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 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.
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.
Claim(s) 1-11 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO2022/261205, hereinafter, ‘205 in view of Prasad et al 20240143077, hereinafter, ‘205.
In regards to claim 1, ‘205 teaches a method comprising (abstract):
at an electronic device having a processor one or more sensors and one or more displays (fig. 2 (250)) obtaining an enrolled eye model of a user that was determined based on sensor data obtained via the one or more sensors of the electronic device (fig. 2 (262, 264, and 240))
205 fails to expressly teach obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model
However, Prasad teaches obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model ([0074-0075,0092-0097] [0074] The semantic correction component may additionally or alternatively be configured to consider a history of the predicted eye gaze positions in determining whether to adjust an output of adder 306) Prasad
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 to further include obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model in order to speed up processing versus recalculating and to reduce errors until acceptable [0094]
Therefore, ‘205 in view of Pradad teaches generating a predicted gaze position based on the enrolled eye model wherein the predicted ([0032-0047] ‘205) [0047] Pradad gaze position corresponds to a position on the one or more displays (fig. 2 (250) display ‘205)
and generating a corrected gaze position based on an output of a correction process that receives as input the predicted gaze position and the eye-model inaccuracy information (fig. 4 (470))(fig. 5a (500c)) ‘205 and ([0074-00750092-0097]) Prasad
In regards to claim 16, ‘205 teaches a non-transitory computer-readable medium comprising instructions that when executed by a processor cause the processor to perform operations comprising (fig. 2 (250)) obtaining an enrolled eye model of a user that was determined based on sensor data obtained via the one or more sensors of the electronic device (fig. 2 (262, 264, and 240))
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205 fails to expressly teach obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model
However, Prasad teaches obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model ([0074-0075,0092-0097])
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 to further include obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model in order to speed up processing versus recalculating and to reduce errors until acceptable [0094]
Therefore, ‘205 in view of Pradad teaches generating a predicted gaze position based on the enrolled eye model wherein the predicted ([0032-0047] ‘205) [0047] Pradad gaze position corresponds to a position on the one or more displays (fig. 2 (250) display ‘205) and ([0074-0075,0092-0097]) Prasad
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and generating a corrected gaze position based on an output of a correction process that receives as input the predicted gaze position and the eye-model inaccuracy information (fig. 4 (470))(fig. 5a (500c)) ‘205.
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In regards to claim 17, ‘205 teaches an electronic device comprising: one or more sensors; one or more displays; a non-transitory computer-readable storage medium; and one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed on the one or more processors, cause the electronic device to perform operations comprising: (fig. 2 (250)) obtaining an enrolled eye model of a user that was determined based on sensor data obtained via the one or more sensors of the electronic device (fig. 2 (262, 264, and 240))
205 fails to expressly teach obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model
However, Prasad teaches obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model ([0074-0075, 0092-0097]
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 to further include obtaining eye-model inaccuracy information corresponding to inaccuracies of prior gaze position predictions determined using the enrolled eye model in order to speed up processing versus recalculating and to reduce errors until acceptable [0094]
Therefore, ‘205 in view of Pradad teaches generating a predicted gaze position based on the enrolled eye model wherein the predicted ([0032-0047] ‘205) [0047] YPrada gaze position corresponds to a position on the one or more displays (fig. 2 (250) display ‘205) and ([0074-0075,0092-0097]) Prasad
In regards to claim 2, ‘205 in view of Pasad teaches the method of claim 1, wherein the enrolled eye model comprises information associated with a 3D [0033-0040] Pasad geometry (fig. 4 vectors) Pasad of an eye of the user used to generate the prior gaze position predictions based on a 3D position of a portion of the eye of the user with respect to eye tracking components and the one or more displays [0074-0076] Pasad and [0026] (fig. 2 250) ‘205).
In regards to claim 3, ‘205 in view of Pasad teaches method of claim 2, wherein the enrolled eye model is based on the sensor data obtained and recorded during an enrollment process for playback to generate predictions comparison with ground truth gaze positions for generating the eye-model inaccuracy information [007-0012] ‘205 and [0092-0093] Pasad
In regards to claim 4, ‘205 in view of Pasad teaches method of claim 1, wherein the inaccuracies comprise errors in the prior gaze position predictions with respect to ground truth gaze positions [0092-0095] Prasad.
In regards to claim 5, ‘205 in view of Pasad teaches method of claim 4, wherein the ground truth gaze positions comprise assumed gaze positions at input events(fig. 7 732)[118-121] Prasad.
In regards to claim 6, ‘205 in view of Pasad teaches method of claim 4, wherein the ground truth gaze positions comprise manually identified intended gaze positions [0091-0093] Prasad.
