DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-19 are currently pending. Claim 20 has been cancelled. Claims 1, 16, 18, and 19 have been amended. Claim 16 has been amended to overcome the claim objection set forth in the Non-Final Office Action mailed on 21 October 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 18, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 2-17 are further rejected due to their dependency to claim 1.
Claims 1, 18, and 19 recite “wherein the trained brain-physiological model determines brain energy increases and decreases based on the second sensor data independently of the electroencephalography sensor and only with the first sensor data.” It is unclear if the model determines the brain energy increases and decreases based on only the second sensor data or based on either the second sensor data or the first sensor data.” Clarification is requested. For examination purposes, this limitation is interpreted as “wherein the trained brain-physiological model determines brain energy increases and decreases based on the second sensor data independently of the electroencephalography sensor.”
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of structural elements, including a sensor interface circuitry. Thus, the claim is directed to a machine, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
wherein, in the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data,
wherein the sensor interface circuitry is further configured to receive the second sensor data in an operation mode, and
wherein, in the operation mode, the processing circuitry is configured to determine the information about the user’s brain resources by processing the second sensor data from a wearable device with the trained brain-physiological model, wherein the trained brain-physiological model determines brain energy increases and decreases based on the second sensor data independently of the electroencephalography sensor and only with the first sensor data
set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites determining the information about the user’s brain resources by processing the second sensor data from a wearable device with the trained brain-physiological model, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The determining of the information about the user’s brain resources does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the determined information about the user’s brain resources, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of wherein, in a calibration mode, the sensor interface circuitry is configured to: receive first sensor data from an electroencephalography sensor only during calibration mode, the first sensor data being indicative of an electroencephalogram of the user including electrical signals measured from a scalp of the user; and receive second sensor data from a physiological sensor, the second sensor data being indicative of a physiological property of the user. Obtaining data (first sensor data, second sensor data) is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 18-20.
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited electroencephalography sensor and physiological sensor are generic sensors configured to perform pre-solutional data gathering activity and the processing circuitry is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. Wisbey et al. ‘222 also shows that the sensor interface circuitry (EEG sensor and physiological sensor) and the processing circuitry are well-understood, routine, and conventional (WURC), as seen in the 35 U.S.C. 103 rejection below.
The dependent claims also fail to add something more to the abstract independent claims. Claims 2-5, 9-11, and 13-15 recite steps that add to the Abstract Idea as each recite a step that could be performed mentally or by hand. Claims 6-8 and 16 recite additional elements that are not significantly more than the Abstract Idea (35 U.S.C. 103 rejections show that the sensors are WURC.). Claims 12 and 17 recite additional elements (mere outputting step) that does not integrate the judicial exception into a practical application. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claims 1-9 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wisbey et al. ‘222 (US Pub No. 2020/0401222 – previously cited) in view of Zhang et al. ‘716 (US Pub No. 2022/0039716) further in view of Boissoneault et al. (“Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome” – 2018).
Regarding claim 1, Wisbey et al. ‘222 teaches an apparatus for providing information about a user’s brain resources (Abstract), the apparatus comprising at least sensor interface circuitry (Fig. 1 gaming headset 110 and [0084]; “the gaming headset 110 is configured with one or more physiological sensor adapted to measure a physiological parameter of the gamer 105 physiological response to playing the game”) and processing circuitry coupled to the sensor interface circuitry (Fig. 3 processor 305 and [0093]),
wherein, in a calibration mode, the sensor interface circuitry is configured to:
receive first sensor data from an electroencephalography sensor, the first sensor data being indicative of an electroencephalogram of the user (Fig. 1 EEG 130 and [0084]) including electrical signals measured from a scalp of the user (Fig. 1 headset 110 and [0084]); and
receive second sensor data from a physiological sensor, the second sensor data being indicative of a physiological property of the user (Fig. 2 PPG 135, HRV 125 and [0084]),
wherein, in the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on a relationship between the first sensor data and the second sensor data ([0043]; “Power (fatigue)”, [0052]; “Power is a prediction of mental fatigue based on EEG and HRV.”, [0084]; “the gaming headset 110 creates the updated machine learning model 160 based on training the baseline machine learning model 140 as a function of the predicted cognitive performance and the HRV 125, EEG 130, and PPG 135 data.”),
wherein the sensor interface circuitry is further configured to receive the second sensor data in an operation mode ([0065]; “determining the gamer's current state of cognitive fatigue based on comparing captured electroencephalogram, heart rate variability, or photoplethysmogram (PPG) data with reference data representative of normal levels”),
wherein, in the operation mode, the processing circuitry is configured to determine the information about the user’s brain resources by processing the second sensor data from the wearable device with the trained brain-physiological model ([0084]; “the gaming headset 110 predicts the gamer 105 cognitive fatigue risk determined as a function of the baseline machine learning model 140”).
