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
1. The amendment filed August 04, 2026 has been entered. Claims 1, 2, 4, and 8-17 are pending in the application. Claims 3 and 5-7 are canceled. New Claims 15-17 are added.
Claim Rejections - 35 USC § 103
2. 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.
3. Claim(s) 1, 4, 9-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over LaValle et al. (U.S. Pub. No. 2014/0354515 A1, hereinafter "LaValle") in view of Thagadur Shivappa (U.S. Pub. No. 2017/0295466 A1, hereinafter "Shivappa").
Regarding Claim 1, LaValle teaches a method (a method for predictive tracking for a head mounted display, Abstract), comprising:
receiving motion data representing motions of a head-mounted listening device (headset 300, has sensors 322 are sensors used to generate motion, position, and orientation data. These sensors may be or include gyroscopes, accelerometers, magnetometers, video cameras, color sensors, or other motion, position, and orientation sensors, Fig. 3, Para. [0044]);
transforming the motion data into quaternion domain (VR headset pitch, roll and yaw (three-dimensional orientation) movement can be expressed using quaternions, Para. [0064]);
predicting, by one or more processors ( processor 210, Figs. 1, 2, and 4, Para. [0024]), future motions of the head-mounted listening device (the motion predictor and smother 415 predict a future movement, orientation, and position, Para. [0056]), the predicting including creating angular acceleration data from the transformed motion data (the motion predictor and smother 415 may differentiate over a series of angular velocity measurements to derive an angular acceleration and extrapolate into the future a predetermined time to predict a future movement, orientation, and position, Paras. [0056] and [0064]) and applying one or more smoothing filters to the angular acceleration data (the motion predictor and smoother 415 may perform "smoothing" operations on sensor data, Para. [0057]; in response to this jitter and other similar errors, the motion predictor and smoother 415 may also apply smoothing to the received data, Para. [0059]), the predicted future motions including rotation angles around corresponding axes in the quaternion domain (the predicted future motions will include rotation angles based on three-dimensional orientation of VR headset expressed in quaternion, Para. [0064]); and
wherein the predicting comprises creating angular velocity data from the transformed motion data and determining a sliding window average of the angular velocity from a history of the created angular velocity (motion predictor and smother 415 may, for example, average angular velocity measurements over a short time frame (e.g., ten measurements over the last ten milliseconds) [sliding window] to derive a prediction about the future movement, orientation, and position, Para. [0056]),
wherein creating the angular acceleration data comprises using numerical differentiation on the created angular velocity data (the motion predictor and smoother 415 may differentiate over a series of angular velocity measurements to derive an angular acceleration, Para. [0056]), and
wherein the future motions are determined based on the sliding window average of the angular velocity (motion predictor and smoother 415 may, for example, average angular velocity measurements over a short time frame to derive a prediction about the future movement, orientation, and position, Para. [0056]).
LaValle fails to explicitly teach a method of audio processing, comprising:
providing the predicted future motions of the head-mounted listening device to a processor for adjusting a sound field presented by the listening device such that the sound field follows predicted movements of the head-mounted listening device,
wherein a size of the sliding window is determined by the smoothed angular acceleration data.
However, LaValle teaches determining the size of the sliding window (In situations in which the angular velocity measurement is quite large, relatively speaking (e.g., on the order of 500 degrees/second) the prediction interval over which the system predicts the movement is made much larger, Para. [0085]; average angular velocity measurements over a short time frame to derive a prediction about the future movement, orientation, and position, Para. [0056]), and smoothing motion data and deriving angular acceleration by differentiating angular velocity data, Paras. [0056] and [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method (as taught by LaValle) to include the sliding window determined by smoothed angular acceleration data by the selection of the number of measurements or duration or interval of the averaging window based on the smoothed motion data including angular acceleration (as taught by LaValle). Such selection would have been a routine design choice for balancing responsiveness against noise reduction in a predictive motion-tracking system. Increasing or decreasing the window in response to motion dynamics would have predictably improved tracking stability during slow movement while preserving responsiveness during rapid movement.
Furthermore, Shivappa teaches a method of audio processing (method of audio processing includes predicted position data based on the position data and generating an output spatialized audio signal based on the predicted position data, Para. [0005]), comprising:
providing the predicted future motions of the head-mounted listening device to a processor for adjusting a sound field presented by the listening device such that the sound field follows predicted movements of the head-mounted listening device (the audio processing device may generate an output spatialized audio signal based on the predicted position data. The audio processing device may process the spatialized audio signal based on the predicted position data to cause the spatialized audio signal to change a perceived direction or distance of a sound to correspond to the predicted position data, Para. [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method (as taught by LaValle) to include the audio processing using predicted future motions to adjust sound-field presented by the listening device (as taught by Shivappa). Doing so prevents or reduces a likelihood of an audio output from lagging behind the user's movement (Shivappa Para. [0021]).
