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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/05/2025 is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7, and 11-17, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Conliffe (US Pub No. 2015/0088500).
Regarding claim 1, Conliffe teaches a system (Abstract, wearable apparatus), comprising: a processor that executes computer executable components stored in memory (Abstract, memory and processor), wherein the computer executable components comprise: an identification component that identifies a point of interest of a wearer of an augmented reality headset (Fig 8, step 802 camera tracks eye of wearer), wherein the augmented reality headset includes microphones (Fig 8, step 804 microphone associated with wearable device) and audio transmitter (Fig 8, step 810 audio signal provided to wearer via speaker); a focus component that isolates audio data received associated with the point of interest (Fig 3 & ¶ [0030], beamformer generating directional instructions); and an output component that amplifies isolated audio data output by the audio transmitter (¶ [0017], enhanced and amplified audio output).
Regarding claim 2, Conliffe teaches the system of claim 1, further comprising a language processing component that converts languages in real time to the wearer (¶ [0057], language packs for localization and translation).
Regarding claim 3, Conliffe teaches the system of claim 1, wherein the identification component uses eye movement tracking to identify the point of interest of the wearer (Fig 8, step 802 camera tracks eye of wearer).
Regarding claim 4, Conliffe teaches the system of claim 1, wherein the identification component tracks movement of an audio source (¶ [0021], camera used to track movement of an object or provide video with regard to a particular target).
Regarding claim 5, Conliffe teaches the system of claim 1, wherein output component adjusts settings in real time to improve audio quality (Fig 8, method performed based on eye tracking (real-time)).
Regarding claim 6, Conliffe teaches the system of claim 1, wherein the focus component filters background noise (¶ [0031], background noise reduction).
Regarding claim 7, Conliffe teaches the system of claim 1, wherein the output component is configured to adjust sound settings to optimize hearing for a selected sound source (¶ [0017], enhanced and amplified audio output).
Regarding claim 11, Conliffe teaches a computer-implemented method (Fig 8, method 800) that utilizes a processor that executes computer executable components stored in memory (Abstract, memory and processor) to perform the following acts: identifying a point of interest of a wearer of an augmented reality headset (Fig 8, step 802 camera tracks eye of wearer), wherein the augmented reality headset includes microphones (Fig 8, step 804 microphone associated with wearable device) and audio transmitter (Fig 8, step 810 audio signal provided to wearer via speaker); isolating audio data received associated with the point of interest (Fig 3 & ¶ [0043], spatial postfilter 304 isolates received signals from beamformer 302); and amplifying isolated audio data output by the audio transmitter (¶ [0017], enhanced and amplified audio output).
Regarding claim 12, Conliffe teaches the method of claim 11, further comprising converting languages in real time to the wearer (¶ [0061], real-time translations).
Regarding claim 13, Conliffe teaches the method of claim 11, further comprising using eye movement tracking to identify the point of interest of the wearer (Fig 8, step 802 camera tracks eye of wearer).
Regarding claim 14, Conliffe teaches the method of claim 11, further comprising tracking movement of an audio source (¶ [0021], camera used to track movement of an object or provide video with regard to a particular target).
Regarding claim 15, Conliffe teaches the method of claim 11, further comprising adjusting settings in real time to improve audio quality (Fig 8, method performed based on eye tracking (real-time)).
Regarding claim 16, Conliffe teaches the method of claim 11, further comprising filtering unwanted background noise from the point of interest (¶ [0031], background noise reduction).
Regarding claim 17, Conliffe teaches the method of claim 11, further comprising adjusting sound settings to optimize hearing for selected sound source (¶ [0017], enhanced and amplified audio output).
Regarding claim 19, Conliffe teaches a computer program product (Abstract) comprising a computer readable storage medium having program instructions embodied therewith (¶ [0063], instructions stored in memory 704), the program instructions executable by a processor to cause the processor (¶ [0063], processor 702 processes instructions for execution) to: identify a point of interest of a wearer of an augmented reality headset (Fig 8, step 802 camera tracks eye of wearer), wherein the augmented reality headset includes microphones (Fig 8, step 804 microphone associated with wearable device) and audio transmitter (Fig 8, step 810 audio signal provided to wearer via speaker); isolate audio data received associated with the point of interest (Fig 3 & ¶ [0043], spatial postfilter 304 isolates received signals from beamformer 302); and amplify isolated audio data output by the audio transmitter (¶ [0017], enhanced and amplified audio output).
Regarding claim 20, Conliffe teaches the computer program product of claim 19, the program instructions executable by a processor further cause the processor to: use eye movement tracking to identify the point of interest of the wearer (Fig 8, step 802 camera tracks eye of wearer).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 8-10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Conliffe (US Pub No. 2015/0088500) as applied to claims above, and further in view of Kemmerer et al (US Pub No. 2022/0091674, hereinafter Kemmerer).
Regarding claim 8, Conliffe teaches the system of claim 4.
Conliffe does not explicitly teach a storage component that saves stored volume thresholds for the audio source and background noise.
Kemmerer teaches a storage component that saves stored volume thresholds for the audio source and background noise (See Kemmerer ¶ [0040], threshold-based digital signal processing algorithms for controlling a threshold to adjust detection sensitivity).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the volume threshold taught by Kemmerer with the system taught by Conliffe. Doing so allows for controllable activity detection sensitivity (See Kemmerer ¶ [0040], threshold-based digital signal processing algorithms for controlling a threshold to adjust detection sensitivity) which provides a more customized user experience.
Regarding claim 9, Conliffe teaches the system of claim 1.
Conliffe does not explicitly teach an artificial intelligence component that trains an artificial intelligence model on wearer preferences.
Kemmerer teaches an artificial intelligence component that trains an artificial intelligence model on wearer preferences (See Kemmerer ¶ [0041], deep learning algorithms trained regarding specific user preferences).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the deep learning taught by Kemmerer with the system taught by Conliffe. Doing so allows for automatic pattern recognition of a wearer’s use patterns allowing for a well-defined user profile and overall ease of use for the wearer.
Regarding claim 10, Conliffe in view of Kemmerer teaches the system of claim 9, wherein the artificial intelligence component automatically adjusts hearing settings based on the wearer preferences (See Kemmerer ¶ [0091], contextual information determined to enable noise cancellation adjustment and/or other setting adjustments).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the automatic adjustments taught by Kemmerer with the system taught by Conliffe. Doing so allows for automatic pattern recognition of a wearer’s use patterns allowing for a well-defined user profile and overall ease of use for the wearer.
Regarding claim 18, Conliffe teaches the method of claim 11.
Conliffe does not explicitly teach a storage component that saves stored volume thresholds for the audio source and background noise.
Kemmerer teaches a storage component that saves stored volume thresholds for the audio source and background noise (See Kemmerer ¶ [0040], threshold-based digital signal processing algorithms for controlling a threshold to adjust detection sensitivity).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the volume threshold taught by Kemmerer with the method taught by Conliffe. Doing so allows for controllable activity detection sensitivity (See Kemmerer ¶ [0040], threshold-based digital signal processing algorithms for controlling a threshold to adjust detection sensitivity) which provides a more customized user experience.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mehra (US Patent No. 10555106) teaches gaze directed audio enhancement.
Khaleghimeybodi et al (US Patent No. 11470439) teaches an augmented reality headset that uses machine learning to customize HRTFs.
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/T.M.L./Examiner, Art Unit 2694
/ALEXANDER KRZYSTAN/Primary Examiner, Art Unit 2694
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