In regards to claim 7, ‘205 in view of Pasad teaches method of claim 1, wherein the correction process comprises a transformer implemented process (fig. 6 Tx [0052] ‘205 and [0087] Prasad,
In regards to claim 8, ‘205 in view of Pasad teaches method of claim 1, wherein the eye-model inaccuracy information comprises a set of historical user gaze event data being input into the correction process in combination with current environment condition data to generate the corrected gaze position in real time during a current user session.[0074-0076] Prasad
In regards to claim 9, ‘205 in view of Pasad teaches method of claim 1, wherein the eye-model inaccuracy information comprises a set of current user gaze event data being input into the correction process in combination with current environment condition data to generate the corrected gaze position in real time during a current user session [0074] (fig. 3a (306) )Prasad.
In regards to claim 10, ‘205 in view of Pasad teaches method of claim 9, wherein the current environment condition data comprises light condition data [0034] Prasad and [0046] ‘205.
In regards to claim 11, ‘205 in view of Pasad teaches method of claim 1, wherein the inaccuracy information comprises a spatial difference between the predicted gaze location and a ground truth gaze location with respect to a UI element [0075] Pasad and fig. 2/3 display and UI [0052-0054] ‘205.
In regards to claim 18, ‘205 in view of Prasad teaches electronic device of claim 17, wherein the enrolled eye model comprises information associated with a 3D geometry of the eye of the user (cornea shape, pupil radius, other eye dimensions) used to generate the prior gaze position predictions based on a 3D position of a portion of the eye of the user with respect to eye tracking components and the one or more displays.[0026,0032,0066,] ‘205 and [0092-0093] Pasad
In regards to claim 19, ‘205 in view of Prasad teaches electronic device of claim 17, wherein the enrolled eye model is based on the first set of sensor data obtained and recorded during an enrollment process for playback to generate predictions comparison with ground truth gaze positions (e.g., assumed gaze positions at input events) for generating the eye-model inaccuracy information. [007-0012] ‘205.
In regards to claim 20, ‘205 in view of Prasad teaches electronic device of claim 17, wherein the inaccuracies comprise errors in the prior gaze position predictions with respect to ground truth gaze positions. [0092-0095] Prasad.
4. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO2022/261205, hereinafter, ‘205 in view of Prasad et al 20240143077, hereinafter, ‘205 further in view of Ambrus et al 2015/0212576 hereinafter, Ambrus
In regards to claim 12, ‘205 and Prasad fail to teach the method of claim 11, wherein the UI element comprises a geometrical size that is less than a threshold size.
However, Miller teaches wherein the UI element comprises a geometrical size that is less than a threshold size[0075].
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 and Prasad to further include wherein the UI element comprises a geometrical size that is less than a threshold size as taught by Miller in order to have an adequate fixation time [0075]
Claim(s) 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over ‘205 in view of Pasad further in view of Komp 2021/0350624 hereinafter, Komp.
In regards to claim 13, ‘205 and Pasad fail to teach the method of claim 1, further comprising: detecting, during an enrollment process, dominant eye of the user; and assigning a weighting factor to data associated with the dominant eye with respect to the other eye of the user, wherein the correction process further receives as input, the weighting factor applied to the data associated with the dominant eye with respect to the other eye of the user.
However, Komp teaches detecting, during an enrollment process, dominant eye of the user; and assigning a weighting factor to data associated with the dominant eye with respect to the other eye of the user, wherein the correction process further receives as input, the weighting factor applied to the data associated with the dominant eye with respect to the other eye of the user.[0045].
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 and Pasad to further include further comprising: detecting, during an enrollment process, dominant eye of the user; and assigning a weighting factor to data associated with the dominant eye with respect to the other eye of the user, wherein the correction process further receives as input, the weighting factor applied to the data associated with the dominant eye with respect to the other eye of the user as taught by Komp in order to increase robustness and increase accuracy of eye tracking [0045]
In regards to claim 15, ‘205 and Pasad in view of Komp, see rational of claim 13, teaches the method of claim 1, wherein an online gaze calibration process may be configured to modify the corrected gaze position of a dominant eye of the user over time if a weighting factor applied to the dominant eye is greater than a weighting factor applied a second eye of the user on a consistent basis [0074-0076] Prasad Examiner notes the history will continually modify and cautiously adjust due to the iterative correction.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over ‘205 in view of Pasad further in view of Lodato et al 20190116353, hereinafter, Lodato
In regards to claim 14, ‘205 and Pasad fail to teach the method of claim 1, wherein the sensor data comprises image data detected to be blurry with respect to a first eye of the user eye, and wherein the method further comprises: assigning a weighting factor to the first eye that is less than to a weighting factor applied to a second eye of the user.
However, Lodato teaches wherein the sensor data comprises image data detected to be blurry with respect to a first eye of the user eye, and wherein the method further comprises: assigning a weighting factor to the first eye that is less than to a weighting factor applied to a second eye of the user.[366]
It would have been obvious to one of ordinary skill in the art to modify the teachings of ‘205 and Pasad to further include wherein the sensor data comprises image data detected to be blurry with respect to a first eye of the user eye, and wherein the method further comprises: assigning a weighting factor to the first eye that is less than to a weighting factor applied to a second eye of the user as taught by Lodato in order correct for any unhealthy eye [0366]
Conclusion
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/GRANT SITTA/Primary Examiner, Art Unit 2622
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