Regarding Claim 4, LaValle in view of Shivappa teach wherein creating the angular velocity data comprises using previously created angular velocity data (LaValle, angular velocity can be determined using prior created angular velocity data, Para. [0082]) and transformed motion data corresponding to the angular velocity data (LaValle, motion predictor and smother 415 may, for example, average angular velocity measurements over a short time frame to derive a prediction about the future movement, orientation, and position, Para. [0056]; movement data can also be expressed in quaternions, Para. [0064]).
Regarding Claim 9, LaValle in view of Shivappa teach wherein the head-mounted listening device includes a plurality of earbuds (Shivappa, audio processing device 200 includes headset 102 capable of outputting 3D rendered audio signals, Fig. 2, Para. [0058]) wirelessly connected to a playing device (the audio processing device 200 may receive one or more streams (e.g., data streams, media streams, etc.) via the wireless interface 270, Fig. 2, Para. [0061]).
Regarding Claim 10, LaValle in view of Shivappa teach wherein the predicting and providing steps are performed by one or more processors of a device providing the sound field to the head-mounted listening device (Shivappa, processor 106, Fig. 1, Para. [0025]).
Regarding Claim 11, LaValle in view of Shivappa teach wherein the receiving and transforming steps are further performed by one or more processors of the device providing the sound field to the head-mounted listening device (LaValle, processor 210, Figs. 1, 2, and 4, Para. [0024]; Shivappa, processor 106, Fig. 1, Para. [0025]).
Regarding Claim 12, LaValle in view of Shivappa teach wherein the receiving and transforming steps are performed by one or more processors of the head-mounted listening device (LaValle, processor 312, Fig. 3, Para. [0031]).
Regarding Claim 13, LaValle in view of Shivappa teach a system (LaVelle, system 100, Fig. 1) comprising:
one or more processors (LaValle, processor 210, Figs. 1, 2, and 4, Para. [0024]; and
a non-transitory computer-readable medium storing instructions that, upon execution by the one or more processors, cause the one or more processors to perform the method of claim 1 (LaValle, memory 212 also provides a storage area for data and instructions associated with applications and data handled by the processor 210, Fig. 2, Para. [0038]).
Regarding Claim 14, LaValle in view of Shivappa teach a non-transitory computer-readable medium storing instructions that, upon execution by one or more processors, cause the one or more processors to perform the method of claim 1 (LaValle, memory 212 also provides a storage area for data and instructions associated with applications and data handled by the processor 210, Fig. 2, Para. [0038]).
Regarding Claim 17, LaValle in view of Shivappa teach wherein the future motions are predicted for a prediction period greater than ten times a sensor-data sampling period (LaValle, process repeats for every sensor update or for every interval of sensor updates (e.g. every 10 or 20 sensor updates), Paras. [0096] and [0097]).
4. Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over LaValle et al. (U.S. Pub. No. 2014/0354515 A1, hereinafter "LaValle") in view of Thagadur Shivappa (U.S. Pub. No. 2017/0295466 A1, hereinafter "Shivappa"), and further in view of Yan et al. (Chinese Pub. No. CN 111523076 A, hereinafter "Yan").
Regarding Claim 2, LaValle in view of Shivappa fail to explicitly teach wherein the predicting comprises applying a Recursive Linear Smoothed Newton filter to the angular acceleration data.
However, Yan teaches wherein the predicting comprises applying a Recursive Linear Smoothed Newton filter to the angular acceleration data (applying iterative linear smoothed Newton method (RLSN) [Recursive Linear Smoothed Newton] predicting function on the calculated angular acceleration, Para. [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the audio processing (as taught by LaValle in view of Shivappa) to include the Recursive Linear Smoothed Newton filter (as taught by Yan). Doing so, the accuracy of calculating the angular acceleration is greatly improved and the distortion rate is greatly reduced (Yan Para. [0075]).
5. Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over LaValle et al. (U.S. Pub. No. 2014/0354515 A1, hereinafter "LaValle") in view of Thagadur Shivappa (U.S. Pub. No. 2017/0295466 A1, hereinafter "Shivappa"), and further in view of Tanaka (U.S. Pub. No. 2017/0352160 A1).
Regarding Claim 8, LaValle in view of Shivappa fail to explicitly teach wherein the angular acceleration data are integrated to create an angular velocity changing value.However, Tanaka teaches wherein the angular acceleration data is integrated to create an angular velocity changing value (detected angular accelerations are integrated over time domain, Para. [0091]).It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the audio processing (as taught by LaValle in view of Shivappa) to include the integration of angular acceleration (as taught by Tanaka). Doing so, rotational speed and position can be accurately tracked which improves precision in the identified direction of user's head (Tanaka Para. [0091]).
