the received training control parameters, an execution configuration describing device-level runtime constraints for execution of the hand-eye coordination training task (“compare the predicted outcome with one corresponding categorical label, and then generate a comparison data, thereby adaptively modulating at least one model parameter of the machine learning model according to the comparison data” in [0027]-missing: an execution configuration describing device-level runtime constraints),
wherein the execution configuration is provided to the first electronic device and/or the second electronic device for use in dynamically controlling execution of the hand- eye coordination training task during runtime (the specification recites “compare the predicted outcome with one corresponding categorical label, and then generate a comparison data, thereby adaptively modulating at least one model parameter of the machine learning model according to the comparison data” in [0027]-missing: dynamically controlling…during runtime) within claim 7,
“collect, through the monitoring device, training execution data during runtime execution of the hand-eye coordination training task, and provide the collected training execution data as part of the training execution data used to dynamically regulate execution of the hand-eye coordination training task (the specification recites “compare the predicted outcome with one corresponding categorical label, and then generate a comparison data, thereby adaptively modulating at least one model parameter of the machine learning model according to the comparison data” in [0027]-missing: dynamically regulate execution of the hand-eye coordination training task) within claim 10
appears to constitute new matter. In particular, Applicant does not point to, nor was the Examiner able to find, any support for the features that are provided above as missing features within the specification as originally filed. As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office action.
Claims 8 and 11 incorporate the deficiencies of independent claim 1, through dependency, and are also rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 7-8 and 10-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1, 7-8 and 10-11 are drawn to a system which is within the four statutory categories (i.e. machine).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“a wearable electronic device configured to be worn by the subject and comprising a plurality of physiological sensors configured to continuously generate training-related physiological signals during a training session;
a first electronic device comprising a processor, a display, and a memory, the first electronic device being communicatively coupled to the wearable electronic device and configured to execute a cognitive training application; and
a cloud computing device comprising a processor and a memory storing an application program including a pre-trained machine learning model;
wherein the processor of the first electronic device is configured to:
initiate execution of a hand-eye coordination training task on the first electronic device or on a second electronic device coupled thereto;
during execution of the hand-eye coordination training task, receive, in real time, physiological signals generated by the wearable electronic device; and
transmit training execution data derived from the physiological signals and from task interaction events to the cloud computing device;
wherein the processor of the cloud computing device is configured to:
process the training execution data using the pre-trained machine learning model to generate training control parameters defining device-level execution constraints for the hand-eye coordination training task; and
transmit the training control parameters to the first electronic device;
wherein the processor of the first electronic device is further configured to:
dynamically control execution of the hand-eye coordination training task by modifying at least one device-level training parameter in real time based on the received training control parameters and the physiological signals, such that execution behavior of the training task on the first electronic device is adaptively modulated during runtime in response to the physiological signals.”
