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 .
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.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claims 6-7, 12, 18-19, and 24 are objected to because of the following informalities: typographical and grammatical errors.
The limitation “at sensorimotor zone electrodes, at Mu-frequency” recited in Claim 6 should recite “at sensorimotor zone electrodes at Mu-frequency”.
The comma after the phrase “electrodes” is not needed and actually breaks up the flow of the limitation.
The limitation “at parietal-zone electrode” recited in Claim 7 should recite “at a parietal-zone electrode”.
The limitations “an athletic stadium an operating theatre” and “any surgical environment an aircraft cockpit” recited in Claim 12 should include additional commas (,) between limitation elements such as “an athletic stadium, an operating theater” and “any surgical environment, an aircraft cockpit”. Additionally, the limitations “vehicle control center” and “flight control center” recited in the last two lines of Claim 12 should be separated by a conjunction phrase such as “and” or “or” to showcase that the list is complete.
The limitation “-in response to” recited in Claim 18 should not contain the hyphen (-) and should recite “in response to”.
The limitation “at parietal-zone electrode” recited in Claim 19 should recite “at a parietal-zone electrode”.
The limitations “an athletic stadium an operating theatre” and “any surgical environment an aircraft cockpit” recited in Claim 24 should include additional commas (,) between limitation elements such as “an athletic stadium, an operating theater” and “any surgical environment, an aircraft cockpit”. Additionally, the limitations “vehicle control center” and “flight control center” recited in the last two lines of Claim 24 should be separated by a conjunction phrase such as “and” or “or” to showcase that the list is complete.
It is noted by the examiner that a lot of the claims, some addressed above and others that are not, contain additional spacing between words that can be eliminated or reduced in order to enable for an easier read.
Appropriate correction is required.
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 6, 12, 18-19, and 23-24 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.
Regarding Claim 6, the limitation “obtained from said trainee in response to a visual stimulus presented on a display below said electroencephalographic signals at sensorimotor zone electrodes measured at rest” is indefinite. It is unclear what element is “below” another element within this limitation. Is the value obtained “in response to a visual stimulus” acting as a denominator in the beforementioned “ratio” earlier in the claim and therefore the “at rest” value would act as a numerator? Is the “visual stimulus” itself presented on a display below the obtained signals? Are the signals being shown on a visual stimulus? The specification fails to provide further clarity into this interpretation and therefore this claim remains indefinite.
Regarding Claim 12, the limitation “the group consisting of” lacks proper antecedent basis. This limitation is being interpreted to mean “a group consisting of”.
Regarding Claim 18, the limitation “obtained from said trainee in response to a visual stimulus presented on a display below said electroencephalographic signals at sensorimotor zone electrodes measured at rest” is indefinite. It is unclear what element is “below” another element within this limitation. Is the value obtained “in response to a visual stimulus” acting as a denominator in the beforementioned “ratio” earlier in the claim and therefore the “at rest” value would act as a numerator? Is the “visual stimulus” itself presented on a display below the obtained signals? Are the signals being shown on a visual stimulus? The specification fails to provide further clarity into this interpretation and therefore this claim remains indefinite.
Regarding Claim 19, the limitation “calculating a ratio of excess signals” is indefinite. It is unclear what a “ratio of excess” entails and how this is obtained or calculated. For this examination, this limitation is interpreted to mean “calculating a ratio of change” given that the instant application’s specification does not provide any further insight or explanation as to what “a ratio of excess” entails.
Regarding Claim 23, the limitation “the group consisting of” lacks proper antecedent basis. This limitation is being interpreted to mean “a group consisting of”.
Regarding Claim 24, the limitation “the group consisting of” lacks proper antecedent basis. This limitation is being interpreted to mean “a group consisting of”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, 12-14, 20, 23-26, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone et. al.'547 (U.S. Patent Publication 20160077547) in view of Xie et. al.'422 (CN Patent Application 113398422 - cited by applicant).