Wisbey et al. ‘222 teaches all of the elements of the current invention as mentioned above except for wherein, in the calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor only during calibration mode.
Zhang et al. ‘716 teaches that EEG has low signal-to-noise ratio and spatial resolution, and is easy to cause signal mixing problems. In order to effectively promote the detection techniques of mental fatigue, there is an urgent need to introduce an internal mechanism of fatigue complexity evolution to improve the reliability of the calibration for the critical points of fatigue ([0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified receiving the first sensor data from an electroencephalography sensor of Wisbey et al. ‘222 to include being only during calibration mode as Zhang et al. ‘716 teaches that this will aid in improving the reliability of the calibration for the critical points of fatigue.
Wisbey et al. ‘222 in view of Zhang et al. ‘716 teaches all of the elements of the current invention as mentioned above except for wherein the trained brain-physiological model determines brain energy increases and decreases based on the second sensor data independently of the electroencephalography sensor and only with the first sensor data.
Boissoneault et al. teaches analyzing HRV from ECG measurements (pages 790-791, “HRV analysis”). It was found that the relationship between measurements of HRV and fatigue can be informative because HRV represents a physiological marker of ANS functioning and resilience and can be used as a proxy to measure the extent to which ANS function might contribute to CFS symptoms (page 797).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the trained brain-physiological model of Wisbey et al. ’222 to include determining brain energy increases and decreases based on the second sensor data independently of the electroencephalography sensor and only with the first sensor data as Boissoneault et al. teaches that this will aid in measuring the extent to which ANS function might contribute to CFS symptoms.
Regarding claim 2, Wisbey et al. ‘222 teaches wherein, for training the brain-physiological model, the processing circuitry is configured to personalize a raw brain-physiological model based on the first sensor data ([0044]; “Trained models may be applied across all users as the input data has been personalized prior to training the model.”, [0045]; “This personalization of data helps to ensure all data is relevant to the individual user.”, [0051]; “Power may be determined using personalized variables to predict mental fatigue using a machine learning model, with post-processing of the predicted value for improved accuracy.”).
Regarding claim 3, Wisbey et al. ‘222 teaches wherein, for personalizing the raw brain-physiological model, the processing circuitry is configured to determine, based on the first sensor data, a maximum available brain energy of the user and user specific brain energy increase and decrease characteristics ([0064]; “Some examples may increase a user's knowledge of the level of mental energy the user has available to perform at their best. Such increased knowledge of a user's cognitive energy level may be a result of a system configured to measure cognitive fatigue, determining how much mental resource the user has available to continue to achieve a challenging task.”).
Regarding claim 4, Wisbey et al. ‘222 teaches wherein, for training the brain-physiological model, the processing circuitry is configured to:
determine a relation between the first sensor data and the second sensor data (Fig. 4 step 405 and [0100]; The EEG, HRV, and PPG data are related as all are measured simultaneously as the user plays a game.); and
determine a relation between the second sensor data and the specific brain energy increase and decrease characteristics based on the relation between the first sensor data and the second sensor data ([0125]; “when cognitive fatigue increases”).
Regarding claim 5, Wisbey et al. ‘222 teaches wherein, in the calibration mode, the sensor interface circuitry is further configured to receive contextual data from at least one contextual sensor, the contextual data being indicative of an activity performed by the user ([0097]; accelerometer, GPS module, gyroscope module, motion sensor), and wherein the processing circuitry is configured to train the brain-physiological model further based on the contextual data ([0122]; “Some embodiments may use EEG, PPG and motion sensors embedded in a headset to capture EEG, HRV and movement data. This data is then manipulated and individualized to compare to the users historic profile to determine normal levels under various conditions. Personalized feedback is then provided to the user on their cognitive fatigue.”).
Regarding claim 6, Wisbey et al. ‘222 teaches wherein the at least one contextual sensor is an acceleration sensor, and wherein the contextual data are indicative of a respective movement of one or more body part of the user ([0097]; accelerometer).
