The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Current Status of Claims
This action is a response to communication of December 8, 2025. By amendment of December 8, 2025, the Applicant amended claims 1-4, 13-14 and 20-21. Therefore, claims 1 to 27 remain active in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 8, 2026 was filed on the mailing date of the instant application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s arguments with respect to claim(s) 1-27 have been considered but are moot because the new ground of rejection does not rely on some of the references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell (US Patent Publication Application 2016/0055822 A1) in view of Carrara et al. (PCT Patent Publication WO2022/046047 A1) provided with IDS of January 8, 2026.
In regard of claim 1, Bell discloses a method, at a near-eye display (NED) system, comprising: analyzing a first set of sensor data, including one or both of inertial sensor data or acoustic sensor data, obtained from one or more sensors of the NED system (See at least Figure 7 of Bell illustrating NED system (8) analyzing sensor data (54)including sensor data from acoustic (194) or inertial sensors (193) as discussed in paragraphs [0029, 0060-0066, 0070]); and responsive to detecting, based on the analyzing, that the first set of sensor data includes one or both of inertial characteristics or acoustic characteristics corresponding to a gesture, generating an indication that the gesture has occurred at the NED system (See Figures 7, 9 of Bell illustrating detection (192) based on analyzing sensor data (193, 194) based on sensing of accelerometer/gyroscope for inertial characteristics or microphone for acoustic characteristics and generating indication (195, 197) as discussed in paragraphs [0063-0067, 0101]).
However, the reference to Bell does not provide in great details how physical gesture performed at the NED system has occurs.
In the same field of endeavor, Carrara et al. disclose a NED system (410) including a wearable device (410) shown in Figures 1B, 4A and could be presented also as glasses (NED) or a VR/AR headset as discussed in paragraph [0023] and based on analyzing inertial/acoustic characteristics from sensors (418A, 418B, 450) generating an indication that physical gesture (448) has occurred at the NED system and generate an indication as shown in Figure 1B (100B) as discussed in paragraphs [0021, 0023, 0043].
Therefore, it would be obvious for a person skilled in the art at the moment the invention was filed to use method of Carrara et al. with method of Bell in order to recognize a type gesture for command input.
In regard of claim 2, Bell and Carrara et al. further disclose the method of claim 1, further comprising: controlling at least one operation of the NED system responsive to the indication that the gesture has occurred (See at least Figure 7 of Bell illustrating NED system operating (190) in response to indication that the gesture occurred (193) as discussed in paragraphs [0063-0064] or Figure 4A of Carrara et al. illustrating a tap gesture which is recognized as answer/hang up of the phone as discussed in paragraphs [0043-0044] of Carrara et al.).
In regard of claim 3, Bell and Carrara et al. further disclose the method of claim 1, wherein generating the indication further comprises: generating an indication of attributes of the gesture including one or both of a first set of attributes indicating a direction of the gesture or a second set of attributes indicating a magnitude of the gesture (See at least paragraphs [0063-0068] of Bell discussing attributes of indication of the gesture/object see also paragraphs [0031, 0050] of Carrara et al. discussing such attributes of the gesture as direction of swipe gesture).
In regard of claim 4, Bell and Carrara et al. further disclose the method of claim 3, further comprising: identifying one or both of the direction of the gesture or the magnitude of the gesture based on at least one of the one or both of inertial characteristics or acoustic characteristics (See at least Figure 7 of Bell illustrating gesture recognition engine (193) allowing to identify direction or magnitude of the gesture as discussed in paragraphs [0063-0064] and Figures 1B and 4A of Carrara et al. illustrating identifying gesture magnitude or direction (111) based on inertial (118) of acoustic (115) characteristics as discussed in paragraphs [0021, 0023, 0028, 0031]).
In regard of claim 5, Bell and Carrara et al. further disclose further discloses the method of claim 1, wherein the inertial sensor data includes accelerometer data, and the acoustic sensor data includes microphone data (See Figure 7 of Bell illustrating inertial/acoustic sensor data (193, 194) includes accelerometer data as discussed in paragraph [0101], see also Figure 1B of Carrara et al. reference numerals (118, 115, 128, 125)).
In regard of claim 13, Bell and Carrara et al. further disclose further discloses a near-eye display (NED) system comprising: an image source to project light representing imagery; a waveguide to conduct the light from the image source toward an eye of a user; and a processing device configured to: perform an analysis of a first set of sensor data, including one or more of inertial sensor data or acoustic sensor data, obtained from one or more sensors of the NED system; responsive to a detection, based on the analysis, that the first set of sensor data includes one or more of inertial characteristics or acoustic characteristics corresponding to a gesture, generate an indication that the gesture has occurred at the NED system; and control the image source based on the indication that the gesture has occurred (See Figures 1 - 3 of Bell illustrating a NED (8) with an image source (120) including waveguide (123) and processing device (210) controlling image source (246) as also presented in rejection of claim 1 provided above).
In regard of claim 14, Bell and Carrara et al. further disclose further discloses The NED system of claim 13, wherein the processing device is further configured to: generate an indication of attributes of the gesture including one or more of a first set of attributes indicating a direction of the gesture or a second set of attributes indicating a magnitude of the gesture (See rejection of claim 3 provided above).