Regarding Claim 1, Aimone et. al.'547 discloses a system for testing and training a brain capability of a trainee for planning and executing motion activity (Paragraph [0024] - Embodiments described herein may involve measuring and analyzing bio-signals such as brainwave patterns for a variety of practical applications including improving the training of humans for performing certain tasks or functions; Paragraph [0025] - Embodiments described herein may provide an integrated computing device or system with an interactive VR environment with visual output data to provide a computer simulation of physical elements for training; Paragraph [0060] - The user profile data may be analyzed, such as by machine learning algorithms, either individually or in the aggregate to function as a brain computer interface (BCI), or to improve the algorithms used in the analysis; Paragraph [0118] - The process iteratively continues to improve the environment and motion within to optimize for positive experiences. is the system may be able to release a game that takes a variety of physical responses into account, so that more people can play); said system comprising:
an electroencephalographic sensor arrangement attachable to a head of said trainee (Paragraph [0058] - Sensors positioned along the straps may be specifically configured to travel a distance from the strap, past the user's hair, if any, to the user's scalp. Accordingly, any such sensors may include an elongated contact area, which is optionally of a resilient construction. The facial bio-signal sensors 130 measure, include, electrical bio-signals such as EEG);
a processor configured for receiving and analyzing electroencephalographic signals obtained from said trainee (Paragraph [0039] - the processor is configured to identify a portion of the bio-signal data based on the event time data and process the portion of the bio-signal data to determine the user states during the VR event, the bio-signal time data synchronized to the event time data using a common timeline or clock or a synchronization or context operation);
a memory storing instructions when executed by said processor for (Paragraph [0027] - a processor may be connected to memory);
instructing said trainee to imagine executing techniques (Paragraph [0060] - The user profile data may be analyzed, such as by machine learning algorithms, either individually or in the aggregate to function as a brain computer interface (BCI), or to improve the algorithms used in the analysis; Paragraph [0167] - Guided meditations can be programmed so that instructions issued by the group leader are interpreted visually. i.e. the meditation leader says, “Imagine a beam of bright white light emanating from the top of your heat. This light pulses in time with the rhythm of your breath. This visual metaphor can be translated literally onto the avatars in the meditation group; users can see beams of light emanating from the tops of the heads of the other participants in the group);
measuring electroencephalographic signals on said electroencephalographic sensor arrangement (Paragraph [0062] - the wearable device 105 may implement a method that may involve acquiring bio-signal measurement from a user using the bio-signal measuring sensor during a VR event);
calculating at least one characteristic selected from the following: a concentration index; a motor control index; an alertness index (Paragraph [0119] - Embodiments described herein may permit such inputs as: a user's motor activity, emotional activity, changes in brain-state, focus, attention, vigilance, drowsiness, nausea, and other responses. The system of the present invention creates such outputs as: raw metrics and data of user interaction, such as movement, changes in focus and attention, changes in wakefulness, and other data that contribute to a portrait of user engagement);
providing said trainee with a feedback pattern based on at least one of the following indicators: an accuracy of the motion action based on said concentration index, a power of the motion action based on said motor control index, or motion readiness based on said alertness index (Paragraph [0119] - This data could then be translated into reports, infographics, and other valuable internal information for creating better experiences; Paragraph [0131] - If Liana maintains her heart rate and focus (detected through by the system), the environment stays in place as feedback. If she loses focus or her heart rate, the environment begins to fade away as further feedback. In another embodiment, the greater Liana's engagement gives her additional power to play a game in the VR environment; Paragraph [0135] - Embodiments described herein can track data inputs related to focus and attention, such as EEG sensing, eye-tracking, heart rate, and motion, and translate it into outputs like dynamic changes to a virtual environment, text messages, virtual prompts, and reports on attention);
iterating steps ii to iv (Paragraph [0027] - it should be noted that some embodiments include multiple iterations of a technique; Paragraph [0083] - The user 305 optionally repeats the training content from the VR Application 315 until the user 305 passes the threshold score 460 and/or until the user 305 is satisfied with his or her performance; Paragraph [0095] - As Bill plays through multiple scenarios generated as VR events within the interactive VR environment, the system logs his Brain State responses (user states 425) during the VR events; Paragraph [0154] - The EEG-reading device in the VR headset captures their moods and thought processes during each iteration of change that an application wishes to test); and
generating a dynamic visual representation of the motion action being executed by an avatar in a virtual environment according to at least one of the power, accuracy, and motion readiness obtained during the iterations (Paragraph [0042] - The feedback may be provided as a visual representation in the VR environment to have effects in the VR environment, including during the VR event to provide visual, real time or near real time feedback; Paragraph [0077] - Feature selection selects the features of the bio-signal data that optimize the accuracy of the user state estimator 325 for predicting the brain state of the user; Paragraph [0131] - If Liana maintains her heart rate and focus (detected through by the system), the environment stays in place as feedback. If she loses focus or her heart rate, the environment begins to fade away as further feedback. In another embodiment, the greater Liana's engagement gives her additional power to play a game in the VR environment; Paragraph [0175] - Embodiments described herein may provide a dynamic, responsive virtual environment that helps train professionals).
