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 .
DETAILED ACTION
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-13, 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dharmarajan (2017/0277684).
Consider claims 1, 8 and 15, Dharmarajan teaches a computer-implemented method, system and non-transitory machine-readable storage medium storing instructions that when executed by one or more processors, cause the one or more processors operations/comprising: extracting one or more video frames from video streams of a set of communication sessions, wherein the video frames include a representation of a gesture (par. 0026; “It should also be noted that the images captured of an operator making sign language gestures for subsequent conversion of sign language to text may include captured motion video (e.g., multiple images captured as motion video frames at a predetermined frame rate) of the operator forming the sign language gestures. It should be further noted that the images of an avatar making sign language gestures for presentation to an operator may include motion video animation (e.g., multiple images presented as motion video frames at a predetermined frame rate) of the avatar forming the sign language gestures”; par. 0052; “Correspondingly, the connection routine 340 may include a communication component 349 executable by the processor 350 to operate the network interface 390 to also exchange communications via the network 999 as previously described. Again, among such communications may be those conveying captured images of an operator making sign language gestures (e.g., captured motion video of the operator making sign language gestures), images of at least one avatar making sign language gestures (e.g., motion video animation of at least one avatar making sign language gestures), text, captured speech of an operator, and/or speech generated from text to be acoustically output to an operator”); defining a training dataset from the one or more video frames and features extracted from the set of communication sessions; training a neural network using the training dataset, the neural network being configured to classify gesture as a communication (par. 0055-0057; “the components 211 and/or 218 may be executed by the processor 150 or 350 to perform a training operation to improve the ability of the sign recognition component 211 to interpret the sign language gestures made by the operator of the communication device 100a. More specifically, where the signing routine 210 is executed within the communication device 100a to perform such a training operation (as depicted with dotted lines in FIG. 2A), the sign recognition component 211 may operate the camera 110 to capture images of the operator of the communication device 100a making sign language gestures of their choosing”); extracting a video frame from a new video stream of a new communication session, wherein the video frame includes a representation of a particular gesture; executing the neural network using the video frame, wherein the neural network generates a predicted classification of the particular gesture; generating a communication response corresponding to the predicted classification of the gesture (par. 0089-0090; “receive images captured of the operator who uses sign language making sign language gestures. As has been discussed, if the signing routine 210 is executed within a connection server 300 such that conversions to and from sign language are performed by the processor 350 thereof, then the captured images of the operator making sign language gestures may be received from the communication device of the operator who uses sign language via a network extending therebetween (e.g., a portion of the network 999)”; “the processor may employ training data earlier generated during a training operation to improve the accuracy of the interpretation of those sign language gestures as part of performing the conversion”); and facilitating a transmission of the communication response, the communication response being a response to the particular gesture (par. 0091; “the processor may transmit the text generated by this conversion to the other communication device of the other operator who does not use sign language via the network. As previously discussed, if the conversion from sign language to text is performed within the communication device of the operator who uses sign language, then the text may be relayed through the connection server, as well as through the network”).
Consider claims 2, 9 and 16, Dharmarajan teaches wherein the new communication session is between a user device and a terminal device (par. 0083; “a processor of a connection server of a communication system (e.g., the processor 350 of a connection servers 300 of the communication system 1000) may receive, via a network, a request from a communication device associated with an operator using sign language (e.g., the communication device 100a) to communicate with one of multiple operators of one of multiple communication devices”).
Consider claims 3, 10 and 17, Dharmarajan teaches wherein the particular gesture is a static position of a body part (par. 0026; “It should also be noted that the images captured of an operator making sign language gestures for subsequent conversion of sign language to text may include captured motion video (e.g., multiple images captured as motion video frames at a predetermined frame rate) of the operator forming the sign language gestures”).
Consider claim 4, 11 and 18, Dharmarajan teaches wherein the particular gesture is a motion involving one or more body parts (par. 0026; “It should also be noted that the images captured of an operator making sign language gestures for subsequent conversion of sign language to text may include captured motion video (e.g., multiple images captured as motion video frames at a predetermined frame rate) of the operator forming the sign language gestures”).
Consider claims 5, 12 and 19, Dharmarajan teaches further comprising: extracting an audio segment from the new communication session that corresponds to the video frame, wherein executing the neural network further uses the audio segment. (par. 0040; 0070; “Alternatively or additionally, the processor 150 of the communication device 100b may operate one or more microphones incorporated into and/or coupled to the audio interface 170 to capture speech sounds uttered by the operator of the communication device 100b. The processor 150 may then be caused to operate the network interface 190 to transmit those captured speech sounds to the connection server 300 for conversion into the images of the avatar making sign language gestures as has been described”).
Consider claims 6 and 13, Dharmarajan teaches wherein the neural network is an ensemble network comprising two or more neural networks configured to generate outputs of different types (par. 0030; “the first communication device or the connection server may operate in a training mode to learn idiosyncrasies of the first operator in making sign language gestures. In such a training mode, the first operator may make a number of sign language gestures of their choosing and/or may be guided through making a selection of sign language gestures within a field of view of a camera of the first communication device”; par. 0031; “Alternatively or additionally, the first communication device may transmit profile data to the connection server indicating that the first communication device includes at least a camera to capture sign language gestures and/or a display to present an avatar making sign language gestures. The connection server may employ such indications in profile data to trigger and/or enable conversions between sign language and text, and/or between sign language and speech as part of enabling the first operator to more easily converse with another operator who does not use sign language”).
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 of this title, 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 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dharmarajan (2017/0277684) in view of Jandhyala et al (2022/0157083).
Consider claims 7, 14 and 20, Dharmarajan does not explicitly suggest wherein the neural network is configured to generate a boundary box over the particular gesture. In the same field of endeavor, Jandhyala et al teach a systems and method that identify a gesture based on event camera data and frame-based camera data. In some implementations, the frame-based camera data is used to identify a region of interest (e.g., a bounding box) for the event camera to analyze (par. 0003; 0059-0060; “a plurality of events 730 within the bounding box are detected by the event camera 422b. In some implementations, the events 730 detected by the event camera 422b include positive events (e.g., generated by the leading edge of the hand or fingers) and negative events (e.g., generated by the trailing edge of the hand of fingers)”). Therefore, it would have been obvious to a person of ordinary skills in the art before the effective filing date the invention was made to incorporate the teaching of Jandhyala et al into view of Dharmarajan and the results would have been predictable and resulted in providing an improved process for identify gesture thereby allow the system to quickly, efficiently, and accurately identify and classify gestures.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/Quoc D Tran/
Primary Examiner, Art Unit 2691
December 4, 2025