The limitation of “process the training execution data using the pre-trained machine learning model to generate training control parameters defining device-level execution constraints for the hand-eye coordination training task” is directed to an abstract idea of mathematical relationships, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Claim 7 has been amended to recite:
“receive, from the cloud computing device, the training control parameters generated based on the training execution data; and
generate, based on the received training control parameters, an execution configuration describing device-level runtime constraints for execution of the hand-eye coordination training task;
wherein the execution configuration is provided to the first electronic device and/or the second electronic device for use in dynamically controlling execution of the hand- eye coordination training task during runtime”
The limitation of “generate, based on the received training control parameters, an execution configuration describing device-level runtime constraints for execution of the hand-eye coordination training task” is directed to an abstract idea of mathematical relationships, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Claims 7-8, 10-11 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 7-8, 10-11 recite the same abstract idea. Claims 7-8, 10-11 describe a further limitation regarding the basis for generating training control parameters for the training task. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements that are shown in bolded style below:
Claim 1 has been amended to recite a digital product system for conducting a cognitive function test and executing a cognitive training session having dynamically controlled execution behavior for a subject; the digital product system comprising:
a wearable electronic device configured to be worn by the subject and comprising a plurality of physiological sensors configured to continuously generate training-related physiological signals during a training session;
a first electronic device comprising a processor, a display, and a memory, the first electronic device being communicatively coupled to the wearable electronic device and configured to execute a cognitive training application; and
a cloud computing device comprising a processor and a memory storing an application program including a pre-trained machine learning model;
wherein the processor of the first electronic device is configured to:
initiate execution of a hand-eye coordination training task on the first electronic device or on a second electronic device coupled thereto;
during execution of the hand-eye coordination training task, receive, in real time, physiological signals generated by the wearable electronic device; and
transmit training execution data derived from the physiological signals and from task interaction events to the cloud computing device;
wherein the processor of the cloud computing device is configured to:
process the training execution data using the pre-trained machine learning model to generate training control parameters defining device-level execution constraints for the hand-eye coordination training task; and
transmit the training control parameters to the first electronic device;
wherein the processor of the first electronic device is further configured to:
dynamically control execution of the hand-eye coordination training task by modifying at least one device-level training parameter in real time based on the received training control parameters and the physiological signals, such that execution behavior of the training task on the first electronic device is adaptively modulated during runtime in response to the physiological signals.
Claim 7 has been amended to recite the digital product system of claim 1, further comprises a third electronic device comprising a third processor and a third memory storing a third application program, and the third processor executes the third application program so as to be configured to:
receive, from the cloud computing device, the training control parameters generated based on the training execution data; and
generate, based on the received training control parameters, an execution configuration describing device-level runtime constraints for execution of the hand-eye coordination training task;
wherein the execution configuration is provided to the first electronic device and/or the second electronic device for use in dynamically controlling execution of the hand- eye coordination training task during runtime.
Claim 8 recites the digital product system of claim 7, wherein the third electronic device is selected from a group consisting of the tablet computer, the smart phone, the smart television, the laptop computer, the desktop computer, and the all-in-one computer.
Claim 10 has been amended to recite the digital product system of claim 7, further comprising a monitoring device in communication with the first electronic device, wherein the first processor of the first electronic device is further configured to:
collect, through the monitoring device, training execution data during runtime execution of the hand-eye coordination training task, and
provide the collected training execution data as part of the training execution data used to dynamically regulate execution of the hand-eye coordination training task.
Claim 11 recites the digital product system of claim 10, wherein the monitoring device comprises at least one selected from a group consisting of a camera and a motion capture system.
These additional elements are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
In particular, each of the first, the second and the third electronic devices, as well as the monitoring device and wearable device are all generic computing devices. The first and third electronic devices are selected form a group consisting of tablet computer, smart phone, smart television, laptop computer, desktop computer and all-in-one computer, and the second electronic device is selected form a group consisting of gaming device, tablet computer, virtual reality helmet, and mixed reality helmet, a monitoring device comprising a camera or a motion capture system, wearable electronic device with sensors and the cloud computing device are all generic computing devices as indicated in the current specification, in [0030].
The claim limitations are recited as being performed by generic computing devices and the computing devices are recited at a high level of generality and the limitations amount to no more than mere instructions to apply the exception using generic computing components. Similarly, the limitations of claim 13 recite using machine learning model, but provide nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning model is used to generally apply the abstract idea without limiting how the trained model functions. The machine learning model is described at a high level such that it amounts to using computer with generic machine learning model to apply the abstract idea.
