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 Objections
Claim 5 is objected to because of the following informalities: in line 4, the term “sesnsor” should be changed to read “sensor”. Appropriate correction is required.
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 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Trim et al. (US 20200166781 A1) in view of Abou Shousha (US 20220125299 A1).
Regarding claim 1, Trim et al. discloses a method comprising:
receiving operational data from an electronics-enabled eyewear device having an optical system with variable opacity, the optical system being switchable between a see-through state and an opaque state (smart eyewear system co-exists with smart device monitoring tools, collects sensory data (audio-visual information) of the surrounding area from a camera/microphone placed on the eyewear system, identifies the situation (people, event, etc.) and instructs an electrochromic glass controller to tune the color and opacity of lenses, para. 0044, 0003);
Trim et al. does not expressly disclose feeding the operational data to a machine learning model trained with use-case sensor data; and based at least in part on output from the trained machine learning model, triggering autonomous mode switching of the optical system between the see-through state and the opaque state.
Abou Shousha teaches a machine learning model 162 having a prediction model wherein a modification profile for modification parameters or functions to dynamically configure one or more display portions (e.g., dynamic adjustment of transparent or opaque portions of a transparent display, dynamic adjustment of projecting portions of a projector, etc.), see para. 0088.
Trim et al. in view of Abou Shousha are analogous art because they are from the similar problem solving area of virtual vision. At the time of the invention, it would have been obvious to a person of ordinary skill in the art to add the machine learning feedback of Abou Shousha to the method of Trim et al. in order to obtain transparency control of display. The motivation for doing so would be to conveniently switch between views.
Regarding claim 2, Trim et al. discloses the method of claim 1, wherein the operational data comprises:
sensor data captured by sensors (para. 0044) integrated in the eyewear device; and
a current optical state of the optical system (a smart eyewear system, that dynamically adjusts its appearance based on a current situational context of a user. The smart eyewear system co-exists with smart device monitoring tools, collects sensory data (audio-visual information) of the surrounding area from a camera/microphone placed on the eyewear system, identifies the situation (people, event, etc.), para. 0044).
Regarding claim 11, Abou Shousha discloses an eyewear device (spectacles device 170, para. 0093) comprising:
a lens assembly (see-through display 196, para. 0096);
an eyewear body (spectacles device 170, para. 0093) on which the lens assembly is mounted, the eyewear body being configured for head-mounted wear during which the lens assembly is supported in position to occupy a field-of-view of a wearer;
a screening mechanism incorporated in the lens assembly and configured to selectively dispose the lens assembly between at least two different optical states comprising a see-through state and an opaque state (transparency of the glass in the spectacles device comprising custom-reality glasses may be controllably adjusted to block light from that portion of the visual field corresponding to where image correction is performed (e.g., at a central region or a peripheral region). Subject may see the manipulated image and see through it and perceive the underling actual visual field in that region. Such light blocking can be achieved by a photochromic glass layer within the spectacles device, para. 0225);
one or more sensors incorporated in the eyewear body (para. 0094);
an opacity controller (processor 2300, para. 0223) comprising one or more computer processor devices housed by the eyewear body, the one or more computer processor devices being configured to perform operations comprising:
receiving current sensor data captured by the one or more sensors (para. 0044);
inputting at least a subset of the current sensor data to a machine learning model trained with historical use-case sensor data associated with one or more situational factors pertinent to opacity mode-switching (machine learning model 162 having a prediction model wherein a modification profile for modification parameters or functions to dynamically configure one or more display portions (e.g., dynamic adjustment of transparent or opaque portions of a transparent display, dynamic adjustment of projecting portions of a projector, etc.), see para. 0088); and
based at least in part on an output provided by the trained machine learning model responsive to the sensor data, triggering autonomous mode switching of the lens assembly between the see-through state and the opaque state (machine learning model 162 having a prediction model wherein a modification profile for modification parameters or functions to dynamically configure one or more display portions (e.g., dynamic adjustment of transparent or opaque portions of a transparent display, dynamic adjustment of projecting portions of a projector, etc.), see para. 0088).
Regarding claim 12, Abou Shousha discloses the eyewear device of claim 11, wherein the machine learning model is provided by on-board electronics comprising a memory (memory 308, para. 0107) storing the trained machine learning model.
Regarding claim 13, Abou Shousha discloses the eyewear device of claim 12, wherein the machine-learning model is hosted off-board, the opacity controller being configured such that the inputting of the current sensor data to the machine learning model comprises communicating the sensor data wirelessly of a separate device, the output of the machine learning model being receiving via responsive inbound wireless communication (para. 0101).
Regarding claim 14, Abou Shousha discloses the eyewear device of claim 13, wherein the machine learning model is provided by a mobile device carried by and associated with the wearer separately from the eyewear device (example of figure 3).
Regarding claim 15, Abou Shousha discloses the eyewear device of claim 14, wherein the machine learning model is provided an online service in communication with the opacity controller via the Internet (para. 0111).
Allowable Subject Matter
Claims 3-10 and 16-20 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
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/THOMAS J LETT/Primary Examiner, Art Unit 2611
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