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 .
Response to Arguments
Claims 1-20 are pending and the examiner is maintaining the double patenting rejection, seeing that no terminal disclaimer has been filed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-9 and 12-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4-20 of U.S. Patent No. 12,096,982B2 in view of XU US2018/0157899A1.
The patent teaches mostly all limitations of claims 1-9, 12-20 but doesn’t teach, however the analogous prior art XU teaches:
Regarding claim 1: comprising one or more hidden neural network layers, each hidden layer of the one or more hidden neural network layers comprising one or more weights (fig. 8, 800 see pars. 54 lines 13-27; par. 139 lines 1-6 and 25-32; par. 149 lines 1-5);
Regarding claim 2: model weights (par. 54 lines 13-27);
Regarding claims 6-7: a neural network (par. 9 lines 1-6);
Regarding claims 12, 15 and 16: an artificial intelligence model (par. 7 lines 6-8);
Regarding claims 17-18: an artificial intelligence model (par. 7 lines 6-8), a machine learning model (par. 9 lines 1-6; note: the CNN is interpreted as a machine learning model);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine one or more hidden neural network layers, each hidden layer of the one or more hidden neural network layers comprising one or more weights; model weights; a neural network; an artificial intelligence model; a machine learning model as shown in XU with the patent for the benefit of addressing a shortcoming in the prior art related to existing intrusive target detection approaches depend on detected interactions of a user with a terminal device. However, when the above approaches are applied, the identification operation becomes relatively cumbersome, time consuming, and computational intensive, and further requires the user to perform a designated motion [XU par. 6 lines 1-3 and 8-11].
Claims of 18/893,914 Claims of US12,096,982B2
1. A head-mounted device comprising:
one or more inward-facing displays configured to face eyes of a user and present image content related to an environment of the user;
one or more inward-facing infrared sensors configured to project a light pattern onto a cornea of the user using infrared emitting diodes and capture a reflection of the light pattern reflected from the cornea; and
one or more processors and memory storing instructions that, when executed by the one or more processors, cause operations comprising:
storing a machine learning model, comprising one or more hidden neural network layers, trained to predict one or more eye conditions, each hidden layer of the one or more hidden neural network layers comprising one or more weights;
presenting the image content related to the environment via the one or more inward-facing displays of the head-mounted device;
projecting the light pattern onto the cornea of the user via the one or more inward-facing infrared sensors of the head-mounted device;
obtaining reflection data related to the reflection of the light pattern via the one or more inward-facing infrared sensors of the head-mounted device; and
generating, via the machine learning model stored on the head-mounted device, an eye-related indication based on one or more anomalies detected in the reflection data related to the reflection of the light pattern.
2. A method comprising:
causing, via one or more inward-facing displays of a head-mounted device, presentation of image content to a user of the head-mounted device;
causing, via one or more inward-facing infrared devices of the head-mounted device, projection of light emitted from one or more infrared diodes onto a cornea of the user;
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, reflection data related to a reflection of the infrared-diode-emitted light reflected from the cornea; and
generating, via one or more processors of the head-mounted device using a machine learning model comprising model weights, an eye-related indication based on the reflection data comprising one or more anomalies.
3. The method of claim 2, wherein causing the presentation of the image content comprises generating the image content related to an environment of the user and presenting the image content related to the environment on the one or more inward-facing displays of the head-mounted device.
4. The method of claim 2, further comprising:
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, feedback data related to an eye of the user in connection with the presentation of the image content; and
generating, via the machine learning model stored on the head-mounted device, one or more predictions related to an eye condition based on the feedback data.
5. The method of claim 2, further comprising:
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, feedback data related to eye movement of the user in connection with the presentation of the image content; and
generating, via the machine learning model stored on the head-mounted device, one or more predictions related to an eye condition based on the feedback data.
6. The method of claim 2, further comprising:
locally storing, at the head-mounted device, the machine learning model, comprising a neural network, configured to generate outputs indicating one or more eye conditions,
wherein assessing the machine learning model and generating the eye-related indication comprises assessing the machine learning model locally stored at the head-mounted device and using the machine learning model locally stored at the head-mounted device to generate the eye-related indication based on the reflection data comprising the one or more anomalies.
7. The method of claim 2, wherein assessing the machine learning model and generating the eye-related indication comprises remotely assessing the machine learning model, comprising a neural network, via a service and using the remotely-accessed machine learning model to generate the eye-related indication based on the reflection data comprising the one or more anomalies.
8. The method of claim 2, wherein the one or more inward-facing infrared devices comprises one or more inward-facing infrared cameras of the head-mounted device.
