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 .
Claim Status
Claims 1-15 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02-03-2025 has been considered by the examiner.
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.
Claim(s) 1 and 3-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhowmik (US 2024/0285190) in view of McCarthy (US 2021/0344957).
Regarding claims 1 and 14,
Bhowmik teaches a system and corresponding method for predicting a defined motor state from augmented reality headset data for automatic cue activation, the method comprising:
receiving from at least one sensor: i) motion data corresponding to motion of a user; and ii) environmental data relating to the user's location([0010] teaches "the ear-wearable device is configured to record signals from at least one of the motion sensor and the microphone and process the signals to characterize an existing gait of the device wearer."; also see [0091]; [0120] teaches "evaluating signals from sensors, such as at least one of a motion sensor and a microphone"; [0142] teaches "... device 100 can be configured to cross-reference changes in gait with changes in footwear of a device wearer as identified by at least one of signals from a microphone ...different types of footwear can create characteristic sounds associated with foot contact with the ground", thus the microphone detects environmental sounds/data; [0217] teaches sensing ambient conditions around the device wearer to change a tempo of the series of audio cues accordingly; furthermore [0242] teaches a temperature sensor);
determining one or more parameters from the motion data and environmental data ([0128] teaches, "the ear-wearable device 100 can be configured to calculate one or more desired or target gait parameters by evaluating signals from a motion sensor and referencing stored data regarding target gait parameters", thus teaching that the motion sensor/microphone sensed data is processed so as to determine a first set of gait parameters used to determine target parameters);
assigning an output value to a function of the one or more parameters of the motion data and environmental data([0119] teaches calculating one or more desired gait parameters and then provide a series of audio cues to a device wearer consistent with the one or more desired or target gait parameters; [0127] teaches," the one or more ...target parameters includes one or more of a desired or target gait tempo, a gait cadence, step impact magnitude, a left vs. right symmetry value, stride height, or the like.");
comparing the output value ([0132] teaches initiating " a series of audio cues based on detection of a gait with a left-right symmetry variability or statistics (in one or more gait parameters) crossing a threshold value", thus teaching that the output values are utilized in a further comparison);
based on the comparison, determining a likelihood of occurrence of freezing of gait ([0230] teaches the threshold value can be related to the prediction of the occurrence of such events based on a comparison of past gait data associated with the occurrence of such events and current gait data); and
automatically activating one or more types of cues on or via the augmented reality device upon determining that the likelihood of freezing of gait occurring is greater than a customizable threshold ([0132] teaches "the ear-wearable device 100 can be configured to initiate or discontinue a series of audio cues based on a detected activity state as reflected in data from a motion sensor and/or a microphone.")
Bhowmik fails to expressly teach that i) motion data and ii) environmental data are received from at least one sensor of an augmented reality device; and further comparing the output value to a trained data model.
McCarthy teaches a system which captures data relating to repetitive movements and furthermore, devices on which a collector is installed can include augmented reality (AR) devices, virtual reality (VR) devices, tablets, mobile devices, laptop computers, desktop computers, and the like ([0083]); and further teaches an exemplary ANR system 3200 shown in FIG. 32 ([0174]). McCarthy teaches i) motion data and ii) environmental data are received from at least one sensor of an augmented reality device (McCarthy teaches "Receiving an input at the system can include measuring the movements of the person via a sensor to determine the biomechanical parameters of movements (e.g. temporal, spatial, and left/right comparisons).", paragraph [0174]; [0060] teaches "one or more image capture devices 206, such as video cameras, (see FIG. 4) are used with a time-synched video feed"; furthermore the system can include augmented reality (AR) devices, virtual reality (VR) devices, tablets, mobile devices, laptop computers, desktop computers, and the like, [0083] );
determining one or more parameters from motion data ([0041], FIG. 33 is a graphical visualization of measured parameters), and
further comparing the output value to a trained data model (teaches "The dynamic closed-loop rehabilitation platform music therapy system utilizes several deep learning neural networks" in [0110]; fig. 9; [0010] teaches "... a critical thinking algorithm (CTA) module that configures the processor to analyze the time-stamped biomechanical data to determine a temporal relationship of the patient's repetitive movements relative to the visual elements... the processor to dynamically adjust the AR visual and RAS output …”; claim 5 teaches, "determine whether the bio-mechanical data or physiological data measured for the patient meets a training goal parameter".
Before the effective filing date of the invention, it would have been obvious to incorporate Bhowmik's gait-event prediction techniques into McCarthy's augmented reality system, thereby improving its therapeutic effectiveness and increasing the systems responsiveness to gait abnormalities. Such a modification is a predictable use of known gait-prediction techniques within McCarthy's sensor-driven adaptive rehabilitation system.