6. Claim(s) 15 rejected under 35 U.S.C. 103 as being unpatentable over LaValle et al. (U.S. Pub. No. 2014/0354515 A1, hereinafter "LaValle") in view of Thagadur Shivappa (U.S. Pub. No. 2017/0295466 A1, hereinafter "Shivappa"), and further in view of Wang et al. (Adaptive Window Algorithm for Acceleration Estimation Using Optical Encoders. In: Li, K., Xue, Y., Cui, S., Niu, Q. (eds) Intelligent Computing in Smart Grid and Electrical Vehicles. ICSEE 2014 LSMS 2014. Communications in Computer and Information Science, vol 463. Springer, Berlin, Heidelberg).
Regarding Claim 15, LaValle in view of Shivappa fail to explicitly teach wherein the size of the sliding window is inversely proportional to the smoothed angular acceleration data.
However, Wang teaches wherein the size of the sliding window is inversely proportional to the smoothed angular acceleration data (the window size n should be selected properly depending on the signal itself. The window size should be narrow when the acceleration is high, producing faster and reliable estimation. When the acceleration is low, the window size is large, Pg. 253, Para. 4; i.e. the sliding window is inversely proportional to the acceleration data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the audio processing (as taught by LaValle in view of Shivappa) to include the sliding window inversely proportional to the smoothed angular acceleration data (as taught by Tanaka). Doing so, accuracy of angular acceleration is improved (Wang Pg. 254, Para. 1).
7. Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over LaValle et al. (U.S. Pub. No. 2014/0354515 A1, hereinafter "LaValle") in view of Thagadur Shivappa (U.S. Pub. No. 2017/0295466 A1, hereinafter "Shivappa"), and further in view of Lin et al. (WIPO Pub. No. WO 2016/187759 A1, hereinafter "Lin").
Regarding Claim 16, LaValle in view of Shivappa fail to explicitly teach wherein the sliding window average is determined from a set of most recent angular velocity values stored in a first-in first-out (FIFO) buffer.
However, Lin teaches wherein the sliding window average is determined from a set of most recent angular velocity values stored in a first-in first-out (FIFO) buffer (first in first out (FIFO) buffer is used for the sliding window, Para. [00197]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the audio processing (as taught by LaValle in view of Shivappa) to include the sliding window FIFO buffer (as taught by Tanaka). Doing so, current state information can be selected for the sliding window filter (Lin Para. [00197]).
Response to Arguments
8. Applicant's arguments filed August 04, 2026 have been fully considered but they are not persuasive. Applicant has amended independent Claim 1 and contends see applicant’s remark (pages 6 and 7), neither Merimaa nor LaValle discloses determining a sliding-window average of angular velocity from a history of previously created angular velocity data and then determining future motions based on that sliding-window average.
The cited combination does not disclose determining the size of the sliding window based on smoothed angular acceleration data.
Amended claim 1 now requires that angular acceleration data be created using numerical differentiation on the created angular velocity data. The final Office Action does not identify any disclosure in the cited primary combination of this particular sequence of operations, namely creating angular velocity data from transformed motion data and then generating angular acceleration data through numerical differentiation of the created angular velocity data. The rejection instead treats angular velocity, angular acceleration, and future-motion prediction as separate concepts drawn from various portions of the references, without identifying the particular processing chain now recited.
In response to applicant’s argument above, Claim 1 has now been rejected under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa.
LaVelle teaches discloses determining a sliding-window average of angular velocity from a history of previously created angular velocity data and then determining future motions based on that sliding-window average (Para. [0056]).
LaVelle teaches adaptively varying the size of the sliding window (Para. [0085]); averaging angular velocity measurements over a short time frame to derive a prediction about the future movement, orientation, and position, (Para. [0056]); and smoothing motion data and deriving angular acceleration by differentiating angular velocity data (Paras. [0056] and [0057]). It would have been obvious to select the number of measurements or duration of the averaging window based on the smoothed motion characteristics, including angular acceleration.
The claims do not patentably distinguish over the prior art merely by reciting a sequence. Rearranging or selecting an operational sequence from a pool of known, prior-art processes falls within the ordinary capabilities of a person of ordinary skill in the art.
The rejection of Claim 1 under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa is maintained.
Dependent Claims 4, and 9-14 are now rejected under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa.
The rejections of Claims 4, and 9-14 under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa is maintained.
Dependent Claim 2 is now rejected under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa, and further in view of Yan.
The rejection of Claim 2 under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa, and further in view of Yan is maintained.
Dependent Claim 8 is now rejected under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa, and further in view of Tanaka.
The rejection of Claim 8 under 35 U.S.C. 103 as being unpatentable over LaValle in view of Shivappa, and further in view of Tanaka is maintained.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barak (U.S. Pub. No. 2021/0223858 A1) teaches the use of a filter to smoothing the constant angular acceleration estimate (Para. [0021]).
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIMEZIE E BEKEE whose telephone number is (571)272-0202. The examiner can normally be reached M-F 7.30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Duc Nguyen can be reached at 571-272-7503. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHIMEZIE EZERIWE BEKEE/Examiner, Art Unit 2691
/DUC NGUYEN/Supervisory Patent Examiner, Art Unit 2691
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