9. The method of claim 8, wherein the one or more inward-facing infrared cameras of the head-mounted device comprises infrared emitting diodes and is configured to capture light, emitted from the infrared emitting diodes, that is reflected from an eye of the user.
12. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:
causing, via one or more inward-facing displays of a head-mounted device, presentation of image content to a user of the head-mounted device;
obtaining, via one or more inward-facing infrared sensors of the head-mounted device configured to capture light emitted from one or more infrared diodes, reflection data related to a reflection of the presentation reflected from an eye of the user; and
generating, via an artificial intelligence model, an eye-related indication based on the reflection data comprising one or more anomalies.
13. The one or more non-transitory computer-readable media of claim 12, the operations further comprising causing, via infrared emitting diodes of the head-mounted device, projection of light onto an eye of the user.
14. The one or more non-transitory computer-readable media of claim 12, wherein causing the presentation of the image content comprises generating the image content related to an environment of the user and presenting the image content related to the environment on the one or more inward-facing displays of the head-mounted device.
15. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
obtaining, via the one or more inward-facing infrared sensors of the head-mounted device, feedback data related to an eye of the user in connection with the presentation of the image content; and
generating, via the artificial intelligence model stored on the head-mounted device, one or more predictions related to an eye condition based on the feedback data.
16. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
obtaining, via the one or more inward-facing infrared sensors of the head-mounted device, feedback data related to eye movement of the user in connection with the presentation of the image content; and
generating, via the artificial intelligence model stored on the
head-mounted device, one or more predictions related to an eye condition based on the feedback data.
17. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
locally storing, at the head-mounted device, the artificial intelligence model, comprising a machine learning model, configured to generate outputs indicating one or more eye conditions,
wherein assessing the artificial intelligence model and generating the eye-related indication
comprises assessing the artificial intelligence model locally stored at the head-mounted device and using the artificial intelligence model locally stored at the head-mounted device to generate the eye-related indication based on the
reflection data comprising the one or more anomalies.
18. The one or more non-transitory computer-readable media of claim 12, wherein assessing the artificial intelligence model and generating the eye-related indication
comprises remotely assessing the artificial intelligence model, comprising a machine learning model, via a service and using the remotely-accessed artificial intelligence model to generate the eye-related indication based on the reflection data comprising the one or more anomalies.
19. The one or more non-transitory computer-readable media of claim 12, wherein the one or more inward-facing infrared sensors comprises one or more inward-facing infrared cameras of the head-mounted device.
20. The one or more non-transitory computer-readable media of claim 19, wherein the one or more inward-facing infrared cameras of the head-mounted device comprises infrared emitting diodes and is configured to capture light, emitted from the infrared emitting diodes, that is reflected from an eye of the user.
1. A head-mounted device comprising:
one or more inward-facing displays configured to face eyes of a user and present image content related to an environment of the user;
one or more inward-facing infrared sensors configured to project a light pattern onto a cornea of the user using infrared emitting diodes and capture a reflection of the light pattern reflected from the cornea; and
one or more processors and memory storing instructions that, when executed by the one or more processors, cause operations comprising:
storing a machine learning model trained to predict one or more eye conditions;
presenting the image content related to the environment via the one or more inward-facing displays of the head-mounted device;
projecting the light pattern onto the cornea of the user via the one or more inward-facing infrared sensors of the head-mounted device;
obtaining reflection data related to the reflection of the light pattern via the one or more inward-facing infrared sensors of the head-mounted device; and
generating, via the machine learning model stored on the head-mounted device, a dry-eye-related indication based on one or more anomalies detected in the reflection data related to the reflection of the light pattern.
4. A method comprising:
causing, via one or more inward-facing displays of a head-mounted device, presentation of image content to a user of the head-mounted device;
causing, via one or more inward-facing infrared devices of the head-mounted device, projection of light emitted from one or more infrared diodes onto a cornea of the user;
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, reflection data related to a reflection of the infrared-diode-emitted light reflected from the cornea;
assessing, via one or more processors of the head-mounted device, a machine learning model configured to generate outputs indicating one or more eye conditions; and
generating, via the one or more processors of the head-mounted device using the machine learning model, a
dry-eye-related indication based on the reflection data comprising one or more anomalies.
5. The method of claim 4, wherein causing the presentation of the image content comprises generating the image content related to an environment of the user and presenting the image content related to the environment on the one or more inward-facing displays of the head-mounted device.