Aimone et. al.'547 fails to explicitly disclose instructing said trainee to imagine executing said motion action. However, the examiner notes that they believe it would be reasonable that one of ordinary skill in the art at the time the invention was effectively filed would understand that Aiomone et. al.’547 which discloses a device comprising a brain computer interface (BCI) would involve a user to imagine motion actions. Nonetheless, the examiner makes note of another application, Xie et. al.'422, that explicitly teaches instructing a user to execute motion actions while using a system comprising brain computer interface (Paragraph [n0004] Lines 58-59 - The brain-machine interface based on motion imagery can directly convert the brain neural activity signal into the control signal of the computer or external device; Paragraph [n0045] Lines 372-374 - basic control mode of the training task is subject to motion imagination according to the prompt, when identifying the subject imagined left (right), the corresponding limb in the scene finish the corresponding action; Paragraph [n0044] Lines 360-361 - after performing the feature extraction, establishing a motion imagination intention identification model as a default identification model of the rehabilitation training system). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.’547 to include instructing a user to execute motion-related actions upon instruction in order to create baseline data used to train a motion identification model of a system to accurately detect imagined motion actions performed by a user that can be depicted as intended motion actions in a virtual reality realm as seen in Xie et. al.’422. The examiner additionally notes that the relied upon paragraphs of Xie et. al.’422 by the examiner refer to the locations by which these elements are recited in the English Translation of the Foreign Reference provided by the applicant on 06 January 2025.
Regarding Claim 13, the sections of Aimone et. al.'547 in view of Xie et. al.'422 cited above disclose a method comprising the elements set forth in the claim.
The examiner notes that although Aimone et. al.’547 includes a variety of embodiments, the elements relied upon the examiner are not limiting to those specific embodiments of the invention and this is supported within paragraphs [0023-0024], [0026], and [0183] of Aimone et. al.’547.
Regarding Claim 2, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above. Aimone et. al.’547 further discloses comprising a display configured for providing a visual stimulus to said trainee, and said memory includes instructions for presenting a visual message and/or playing an audio message for instructing the trainee to imagine executing techniques in response to visual stimuli (Paragraph [0060] - One or more user effectors may also be provided at the wearable device or other local computing device for providing feedback to the user, for example, to vibrate or provide some audio or visual indication to assist the user; Paragraph [0114] - For example, Bill enters a virtual world designed for exploration and experience via a wearable computing device. Rather than using a handheld controller, he determines his velocity in 3D-space by his focus/surprise/noticing his environment. Instead of an easy feeling of control, the environment seems to take him around and respond to his brain states in real time as example feedback based on his scored brain state. Bill gets bored of flying around the space, and as he loses attention and relaxes, aspects of the VR environment change, and his motion and velocity drastically slow down; Paragraph [0167] - Guided meditations can be programmed so that instructions issued by the group leader are interpreted visually. i.e. the meditation leader says, “Imagine a beam of bright white light emanating from the top of your heat. This light pulses in time with the rhythm of your breath. This visual metaphor can be translated literally onto the avatars in the meditation group; users can see beams of light emanating from the tops of the heads of the other participants in the group), but fails to explicitly disclose presenting a visual message and/or playing an audio message instructing the trainee to imagine executing said motion activity in response to displaying said visual stimulus. Xie et. al.'422 teaches instructing a user to execute motion actions (basic control mode of the training task is subject to motion imagination according to the prompt, when identifying the subject imagined left (right), the corresponding limb in the scene finish the corresponding action …after performing the feature extraction, establishing a motion imagination intention identification model as a default identification model of the rehabilitation training system; Paragraph [0175] - Embodiments described herein may provide a dynamic, responsive virtual environment that helps train professionals). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.’547 to include instructing a user to execute motion-related actions upon instruction in order to create baseline data used to train a motion identification model of a system to accurately detect imagined motion actions performed by a user that can be depicted as intended motion actions in a virtual reality realm as seen in Xie et. al.’422.
Regarding Claim 14, the sections of Aimone et. al.'547 in view of Xie et. al.'422 cited above disclose a method comprising the elements set forth in the claim.