Regarding claim 3,
Bhowmik teaches that the output value comprises a representation of a user's motor states ([0009] teaches that target gait parameters include various values, see left/right symmetry value at least in-part. As indicated previously, Bhowmik teaches in [0128] output values over time to various parameters for later processing.)
Regarding claim 4,
Bhowmik teaches accessing a user profile to identify the user's historic motor state when walking within a pre-determined environment, wherein the step of assigning an output value to a function of the one or more parameters of the motion data and environmental data also takes into account the data accessed from the user profile ([0131], [0150] teaches calculation of exertion value from sensors based at least upon classifications; [0226] teaches historical information on past gait sessions etc.).
Regarding claim 5,
Bhowmik teaches adjusting the threshold to control the sensitivity of automatic cue activation for that user ([0217] teaches that sensed ambient conditions change the tempo of the series of audio cues provided to the wearer).
Regarding claim 6,
McCarthy teaches that the motion data is captured by at least one sensor measurement series of the augmented reality device and includes one or more of position, acceleration, orientation in space, and direction data streams ([0060] teaches capturing the position of the torso and limbs; [0075] teaches that captured sensor data comprises at least lateral acceleration).
Regarding claim 7,
McCarthy teaches that the motion data is captured by: i) mapping an environment; ii) determining a base line position of the augmented reality headset in the environment; and iii) tracking the position of the augmented reality headset in the environment as the wearer moves in the environment ([0056] teaches obtaining pressure maps; [0058] teaches map-able spatial and temporal gait dynamics via multiple zone pressure sensing; [0131] teaches determining a baseline condition; [0203] and fig. 36B teaches a virtual space.)
Regarding claim 8,
McCarthy teaches that the motion data is captured by at least one inertial measurement unit (IMU) of the augmented reality device and includes one or more of acceleration, orientation in time and space, and direction ([0174] teaches inertial measurement units; [0075] teaches capturing lateral acceleration; [0174] further teaches utilizing accelerometers and gyroscopes at least in-part).
Regarding claim 9,
McCarthy teaches that the environmental data comprises one or more of pre-stored or real-time captured data concerning maps of the environment ([0203] teaches a simulated virtual scene, fig. 36b), defined locations, raycast distances, camera data ([0060]), location data, geospatial data, distance from headset to floor.
Regarding claim 10,
McCarthy teaches that the step of activating one or more types of cues on or via the augmented reality device further comprises accessing a user profile and selecting and activating one or more types of cues that are preferred by the user as indicated in the user profile ([0124] teaches various cue types; [0130] teaches haptic feedback. [0149] in Bhowmik teaches a plurality of cue types (e.g., visual, auditory).
Regarding claim 11,
McCarthy teaches that the step of determining a likelihood of occurrence of freezing of gait comprises assigning a probability distribution label to each of a plurality of motor states and selecting the motor state having the highest probability and determining from the selected motor state a likelihood of occurrence of freezing of gait ([0113]-[0116] teaches the machine learning model is used to predict probabilities of different possible outputs, see [0114]; and outputting a probability distribution across all classes, see [0116]. McCarthy is interpreted as teaching determining probabilities for multiple movement related classifications).
Regarding claim 12,
McCarthy teaches that the selection of the one or more types of cues is made dependent on the determined motor state, or other parameters, of the user ([0055],[0062],[0068],[0085] teach data associated with a profile; [0130] teaches musical cue types selected based on parameters such as birth date and profile preferences).
Regarding claim 13,
McCarthy teaches that the one or more types of cues are selected from visual cues, audible cues, haptic cues, or other cues ([0130] teaches selection of type of musical cue; [0139] teaches selecting haptic feedback in predetermined conditions related to initiation).
Claim(s) 2 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhowmik (US 2024/0285190) in view of McCarthy (US 2021/0344957) and further in view of Khare (US 2024/0252051).
Regarding claims 2 and 15,
Bhowmik as modified by McCarthy, teaches the system and method of claims 1 and 14 but fails to expressly teach that the trained data model is an echo-state network (ESN) for motor-state prediction.
Khare teaches a system for gait analysis utilizing a trained model and wherein the trained data model is an echo-state network (ESN) for motor-state prediction ([0193] teaches "...the one or more Artificial Intelligence techniques includes the use of one or more trained neural networks...the one or more trained neural networks utilized to create, modify, or enhance ...consists of one or more of ... Echo State Network,")
Before the effective filing date of the invention, it would have been obvious to further modify the Bhowmik system per the teachings of Khare so as to utilize a trained data model which is an ESN because ESNs are effective for time-series prediction in that it is designed to model relationships and retain information about prior states thereby identifying trends which would be helpful in predicting freezing of gait.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689
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