Regarding claim 7, Wisbey et al. ‘222 teaches wherein the at least one contextual sensor is a position sensor, and wherein the contextual data are indicative of a geolocation of the user ([0097]; GPS module).
Regarding claim 8, Wisbey et al. ‘222 teaches wherein the second sensor data is sensor data of a laser Doppler flowmetry sensor, a photoplethysmography sensor (Fig. 1 PPG 135 and [0084]), an electrocardiography sensor ([0045]; ECG or PPG) or a galvanic skin response sensor.
Regarding claim 9, Wisbey et al. ‘222 teaches wherein the brain-physiological model is a machine-learning model (Fig. 1 machine learning model 140 and [0084]).
Regarding claim 14, Wisbey et al. ‘222 teaches wherein, if the first sensor data is received by the sensor interface circuitry while the apparatus is in the operation mode ([0027]; EEG), the processing circuitry is, in the operation mode, configured to train the brain-physiological model based on the first sensor data received while the apparatus is in the operation mode ([0027]; training).
Regarding claim 15, Wisbey et al. ‘222 teaches wherein in the operation mode:
the sensor interface circuitry is further configured to receive contextual data from at least one contextual sensor, the contextual data being indicative of an activity performed by the user ([0122]; motion sensors, movement data); and
the processing circuitry is configured to determine the information about the user’s brain resources by processing the contextual data in addition to the second sensor data with the trained brain-physiological model ([0122]; “This data is then manipulated and individualized to compare to the users historic profile to determine normal levels under various conditions. Personalized feedback is then provided to the user on their cognitive fatigue.”).
Regarding claim 16, Wisbey et al. ‘222 teaches wherein the information about the user’s brain resources is one or more of the following:
a respective estimated available brain energy for one or more daytime ([0043]; “Focus (concentration)”, [0064]; “level of mental energy the user has available to perform at their best”);
one or more type of brain resources unconsciously consumed by the user;
a history of brain resources usage; and
an expected point of time at which the user reaches a state of brain fatigue.
Regarding claim 17, Wisbey et al. ‘222 teaches a user interface, wherein, in the operation mode, the user interface is configured to output the determined information about the user’s brain resources (Fig. 1 mobile device 170, mobile app 175 and [0084]; “the mobile device 170 includes the mobile app 175. In the illustrated embodiment, the mobile app 175 is configured to present the user with cognitive performance alerts and status received from the gaming headset 110. In the depicted embodiment, the mobile app 175 displays the user 105 cognitive load 180 received from the gaming headset 110.”).
Regarding claim 18, Wisbey et al. ‘222, as modified by Zhang et al. ‘716 and Boissoneault et al., teaches a computer-implemented method for providing information about a user’s brain resources, as claimed, as claim 18 is analogous to claim 1.
Regarding claim 19, Wisbey et al. ‘222, as modified by Zhang et al. ‘716 and Boissoneault et al., teaches a non-transitory machine-readable medium having stored thereon a program having a program code for perform the method according to claim 18, when the program is executed on a processor or a programmable hardware ([0091]; “processor-executable program instructions”).
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. further in view of Aimone et al. ‘625 (US Pub No. 2020/0337625 – previously cited).
Regarding claim 10, Wisbey et al. ‘222 teaches wherein the apparatus further comprises a user interface (Fig. 1 mobile device 170, mobile app 175 and [0084]; “the mobile device 170 includes the mobile app 175. In the illustrated embodiment, the mobile app 175 is configured to present the user with cognitive performance alerts and status received from the gaming headset 110. In the depicted embodiment, the mobile app 175 displays the user 105 cognitive load 180 received from the gaming headset 110.”).
Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. teaches all of the elements of the current invention as mentioned above except for wherein in the calibration mode:
the user interface is configured to:
output a questionnaire regarding a physiological and/or cognitive status of the user as subjectively perceived by the user; and
receive a user feedback to the questionnaire, and the processing circuitry is configured to train the brain-physiological model for the user further based on the user feedback to the questionnaire.