Claims 6, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell (US Patent Publication Application 2016/0055822 A1) in view of Carrara et al. (PCT Patent Publication WO2022/046047 A1) and further in view of Shchur et al. (US Patent Publication Application 2018/0348853 A1).
In regard of claim 6, Bell and Carrara et al. further disclose the method of claim 1.
However, the combination of reference to Bell and Carrara et al. does not disclose the method further comprising: filtering the first set of sensor data to remove at least one of noise or frequencies below a cut-off frequency, wherein the analyzing comprises analyzing the filtered set of sensor data.
In the same field of endeavor, Shchur et al. disclose a method for a NED system including filtering performed by CPU (5373) or GPU (5374) of sensor data (5380) as discussed in paragraphs [0282-0283, 0286] and illustrated in Figure 53 of Shchur et al.
Therefore, it would be obvious for a person skilled in the art to do noise filtering for sensor data as shown by Shchur et al. in the method of Bell and Carrara et al. in order to greatly increase the user convenience during control the near-eye display system.
In regard of claim 23, Bell, Carrara et al. and Shchur et al. further disclose the method of claim 20, further comprising: filtering the input stream to remove at least one of noise or frequencies below a cut-off frequency, wherein the analyzing comprises analyzing the filtered input stream (See rejection of claim 6 provided above) .
Claims 7-10, 15-17, 20-22, and 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell (US Patent Publication Application 2016/0055822 A1) in view of Carrara et al. (PCT Patent Publication WO2022/046047 A1) and further in view of Petill et al. (US Patent Publication Application 2021/0012113 A1).
In regard of claim 7, Bell and Carrara et al. further disclose the method of claim 1.
However, the combination of references to Bell and Carrara et al. does not particularly disclose the method further comprising: transforming the first set of sensor data from a time domain to a time-frequency domain, wherein the analyzing comprises analyzing the transformed first set of sensor data.
In the same field of endeavor, Petill et al. disclose the method at NED including the step of transforming sensor data from time domain to a time-frequency domain like Gaussian models transformation as discussed in the paragraph [0043] and shown in Figure 1, reference numeral (40).
Therefore, it would be obvious for a person skilled in the art at the moment the invention was filed to use transformation of sensor data shown by Petill et al. as a step of Bell and Carrara et al. method in order to train sensor data and improve utility for correctly recognizing speech audio.
In regard of claim 8, Bell, Carrara et al. and Petill et al. further disclose the method of claim 1, further comprising: implementing at least one neural network, wherein the analyzing comprises analyzing the first set of sensor data using the at least one neural network (See at least Figure 1 of Petill et al. illustrating a neural network (38) for analyzing sensor data (36) as discussed in paragraph [0037]).
In regard of claim 9, Bell, Carrara et al. and Petill et al. further disclose the method of claim 8, wherein the at least one neural network is one or more of: a convolutional neural network (CNN) that takes raw sensor data in a time domain as input; a temporal CNN that takes raw inertial sensor data in the time domain as input; a CNN that takes inertial sensor data transformed into the time domain as input; or a temporal CNN including residual connections that takes acoustic sensor data transformed into the time domain as input (See Figure 1 of Petill et al. illustrating the neural network (38) using raw sensor data (36) to input as discussed in paragraph [0024] of Petill et al.).
In regard of claim 10, Bell, Carrara et al. and Petill et al. further disclose the method of claim 8, further comprising: training the at least one neural network using a set of training data including labels identifying at least one or more of a start event of a gesture, an end event of a gesture, inertial characteristics, patterns of inertial characteristics, acoustic characteristics, patterns of acoustic characteristics, or gesture attributes (See Figure 1 of Petill et al. illustrating the neural network (38) performing training using set of training data including labels (58, 46, 48) as discussed in paragraphs [0047-0049]).
In regard of claim 15, Bell, Carrara et al. and Petill et al. further disclose the NED system of claim 13, wherein the processing device is further configured to: implement at least one neural network, wherein the processing device is configured to perform the analysis by analyzing the first set of sensor data using the at least one neural network (See rejection of claim 8 provided above).
In regard of claim 16, Bell, Carrara et al. and Petill et al. further disclose the NED system of claim 15, wherein the at least one neural network is one or more of: a convolutional neural network (CNN) that takes raw sensor data in a time domain as input; a temporal CNN that takes raw inertial sensor data in the time domain as input; a CNN that takes inertial sensor data transformed into the time domain as input; or a temporal CNN including residual connections that takes acoustic sensor data transformed into the time domain as input (See rejection of claim 9 provided above).
In regard of claim 17, Bell, Carrara et al. and Petill et al. further disclose the NED system of claim 15, wherein the processing device is further configured to: train the at least one neural network using a set of training data including labels identifying at least one or more of a start event of a gesture, an end event of a gesture, inertial characteristics, patterns of inertial characteristics, acoustic characteristics, patterns of acoustic characteristics, or gesture attributes (See rejection of claim 10 provided above).