Regarding Claim 8, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above. Aimone et. al.’547 further discloses wherein said memory comprises an instruction of analyzing at least one of said concentration index, motor control index and alertness index of said trainee or a group of said trainees and presenting training progress data in a chronological manner (Paragraph [0038] - provide real time or near real time feedback to the user during the presentation of the content; Paragraph [0039] - In some embodiments, the VR event is associated with event time data and a portion of bio-signal data is associated with bio-signal time data corresponding to the event time data, wherein the processor is configured to identify a portion of the bio-signal data based on the event time data and process the portion of the bio-signal data to determine the user states during the VR event, the bio-signal time data synchronized to the event time data using a common timeline or clock or a synchronization or context operation; Paragraph [0165] - This can allow Chenelle to see how her meditation is progressing, i.e. whether she is meeting her meditation goals in terms of relaxation, etc. Chenelle can then optimize her meditation practice to meet specific goals by modifying her breathing or some other variables to create a different outcome during the meditation practice).
Regarding Claim 20, the sections of Aimone et. al.'547 in view of Xie et. al.'422 cited above disclose a method comprising the elements set forth in the claim.
Regarding Claim 23, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 13 above. Aimone et. al.’547 further discloses wherein said feedback pattern relates to a visual environment selected from the group consisting of a sports motion action, a surgical action or a flight control action further wherein said motion action is an action directly concerned with executing a sports activity, a surgical manual or physical action, a flight control, joystick, rudder or other flight motion activity or any activity requiring physical motion of the limbs and eye coordination (Paragraph [0175] - Kim downloads a simulation of the surgery and uses the system to develop skills at performing difficult parts of the surgery. He learns the rhythm of the surgery, as well as how to handle surprises like losses in blood pressure or sudden changes in heart rate. The system generates a report for Kim at the end of each session to help him see when he felt most anxious, and which parts of the surgery he might feel most anxious about. The report also tells him when his mind wanders or when he's distracted during the surgery simulation. When Kim actually performs the surgery in real life, he feels a great deal more confident and calmer. The surgery is successful).
Regarding Claim 24, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 13 above. Aimone et. al.’547 further discloses wherein said visual environment is selected from the group consisting of a soccer stadium, a baseball stadium, a basketball hall, a rugby stadium, an athletic stadium an operating theatre, dental operating room, in situ emergency environment or any surgical environment, an aircraft cockpit, unmanned airborne vehicle control center, flight control center (Paragraph [0175] - Kim downloads a simulation of the surgery and uses the system to develop skills at performing difficult parts of the surgery. He learns the rhythm of the surgery, as well as how to handle surprises like losses in blood pressure or sudden changes in heart rate. The system generates a report for Kim at the end of each session to help him see when he felt most anxious, and which parts of the surgery he might feel most anxious about. The report also tells him when his mind wanders or when he's distracted during the surgery simulation. When Kim actually performs the surgery in real life, he feels a great deal more confident and calmer. The surgery is successful).
Regarding Claim 25, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 13 above. Aimone et. al.’547 further discloses comprising steps of calculating an integral index of sports, surgical or flight control readiness as a compound of at least two indexes selected from the group consisting of said concentration index, motor control index and alertness index and normalized by a sum thereof (Paragraph [0087] - The score 450 is a combination of user state score and performance score to provide a combined, for example, numerical score and constructive feedback to the user specific to the content of the VR Application for which he or she is being trained; Paragraph [0107] – entire paragraph - The training system 100 presents Geoff with his score 450 along with his user state 425 and desired user state 435 as part of the feedback 470 showing both mentally and physically he may improve his performance for next time; Figure 4 below showcases "user states" that entail "brain states" and “biosignals” measured by an EEG feeding into an "overall score" section).
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Figure 4
Regarding Claim 26, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above. Aimone et. al.’547 further discloses wherein said memory includes instructions for feeding said electroencephalographic signals into a machine learning model, and obtaining detected patterns robust to EEG noise from the machine learning model, wherein at least one of the concentration index, the motor control index, and the alertness index, is computed according to the detected patterns robust to EEG noise (Paragraph [0059] - Measurement of the EOG signal is also important for noise free interpretation of the EEG signal; ; Paragraph [0060] - The user profile data may be analyzed, such as by machine learning algorithms, either individually or in the aggregate to function as a brain computer interface (BCI), or to improve the algorithms used in the analysis; Paragraph [0076] -The Biological Signal Acquisition 340 acquires the bio-signal data, including brain state signals, from the bio-signal sensors 120 and 130, and such other sensors that attach to the user's body. The bio-signal data, analog signals, are amplified with high signal to noise ratio; Paragraph [0079] - The user profile 335 contains the signal processing parameters tuned to that user 305 and the parameters for the user's specific prediction models. The signal processing parameters are used by the Biological Signal Processing Pipeline 345 to interpret the user's bio-signal data received during a VR event. The parameters for the prediction models are used by the user state estimator 325 to predict a brain state (e.g. drowsy, or agitated etc.) or other user states such as eye position, high muscle tension etc. during the VR event. The user profile 335 may optionally store the history of all of the user's sessions, raw biological data, processed data, demographic data, etc.).