Aimone et al. ‘625 teaches a feedback mechanism that includes questionnaires. These entries can serve as annotations to label the user-data. As an example, if a user performs an event-related potential (ERP) session at the end of the day and reports in the journal entry of being ‘tired/fatigued.’ Then this information can be used to model the user's brain responses into categories such as ‘normal’, ‘alert’, ‘tired/fatigued’. Another example could involve a journal entry at the end of a full night's sleep in which a user reports of being ‘refreshed’ or ‘fatigued’ ([0126]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the user interface of Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. to include outputting a questionnaire regarding a physiological and/or cognitive status of the user as subjectively perceived by the user; and receiving a user feedback to the questionnaire, and the processing circuitry is configured to train the brain-physiological model for the user further based on the user feedback to the questionnaire as Aimone et al. ‘625 teaches that this would aid in refining generalized algorithms that would yield higher performance, quicker adaptation, and greater consistency ([0128]).
Regarding claim 11, Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. further in view of Aimone et al. ‘625 teaches all of the elements of the current invention as mentioned above except for wherein in the operation mode:
the user interface is configured to:
output a second questionnaire regarding the physiological and/or cognitive status of the user as subjectively perceived by the user; and
receive a second user feedback to the second questionnaire, and the processing circuitry is configured to train the brain-physiological model based on the second user feedback to the second questionnaire.
Aimone et al. ‘625 teaches a feedback mechanism that includes questionnaires. These entries can serve as annotations to label the user-data. As an example, if a user performs an event-related potential (ERP) session at the end of the day and reports in the journal entry of being ‘tired/fatigued.’ Then this information can be used to model the user's brain responses into categories such as ‘normal’, ‘alert’, ‘tired/fatigued’. Another example could involve a journal entry at the end of a full night's sleep in which a user reports of being ‘refreshed’ or ‘fatigued’ ([0126]). The data collector may be configured to receive repeated measures. Repeated measures are measurements taken over multiple points of time ([0129]). Repeated measures over different timescales may be used for revising brain models. This may be achieved by analysis of timestamped EEG signals and activity ([0131]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the user interface of Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. further in view of Aimone et al. ‘625 to include outputting a second questionnaire regarding the physiological and/or cognitive status of the user as subjectively perceived by the user; and receiving a second user feedback to the second questionnaire, and the processing circuitry is configured to train the brain-physiological model based on the second user feedback to the second questionnaire as Aimone et al. ‘625 teaches that this would aid in refining generalized algorithms that would yield higher performance, quicker adaptation, and greater consistency ([0128]).
Regarding claim 12, Wisbey et al. ‘222 in view of Zhang et al. ‘716 further in view of Boissoneault et al. further in view of Aimone et al. ‘625 teaches all of the elements of the current invention as mentioned above except for herein, in the operation mode, the user interface is configured to output the second questionnaire repeatedly throughout the day.
Aimone et al. ‘625 teaches selection of intervals/repeated measures may depends on the situation. For example, when learning to swing a golf club, a user may improve over half an hour, and could also improve over longer time scale—year or two. The brain model may be updated and revised over each appropriate time scale, while tracking events at repeated measures ([0132]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to try outputting the second questionnaire repeatedly throughout the day as Aimone et al. ‘625 teaches that repeated measures may be every half hour to a year or two, as choosing the repeated measures would be choosing from a finite number of identified, predictable solutions, with reasonable expectation of success (the range between every half hour to every year or two are identified, predictable solutions).
Response to Arguments
Applicant argues that the claims have been amended to recite that the first sensor data is measured from the scalp of the user. However, this step is pre-solutional data gathering activity. Applicant also argues that amended claim 1 integrates a technological improvement to brain monitoring devices as the claim recites that the first sensor data is only received during the calibration mode and the train brain-physiological model determines brain energy increases and decreases based on the second sensor data independently of the EEG sensor. However, as mentioned previously in the Examiner Interview conducted on 16 December 2025, Examiner suggested to amend the claim to recite that the EEG sensor is separate from the wearable device. However, there is no indication as to the EEG sensor being separate from the wearable device. Claim 1 requires the EEG sensor to measure electrical signals from the scalp of the use. Therefore, the claim may be interpreted so the EEG sensor is on the wearable device. Furthermore, regarding the determining step, this step is seen as a mental process, which is an abstract idea (see 101 analysis above). It is noted that section 2106.05(a) II. of the MPEP states that “…it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” As such, Applicant’s arguments are not persuasive and the 35 U.S.C. 101 rejection has been maintained.
Applicant’s arguments with respect to the 35 U.S.C. 102(a)(1) rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AURELIE H TU whose telephone number is (571)272-8465. The examiner can normally be reached [M-F] 7:30-3:30.
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/AURELIE H TU/ Primary Examiner, Art Unit 3791