In regard of claim 20, Bell, Carrara et al. and Petill et al. further disclose a method, at a near-eye display (NED) system, comprising: obtaining an input stream from one or more of a set of inertial sensors or a set of acoustic sensors of the NED system; analyzing, by at least one neural network, the input stream; responsive to the analyzing, determining, by the at least one neural network, that the input stream includes one or more of an inertial characteristic or an acoustic characteristic corresponding to a gesture having a directional component; detecting, based on the one or more of the inertial characteristic or the acoustic characteristic, that the gesture having a directional component has been performed on the NED system; and controlling at least one operation of the NED system responsive to the detecting that the gesture has been performed (See rejection of claims 1-8 provided above).
In regard of claim 21, Bell, Carrara et al. and Petill et al. further disclose the method of claim 20, wherein detecting that the gesture has been performed further comprises: detecting at least one of a direction or a magnitude of the gesture (See rejection of claim 3 provided above).
In regard of claim 22, Bell, Carrara et al. and Petill et al. further disclose the method of claim 20, wherein the set of inertial sensors includes an accelerometer and the set of acoustic sensors includes a microphone (See Figure 1 and paragraphs [0028, 0101] of Bell discussing accelerometer and microphone (110) as sensors).
In regard of claim 24, Bell, Carrara et al. and Petill et al. further disclose the method of claim 20, further comprising: transforming the input stream from a time domain to a time-frequency domain, wherein the analyzing comprises analyzing the transformed input stream (See rejection of claim 7 provided above).
In regard of claim 25, Bell, Carrara et al. and Petill et al. further disclose the method of claim 20, wherein the at least one neural network is one or more of: a convolutional neural network (CNN) that takes raw sensor data in a time domain as input; a temporal CNN that takes raw inertial sensor data in the time domain as input; a CNN that takes inertial sensor data transformed into the time domain as input; or a temporal CNN including residual connections that takes acoustic sensor data transformed into the time domain as input (See rejection of claim 9 provided above).
In regard of claim 26, Bell, Carrara et al. and Petill et al. further disclose the method of claim 25, further comprising: training the at least one neural network using a set of training data including labels identifying at least one or more of a start event of a gesture having a directional component, an end event of a gesture having a directional component, inertial characteristics, patterns of inertial characteristics, acoustic characteristics, patterns of acoustic characteristics, or gesture attributes (See rejection of claim 10 provided above).
Claims 11, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bell (US Patent Publication Application 2016/0055822 A1) in view of Carrara et al. (PCT Patent Publication WO2022/046047 A1) and further in view of Petill et al. (US Patent Publication Application 2021/0012113 A1) and further in view Shuang et al. (US Patent Publication Application 2024/0170004 A1).
In regard of claim 11, Bell, Carrara et al. and Petill et al. further disclose the method of claim 10, further comprising: automatically generating one or more of the labels based on at least: applying a filter to a second set of sensor data, including one or more of inertial data or microphone data, to remove low-frequency artifacts; responsive to applying the filter, performing principal component analysis (PCA) to reduce the second set of sensor data to one dimension (See at least Figure 53 of Shchur et al. illustrating generation of labels based applying a filter (5373, 5374) as discussed in paragraphs [0283, 0286]).
However, the combination of Bell, Carrara et al. and Petill et al. does not specifically discusses that analysis is performed being responsive to performing the PCA, performing a Fast Fourier Transform (FFT) to transform the second set of sensor data to a time- frequency domain; responsive to performing the FFT, calculating a mean of high-frequency bands within the second set of sensor data; and responsive to calculating the mean of the high-frequency bands, identifying a local maxima of the second set of sensor data.
In the same field of endeavor, Shuang et al. discloses performing principal component analysis (203) of the sensor data by performing FFT transformation as shown in Figure 2 and discussed in paragraphs [0037-0038] of Shuang et al.
Therefore, it would be obvious for a person skilled in the art at the moment the invention was filed to use transformation of sensor data shown by Shuang et al. as a step of Bell, Carrara et al. and Petill et al. method in order to improve reconstructed speech for correctly recognizing speech audio.
In regard of claim 18, Bell, Carrara et al., Petill et al. and Shuang et al. further disclose the NED system of claim 17, wherein the processing device is further configured to automatically generate one or more of the labels based on at least: applying a filter to a second set of sensor data, including one or more of inertial data or microphone data, to remove low-frequency artifacts; responsive to applying the filter, performing principal component analysis (PCA) to reduce the second set of sensor data to one dimension; responsive to performing the PCA, performing a Fast Fourier Transform (FFT) to transform the second set of sensor data to a time- frequency domain; responsive to performing the FFT, calculating a mean of high-frequency bands within the second set of sensor data; and responsive to calculating the mean of the high-frequency bands, identifying a local maxima of the second set of sensor data (See rejection of claim 11 provided above).
Allowable Subject Matter
Claims 12, 19, 27 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Olga V. Aronovich whose telephone number is (571)270-7796. The examiner can normally be reached on Mon-Fri. from 7:30-5:00.
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/OLGA V MERKOULOVA/Primary Examiner, Art Unit 2629