Regarding Claim 28, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above. Aimone et. al.’547 further discloses wherein said memory includes instructions for: computing a baseline threshold based on at least one of the concentration index, the motor control index, and the alertness index calculated during a baseline collection time interval (Paragraph [0149] - Not only are the reports right in front of him when he sits down to read them, the device helps detect his focus and distraction. When Roscoe experiences distraction, the device senses the change in baseline brain state and provides him with stimuli to keep him on task as feedback);
wherein the feedback pattern is generated according to at least one of the concentration index, the motor control index, and the alertness index calculated during the iterations relative to the baseline threshold (Paragraph [0149] - When Roscoe experiences distraction, the device senses the change in baseline brain state and provides him with stimuli to keep him on task as feedback. This stimuli could be a simple chime or sound, or perhaps a colour change, or maybe just a quick reminder text. The device records how long it takes Roscoe to read his way through a document and gives him progress bars to help him understand how he's reading and at what speed).
Regarding Claim 29, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above. Aimone et. al.’547 further discloses wherein said memory includes instructions for dynamically adapting the feedback pattern according to at least one of the calculated concentration index, the motor control index, and the alertness index, compared to a set difficulty level indicating a challenge level to the trainee's abilities (Paragraph [0089] - if the user state score is low and the performance score is low then the feedback is for the threshold 460 to not pass the user 305 and, optionally, to send the user 305 back to re-do the contents of the VR application 315. If the user state score is low but the performance score is high then the feedback to the user is to take a revised 480 training where the content of the VR Application 315 is revised to include, for example, more of the specific VR events 430 for which the user 305 is weak within the interactive VR environment…Table 5B is a look up table for determining user state score and performance score. Other scoring techniques may be used and this is an example illustrative example embodiment. This is an example of training a truck driver who is in a car (as part of the VR environment) travelling down the left lane of a highway. There may be a large 18 wheeler truck ahead (an example VR event as part of the VR environment) that is the source of a number of VR events posing challenges to the driver under the training; Paragraph [0175] - Embodiments described herein may provide a dynamic, responsive virtual environment that helps train professional).
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone et. al.'547 (U.S. Patent Publication 20160077547) as applied to Claims 1 and 13 above, and further in view of Lim et. al.'2019 (Comparison between Concentration and Immersion Based on EEG Analysis).
Regarding Claim 5, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above as well as measuring bio-signals including types of brainwaves and a memory configured to personalize algorithms according to data collected from users’ brainwaves (Paragraph [0062] - Various types of bio-signals, including brainwaves, may be measured and used to control the device or the VR environment in various ways; Paragraph [0066] - Each person's brainwaves are different, therefore requiring slightly different tunings for each user. Each person's brain may also learn over time, requiring the system platform to change algorithm parameters over time in order to continue to analyze the person's brainwaves. New parameters may be calculated based on collected data, and may form part of a user's dynamic profile (which may be called bio-signal interaction profile or user profile 335)), but fails to disclose wherein said memory comprises an instruction of calculating said concentration index as a ratio of change of electroencephalographic signals at parietal-zone and frontal-zone electrodes at alpha-, beta- and theta- frequencies obtained from said electroencephalographic signals at parietal-zone and frontal-zone electrodes measured at rest. Lim et. al.'2019 teaches a user’s concentration state being configured based on a calculated ratio of brainwaves across a user’s scalp (Page 2 Paragraph 2 - Srinivasan et al. suggested that the frontal and parietal cortices are activated during concentration using EEC and fMRI measurements; Page 11 Paragraph 5 - In the cases where the concentration index is set as the ratio of the entire Beta band, including high-Beta, to the Theta band (Figure 6c), the increase in Channels 1 and 2 is considerably clear in the concentration state, which implies that it is more desirable to use "the ratio of the Beta and Theta bands" as an index for concentration). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.'547 in view of Xie et. al.'422 to include an algorithm capable of calculating a concentration index based on a ratio of change between brainwave signals obtains across different areas of a user’s scalp in order to obtain a clear indicator of a concentration state as seen in Lim et. al.’2019.