6. The method of claim 4, further comprising:
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, feedback data related to an eye of the user in connection with the presentation of the image content; and
generating, via the machine learning model stored on the head-mounted device, one or more predictions related to an eye condition based on the feedback data.
7. The method of claim 4, further comprising:
obtaining, via the one or more inward-facing infrared devices of the head-mounted device, feedback data related to eye movement of the user in connection with the presentation of the image content; and
generating, via the machine learning model stored on the head-mounted device, one or more predictions related to an eye condition based on the feedback data.
8. The method of claim 4, further comprising:
locally storing, at the head-mounted device, the machine learning model
configured to generate outputs indicating the one or more eye conditions,
wherein assessing the machine learning model and generating the dry-eye-related indication comprises assessing the machine learning model locally stored at the head-mounted device and using the machine learning model locally stored at the head-mounted device to generate the dry-eye-related indication based on the reflection data comprising the one or more anomalies.
9. The method of claim 4, wherein assessing the machine learning model and generating the dry-eye-related indication comprises remotely assessing the machine learning model
via a service and using the remotely-accessed machine learning model to generate the dry-eye-related indication based on the reflection data comprising the one or more anomalies.
10. The method of claim 4, wherein the one or more inward-facing infrared devices comprises one or more inward-facing infrared cameras of the head-mounted device.
11. The method of claim 10, wherein the one or more inward-facing infrared cameras of the head-mounted device comprises infrared emitting diodes and is configured to capture light, emitted from the infrared emitting diodes, that is reflected off of an eye surface of the user.
12. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:
causing, via one or more inward-facing displays of a head-mounted device, presentation of image content to a user of the head-mounted device;
obtaining, via one or more inward-facing infrared sensors of the head-mounted device configured to capture light emitted from one or more infrared diodes, reflection data related to a reflection of the presentation reflected off of an eye surface of the user;
assessing a prediction model configured to generate outputs indicating one or more eye conditions; and
generating, via the prediction model, a dry-eye-related indication based on the reflection data comprising one or more anomalies.
13. The one or more non-transitory computer-readable media of claim 12, the operations further comprising causing, via infrared emitting diodes of the head-mounted device, projection of light onto an eye surface of the user.
14. The one or more non-transitory computer-readable media of claim 12, wherein causing the presentation of the image content comprises generating the image content related to an environment of the user and presenting the image content related to the environment on the one or more inward-facing displays of the head-mounted device.
15. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
obtaining, via the one or more inward-facing infrared sensors of the head-mounted device, feedback data related to an eye of the user in connection with the presentation of the image content; and
generating, via the prediction model stored on the
head-mounted device, one or more predictions related to an eye condition based on the feedback data.
16. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
obtaining, via the one or more inward-facing infrared sensors of the head-mounted device, feedback data related to eye movement of the user in connection with the presentation of the image content; and
generating, via the prediction model stored on the
head-mounted device, one or more predictions related to an eye condition based on the feedback data.
17. The one or more non-transitory computer-readable media of claim 12, the operations further comprising:
locally storing, at the head-mounted device, the prediction model
configured to generate
outputs indicating the one or more eye conditions,
wherein assessing the prediction model and generating
the dry-eye-related indication comprises assessing the prediction model locally stored
at the head-mounted device and using the prediction model
locally stored at the head-mounted device to generate the dry-eye-related indication based on the reflection data comprising the one or more anomalies.
18. The one or more non-transitory computer-readable media of claim 12, wherein assessing the prediction model and generating
the dry-eye-related indication comprises remotely assessing the prediction model
via a service and using the remotely-accessed
prediction model to generate the dry-eye-related indication based on the reflection data comprising the one or more anomalies.
19. The one or more non-transitory computer-readable media of claim 12, wherein the one or more inward-facing infrared sensors comprises one or more inward-facing infrared cameras of the head-mounted device.
20. The one or more non-transitory computer-readable media of claim 19, wherein the one or more inward-facing infrared cameras of the head-mounted device comprises infrared emitting diodes and is configured to capture light, emitted from the infrared emitting diodes, that is reflected off of an eye surface of the user.
Allowable Subject Matter
Claims 10-11 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 10-11, the prior art doesn’t teach:
10. The method of claim 2, wherein generating the eye-related indication comprises generating, via the one or more processors of the head-mounted device using the machine learning model, a refraction-error-related indication based on the reflection data comprising the one or more anomalies.
11. The method of claim 2, wherein generating the eye-related indication comprises generating, via the one or more processors of the head-mounted device using the machine learning model, a dynamic-aberration-related indication based on the reflection data comprising the one or more anomalies.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Thur & Sat: 2pm-10pm.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612
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