Claims also recite other additional limitations beyond abstract idea, including functions such as receiving/inputting/storing data from/to a database, transmitting data are all insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the processor to generating training control parameters and dynamically controlling execution of the training task steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The current specification describes the machine learning model as “Computer science (CS) engineers skilled in design of AI program certainly know that, machine learning means computers learning from data using algorithms to perform a task without being explicitly programmed, and deep learning algorithm is a subset of machine learning technology. For example, supervised learning algorithm is developed based on deep learning, which works depends upon artificial neural networks (ANN) that consists of an input layer, at least one hidden layer and an output layer. Feed forward neural networks are artificial neural networks in which nodes do not form loops. This type of neural network is also known as a multi-layer neural network as all information is only passed forward. During data flow, input nodes receive data, which travel through hidden layers, and exit output nodes. No links exist in the network that could get used to by sending information back from the output node.” in [0038]. The “supervised learning algorithm” used in this application corresponds to a well-understood, routine and conventional activity, as indicated in the current specification.
Accordingly, the feature of process the training execution data using the pre-trained machine learning model to generate training control parameters is a well-understood, routine and conventional activity known in the industry and claims are directed to mere instruction to apply an exception.
Therefore, claims 1, 7-8 and 10-11 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1, 7-8 and 10-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ganguly et al. (hereinafter Ganguly) (US 11,285,321 B2).
Claim 1 has been amended to recite a digital product system for conducting a cognitive function test and executing a cognitive training session having dynamically controlled execution behavior for a subject; the digital product system comprising:
a wearable electronic device configured to be worn by the subject and comprising a plurality of physiological sensors configured to continuously generate training-related physiological signals during a training session (Ganguly discloses “A stimulator may be configured as a wearable stimulator that is configured to be worn on a subject's arm and/or wrist, (e.g., and configured to apply stimulation to one or more of the subject's radial, ulnar and median nerves).” in col. 3, lines 20-24);
a first electronic device comprising a processor, a display, and a memory, the first electronic device being communicatively coupled to the wearable electronic device and configured to execute a cognitive training application (Ganguly discloses “…the apparatuses described herein may include control logic , such as software or firmware ( including an application software or “ app ” ) , that controls all or a part of 30 the apparatus , and may coordinate activity of the stimulator , including setting and / or modifying the applied electrical stimulation parameters , and / or training , and / or detecting a biomarker and / or detecting a performance metric . This soft- ware or firmware may be referred to herein as a non- 35 transitory computer - readable storage medium storing the program , and may be configured to operate on a processor of a computer , including a wearable computer ( e.g. , a processor of a smartphone , smartwatch , etc. ) or a hand - held device such as a tablet” in col. 7, lines 28-40); and
a cloud computing device comprising a processor and a memory storing an application program including a pre-trained machine learning model (Ganguly discloses “The algorithm may reside in the app on phone/tablet or a standalone computer or in conjunction with cloud processing. For cloud processing, data from phone/tablet is uploaded to cloud where it is analyzed and next cycle the updated algorithm is sent to phone/tablet.” in col. 20, lines 17-22);
wherein the processor of the first electronic device is configured to:
initiate execution of a hand-eye coordination training task on the first electronic device or on a second electronic device coupled thereto (Ganguly discloses “PNS electrode stimulation is modulated based on feedback from EEG as well as training/feedback “game”. This may be further modulated and optimized based on how people with similar EEG profiles and responses performed in the past. For example, the treatment algorithms of later patients is adjusted or informed by feedback data from patients treated earlier. Alternatively, treatment algorithms for the same patient may be personalized based on their response to PNS, and/or adjusted or titrated in subsequent sessions to further enhance treatment or performance…” in col. 19, line 63 to col. 20, line 15);