Regarding Claim 17, the sections of Aimone et. al.'547 in view of Xie et. al.'422 and further in view of Lim et. al.’2019 cited above disclose a method comprising the elements set forth in the claim.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone et. al.'547 (U.S. Patent Publication 20160077547) as applied to Claims 1 and 13 above, and further in view of Badower et. al.'403 (U.S. Patent Publication 20160166169).
Regarding Claim 6, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above as well as measuring bio-signals including types of brainwaves and a memory configured to personalize algorithms according to data collected from users’ brainwaves (Paragraph [0062] - Various types of bio-signals, including brainwaves, may be measured and used to control the device or the VR environment in various ways; Paragraph [0066] - Each person's brainwaves are different, therefore requiring slightly different tunings for each user. Each person's brain may also learn over time, requiring the system platform to change algorithm parameters over time in order to continue to analyze the person's brainwaves. New parameters may be calculated based on collected data, and may form part of a user's dynamic profile (which may be called bio-signal interaction profile or user profile 335)), but fails to disclose wherein said memory comprises an instruction of calculating said motor control index as a ratio of change of electroencephalographic signals at sensorimotor zone electrodes at Mu-frequency obtained from said trainee in response to a visual stimulus presented on a display below said electroencephalographic signals at sensorimotor zone electrodes measured at rest. Badower et. al.'403 teaches changes in Mu brainwaves are responsible for identifying movement in the sensorimotor zone (Paragraph [0253] - The system 3900 used to control the electrical device 3922 uses specific EEG signatures that trigger control including, for example, signatures in the somatosensory system that are focal over the sensorimotor cortex contralateral to movement and include changes in mu (e.g., 10-14 Hz) and beta (e.g., 15-30 Hz) rhythms. Based on the EEG and eye tracking data, the remote action evaluator 3908 of the headset 3902 determines that the user wants to move his or her cursor (i.e., mouse) to a different region of the computer screen...EEG signals including changes in somatosensory mu and beta rhythms are also used in other brain machine interface applications). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.'547 in view of Xie et. al.'422 to include an algorithm capable of calculating a motor index based on a ratio of change between Mu brainwave signals obtained in the sensorimotor cortex region in order to obtain a clear indicator of intended motion as seen in Badower et. al.'403.
Regarding Claim 18, the sections of Aimone et. al.'547 in view of Xie et. al.'422 and further in view of Badower et. al.'403 cited above disclose a method comprising the elements set forth in the claim.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Aimone et. al.'547 (U.S. Patent Publication 20160077547) as applied to Claims 1 and 13 above, and further in view of Barry et. al.'2007 (EEG differences between eyes-closed and eyes-open resting conditions).
Regarding Claim 7, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above as well as measuring bio-signals including types of brainwaves and a memory configured to personalize algorithms according to data collected from users’ brainwaves (Paragraph [0062] - Various types of bio-signals, including brainwaves, may be measured and used to control the device or the VR environment in various ways; Paragraph [0066] - Each person's brainwaves are different, therefore requiring slightly different tunings for each user. Each person's brain may also learn over time, requiring the system platform to change algorithm parameters over time in order to continue to analyze the person's brainwaves. New parameters may be calculated based on collected data, and may form part of a user's dynamic profile (which may be called bio-signal interaction profile or user profile 335)), but fails to disclose wherein said memory comprises an instruction for calculating said alertness index as a ratio of change of electroencephalographic signals at a parietal-zone electrode at alpha-frequency obtained from said trainee with open eyes over said electroencephalographic signals at a parietal-zone with closed eyes. Barry et. al.'2007 teaches changes in brainwaves in the parietal zone whenever a user has their eyes opened versus closed are responsible for identifying level of alertness of the user (Page 1 Paragraph 4 - The obtained results confirm the use of mean alpha level as a measure of resting-state arousal under eyes-closed and eyes-open conditions. The focal nature of EEG effects in the other bands suggests that these reflect cortical processing of visual input, producing differences in activation between eyes-closed and eyes-open resting conditions, rather than just the simple increase in arousal level shown in alpha; Page 3 Paragraph 2 - During specific tasks such as reading, increased activity is found in the parietal areas of both hemispheres, focused over language areas bordering the Sylvian fissure (Rebert et al., 1978, Harmony et al., 1990) where processing of the written word takes place. However, there are also changes found across the entire scalp during reading (and other cognitive tasks), which may indicate global arousal effects; Page 3 Paragraph 3 - This has implications for EEG research using eyes-closed and eyes-open resting conditions as baseline estimates, since these may represent contributions from separate processes (global arousal changes versus focal processing effects) affecting the resultant EEG). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.'547 in view of Xie et. al.'422 to include an algorithm capable of calculating an alertness index based on a ratio of change between brainwave signals obtained in the parietal zone whenever a user has their eyes opened versus whenever their eyes are closed in order to obtain a clear indicator of level of alertness as seen in Barry et. al.'2007.
Regarding Claim 19, the sections of Aimone et. al.'547 in view of Xie et. al.'422 and further in view of Barry et. al.'2007 cited above disclose a method comprising the elements set forth in the claim.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Aimone et. al.'547 (U.S. Patent Publication 20160077547) as applied to Claim 1 above, and further in view of Pino et. al.'206 (U.S. Patent Publication 20230178206).
Regarding Claim 27, Aimone et. al.'547 in view of Xie et. al.'422 discloses the system outlined in Claim 1 above as well as a feedback system in communication with a the system comprising the electroencephalographic sensor arrangement (Paragraph [0067] - The wearable device 105 may further be in communication with another computing device, such as a laptop, tablet, or mobile phone such that data sensed by the headset through the sensors may be communicated to the other computing device for processing at the computing device, or at one or more computer servers, or as input to the other computing device or to another computing device…One or more user effectors may also be provided at the wearable device or other local computing device for providing feedback to the user; Paragraph [0068] - The sensors may be of various types, including: electrical bio-signal sensor in electrical contact with the user's skin; capacitive bio-signal sensor in capacitive contact with the user's skin), but fails to disclose wherein said memory includes instructions for monitoring connectivity level of each sensor of the electroencephalographic sensor arrangement, and generating an indication for adding more gel. Pino et. al.'206 teaches providing feedback to a user to provide gel to EEG sensors when a level of impedance is potentially detected (Paragraph [0066] - The app 228 can, via the GUI 230, also guide the patient to perform an impedance check to ensure good electrical contact of the electrodes with the patient's skin and guiding them to use saline solution or other conductive gels or other aids that improve the conductivity (and therefore lower impedance) with the electrodes, and guide the patient to adjust the tension and positioning of the EEG headset 202, guide the patient to connect the EEG headset 202 via wireless means (e.g. Bluetooth or WiFi), present a connectivity alert to the patient if the smartphone is not receiving or losing data packets, or receiving corrupted packets of EEG data from the EEG headset, provide a noise alert to the patient indicating there are sources of biological noise, environmental noise or improper contact with the patient's skin (e.g. headband has moved) that are corrupting or interfering of the EEG signal such that the EEG headset 202 does not receive signals from the patient's brain, and instructs the patient on actions the patient can take to remedy the issues, such as reducing sources of unwanted noise (e.g., stop chewing gum)). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Aimone et. al.'547 in view of Xie et. al.'422 to include providing feedback pertaining to the connectivity and signaling of the sensors in the electroencephalographic sensor arrangement in order for a user of the device to correct connectivity deficiencies and ensure the signal data obtained from the sensors have as little interference and noise as possible as seen in Pino et. al.'206 teaches.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. DeCharms'238 (U.S. Patent Publication 20130211238) discloses a virtual reality device comprising EEG sensors and instructions for a user to perform tasks, DeCharms et. al.'320 (U.S. Patent Publication 20160005320) discloses a virtual reality device comprising EEG sensors and instructions for a user to perform tasks, Badower et. al.'169 (U.S. Patent Publication 20160166169) discloses EEG sensors and instructions to imagine a motion activity, Intrator'906 (U.S. Patent Publication 20170347906) discloses a device comprising EEG sensors and instruct a user to perform a motion action in the mind, Youngblood et. al.'019 (U.S. Patent Publication 20230025019) discloses a virtual reality device comprising EEG sensors and instructions for a user to perform tasks, and Ryu'149 (WO Patent Publication 2018080149) discloses a virtual reality device comprising EEG sensors to assist in rehabilitation.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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