during execution of the hand-eye coordination training task, receive, in real time, physiological signals generated by the wearable electronic device (Ganguly discloses “The training/feedback “game” is modulated based on performance both in real time and over subsequent sessions. The algorithm may reside in the app on phone/tablet or a standalone computer or in conjunction with cloud processing. For cloud processing, data from phone/tablet is uploaded to cloud where it is analyzed and next cycle the updated algorithm is sent to phone/tablet.…” in col. 20, lines 15-22); and
transmit training execution data derived from the physiological signals and from task interaction events to the cloud computing device (Ganguly discloses “The training/feedback “game” is modulated based on performance both in real time and over subsequent sessions. The algorithm may reside in the app on phone/tablet or a standalone computer or in conjunction with cloud processing. For cloud processing, data from phone/tablet is uploaded to cloud where it is analyzed and next cycle the updated algorithm is sent to phone/tablet.…” in col. 20, lines 15-22);
wherein the processor of the cloud computing device is configured to:
process the training execution data using the pre-trained machine learning model to generate training control parameters defining device-level execution constraints for the hand-eye coordination training task; and transmit the training control parameters to the first electronic device; wherein the processor of the first electronic device is further configured to: dynamically control execution of the hand-eye coordination training task by modifying at least one device-level training parameter in real time based on the received training control parameters and the physiological signals, such that execution behavior of the training task on the first electronic device is adaptively modulated during runtime in response to the physiological signals (Ganguly discloses “…a range of stimulation parameters are used. A key goal of the paradigm is the use of a feedback system to titrate parameters to each individual. Thus, the range of parameter sweeps to be tested are outlined in order to arrive at the customized range. A variety of machine learning and statistical techniques are used to customize parameters…In regard to the specific stimulation parameters, a stimulation frequency is used that ranges from 0.001 to 1000 Hz. This in inclusive of all sub frequencies (e.g., 10, 10.1, . . . 10.9,11). The stimuli may adapt parameters with a block design (e.g., stimulation of a frequency “sweep” starting at 5 Hz and ending at 20 Hz; another example is a burst mode where two separate fixed bursts are employed)… Various parameters influence the development of an efficacious stimulation or treatment algorithm that may be personalized to individual need and performance.” in col. 20, line 36 to col. 21, line 3).
Claim 7 has been amended to recite the digital product system of claim 1, further comprises a third electronic device comprising a third processor and a third memory storing a third application program, and the third processor executes the third application program so as to be configured to:
receive, from the cloud computing device, the training control parameters generated based on the training execution data; and generate, based on the received training control parameters, an execution configuration describing device-level runtime constraints for execution of the hand-eye coordination training task; wherein the execution configuration is provided to the first electronic device and/or the second electronic device for use in dynamically controlling execution of the hand- eye coordination training task during runtime (Ganguly discloses “…a range of stimulation parameters are used. A key goal of the paradigm is the use of a feedback system to titrate parameters to each individual. Thus, the range of parameter sweeps to be tested are outlined in order to arrive at the customized range. A variety of machine learning and statistical techniques are used to customize parameters…In regard to the specific stimulation parameters, a stimulation frequency is used that ranges from 0.001 to 1000 Hz. This in inclusive of all sub frequencies (e.g., 10, 10.1, . . . 10.9,11). The stimuli may adapt parameters with a block design (e.g., stimulation of a frequency “sweep” starting at 5 Hz and ending at 20 Hz; another example is a burst mode where two separate fixed bursts are employed)… Various parameters influence the development of an efficacious stimulation or treatment algorithm that may be personalized to individual need and performance.” in col. 20, line 36 to col. 21, line 3).
Claim 8 recites the digital product system of claim 7, wherein the third electronic device is selected from a group consisting of the tablet computer, the smart phone, the smart television, the laptop computer, the desktop computer, and the all-in-one computer (Ganguly discloses “This soft- ware or firmware may be referred to herein as a non- 35 transitory computer - readable storage medium storing the program , and may be configured to operate on a processor of a computer , including a wearable computer ( e.g. , a processor of a smartphone , smartwatch , etc. ) or a hand - held device such as a tablet .” in col. 7, lines 28-40).
Claim 10 has been amended to recite the digital product system of claim 7, further comprising a monitoring device in communication with the first electronic device, wherein the first processor of the first electronic device is further configured to:
collect, through the monitoring device, training execution data during runtime execution of the hand-eye coordination training task, and
provide the collected training execution data as part of the training execution data used to dynamically regulate execution of the hand-eye coordination training task (Ganguly discloses “Visuomotor coordination, manual dexterity, and finger individuation are key to playing video games, whether it is based on a regular gaming consul or a phone/tablet…One embodiment of the device is designed to enhance gaming skills designed to improve visuomotor coordination, manual dexterity, and finger individuation, reflexes, precision movements, speed (of tapping, etc.), smoothness of finger movement and hand-eye coordination…The patient opens the app on phone or tablet. She plays series of games which test and train various fine motor skills. The app dynamically adjusts difficulty of different aspects of the game to keep user at the edge of their capacity.” in col. 26, line 17 to col. 27, line 27).
Claim 11 recites the digital product system of claim 10, wherein the monitoring device comprises at least one selected from a group consisting of a camera and a motion capture system (Ganguly; col. 26, lines 28-48).
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims are not directed to an abstract idea, since claim 1 recites “a multi-device system configured to dynamically control execution of a hand-eye coordination training task during runtime based on real-time physiological signals collected from a wearable electronic device”, which are a closed-loop execution control architecture in which electronic device is modulated in response to sensor-driven data.
In response, Examiner submits that the 35 USC 101 rejection has been updated in light of the amendments submitted. Accordingly, claim limitations of “dynamically control execution of the hand-eye coordination training task by modifying at least one device-level training parameter in real time based on the received training control parameters and the physiological signals, such that execution behavior of the training task on the first electronic device is adaptively modulated during runtime in response to the physiological signals” and “dynamically regulate execution of the hand-eye coordination training task” are not part of the abstract idea rejection. These limitations correspond to additional elements, that are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Applicant argues that claims recite a technological improvement in the form of: “runtime regulation of task execution behavior”, “sensor-driven device-level control” and “adaptive modification of execution parameters during operation” rather than post hoc analysis or content selection.
In response, Examiner submits that the feature of process the training execution data using the pre-trained machine learning model to generate training control parameters is a well-understood, routine and conventional activity known in the industry, as indicated in the rejection above. The current specification fails to describe “dynamically controlling execution of training task, but describes the machine learning model as “Computer science (CS) engineers skilled in design of AI program certainly know that, machine learning means computers learning from data using algorithms to perform a task without being explicitly programmed, and deep learning algorithm is a subset of machine learning technology. For example, supervised learning algorithm is developed based on deep learning, which works depends upon artificial neural networks (ANN) that consists of an input layer, at least one hidden layer and an output layer…” in [0038]. Since this feature is found to be a well-understood, routine and conventional activity known in the industry, the claims are not directed to an improvement to the technology. The well-understood, routine and conventional activities are not sufficient to amount to significantly more than the judicial exception.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 102 rejection:
Applicant argues that Ganguly does not teach “dynamically controlling execution parameters of a cognitive training task during runtime.
In response, Examiner submits that the 35 USC 102 rejection has been updated in light of the amendments submitted. Accordingly, Ganguly discloses this feature in col. 20, line 36 to col. 21, line 3. Ganguly teaches “…a range of stimulation parameters are used. A key goal of the paradigm is the use of a feedback system to titrate parameters to each individual. Thus, the range of parameter sweeps to be tested are outlined in order to arrive at the customized range. A variety of machine learning and statistical techniques are used to customize parameters…In regard to the specific stimulation parameters, a stimulation frequency is used that ranges from 0.001 to 1000 Hz. This in inclusive of all sub frequencies (e.g., 10, 10.1, . . . 10.9,11). The stimuli may adapt parameters with a block design (e.g., stimulation of a frequency “sweep” starting at 5 Hz and ending at 20 Hz; another example is a burst mode where two separate fixed bursts are employed)… Various parameters influence the development of an efficacious stimulation or treatment algorithm that may be personalized to individual need and performance.” in col. 20, line 36 to col. 21, line 3.
Therefore, the argument is not persuasive.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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