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 filed Amendments/RCE
Applicant’s Amendments/Remarks filed on 06/12/2026 have been received and made of record. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/15/2026 has been entered.
Claims 1, 8, and 15 have been amended.
Claims 2, 9-10, 16, and 20 have been cancelled.
New claims 22-25 have been added.
Claims 1, 3-8, 11-15, 17-19, and 21-25 are currently pending.
Please refer to the action below.
Examiner Notes
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. However, the claimed subject matter, not the specification, is the measure of the invention.
Response to Remarks/Arguments
Applicants’ arguments of 06/12/2026, corresponding to pages 7-13 pertaining to the prior arts of record of Alrod in view of Kim, specifically to the arguments of Kim of pages 7-13 pertaining to the currently amended independent claims 1, 8, and 15, have been considered, but they are moot in light of the new ground of rejections.
Applicant arguments regarding the prior art of Alrod of pages 7-13 citing “For example, Alrod is only concerned with a single lighting metric being met, "a target mean color for the participant face or facial feature." See Alrod, paragraph 86. Thus, Alrod cannot be said to teach "comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal."…… Alrod is directed to improving visibility of participant faces or facial features in a video conferencing environment, specifically by adjusting lighting to achieve a target mean face color….Alrod's purpose is to ensure accurate and consistent illumination of faces, which is critical for visibility, recognition, and communication in video conferencing. This requires precise and often continuous adjustment of lighting to maintain a target facial appearance…..Alrod operates based on a feedback-driven optimization process, adjusting lighting "until one or more of the detected face images is approximately equal to a target mean color" (Alrod, paragraph 23)”.
The Examiner respectfully partially agrees with the Applicant above assertions of Alrod teaching asserting that (“Alrod is directed to improving visibility of participant faces or facial features in a video conferencing environment, specifically by adjusting lighting to achieve a target mean face color….Alrod's purpose is to ensure accurate and consistent illumination of faces, which is critical for visibility, recognition, and communication in video conferencing. This requires precise and often continuous adjustment of lighting to maintain a target facial appearance…..Alrod operates based on a feedback-driven optimization process, adjusting lighting "until one or more of the detected face images is approximately equal to a target mean color").
The Examiner, however, disagrees with the above assertions that “For example, Alrod is only concerned with a single lighting metric being met, "a target mean color for the participant face or facial feature." See Alrod, paragraph 86. Thus, Alrod cannot be said to teach "comparing a plurality of lighting features of the scene”. As, Alrod clearly teaches the determined lighting properties of color feature, brightness feature, and intensity features corresponding to the target mean color goal for causing the system to adjust the lighting system according to the lighting goal and a case of continually repeating the analyzing and the comparing through one or more iterations at different frames and scenes of the video stream.
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 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) 1, 8, and 15 is/are rejected under 35 U.S.C. 103 as obvious over Alrod et al. (US 20170324933, previously cited), in view of LU et al. (CN 112906682, A1).
Regarding claim 1, Alrod teaches a method (at least the Abstract and para. 0079 teaches at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features),
the method comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
determining a lighting goal wherein the lighting goal defines a plurality of required lighting specifications and is determined by an artificial intelligence model (the system further teaches in at least the Abstract and para. 0079 at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features according to at least para. 0013, 0021-0023, 0028 and 0086 a target mean color as a lighting goal based on video feature contents of for the participant faces detected in the meeting video);
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0013, 0023, 0028 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system):
comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal (further comparing the lighting intensity, color, brightness properties indicative of the plurality lighting features of further para. 0013, 0023, 0070 and 0086 to the target mean color indicative of the lighting goal for the one or more participant faces or facial features of those in the meeting video within said lighting goal); and
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0023, and 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein said lighting goal defines a plurality of required lighting specifications and is determined by an artificial intelligence model; comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
LU teaches the determining of a scene of a target endoscopic video image according to a deep learning model being in the art a representative example of an artificial intelligence model, where the scene of the target image is in vivo scene which target lighting source goal as cited at least in the Abstract is to ensure that “collected image brightness is suitable under different scenes” of the target endoscopic video image; the lighting brightness goals further defines at least in the disclosure and cited tables I and II definable plurality of required lighting specifications being further determined by deep learning model which may as understood in the art comprises obviously an artificial intelligence model; the lighting features in the different video scenes or frames of further cited tables I and II and the disclosure are further compared to at least one a plurality of lighting luminance/brightness features of the scene to the plurality of required lighting specifications/ranges within the lighting goal; where in a case the lighting brightness goal is not satisfied, the system as noted in the disclosure and the Abstract adapted for adjusting the lighting system according to the lighting goal. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of LU to include wherein said lighting goal defines a plurality of required lighting specifications and is determined by an artificial intelligence model; comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of LU are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with a video scene according to a lighting goal, LU’s combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes of LU when combined with the smart lighting control methods and systems of Alrod further enables continuous and iterative adjustment of the lighting system based on realtime video scenes or contents according to the said lighting goal wherein ultimately realizing a high quality lighting color and brightness video conference experience according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 8, Alrod teaches in at least para. 0011 a computer system comprising:
a processor set (any set of one, or more, storage media collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given medium of at least para. 0036-0037 and 0065),
one or more computer-readable storage media (memory 228 of at least para. 0036-0037, and 0065); and program instructions (para. 0036-0037, and 0065); stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
determining a lighting goal based on lighting features within a reference video stream (the system further teaches in at least the Abstract and para. 0079 at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features according to at least para. 0021-0023, 0028, 0071 and 0086 a target mean color as a lighting goal based on video feature contents and lighting properties of the participant faces detected in the meeting video);
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0023 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system):
comparing a plurality of lighting features of the scene within the lighting goal (further comparing the lighting intensity, color, brightness properties indicative of the plurality lighting features of further para. 0013, 0023, 0070 and 0086 to the target mean color indicative of the lighting goal for the one or more participant faces or facial features of those in the meeting video within said lighting goal); and
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein the lighting goal defines a plurality of required lighting specifications based on lighting specifications within the reference video stream; and comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
LU teaches the determining of a scene of a target endoscopic video image according to a deep learning model, where the scene of the target image is in vivo scene which target lighting source goal as cited at least in the Abstract is to ensure that “collected image brightness is suitable under different scenes” of the target endoscopic video image; the lighting brightness goals further defines at least in the disclosure and cited tables I and II definable plurality of required lighting specifications being further determined by deep learning model which may as understood in the art comprises obviously an artificial intelligence model; the lighting features in the different video scenes or frames of further cited tables I and II and the disclosure are further compared to at least one a plurality of lighting luminance/brightness features of the scene to the plurality of required lighting specifications/ranges within the lighting goal; where in a case the lighting brightness goal is not satisfied, the system as noted in the disclosure and the Abstract adapted for adjusting the lighting system according to the lighting goal. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of LU to include wherein said lighting goal defines a plurality of required lighting specifications based on lighting specifications within the reference video stream; and comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of LU are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with a video scene according to a lighting goal, LU’s combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes of LU when combined with the smart lighting control methods and systems of Alrod further enables continuous and iterative adjustment of the lighting system based on realtime video scenes or contents according to the said lighting goal wherein ultimately realizing a high quality lighting color and brightness video conference experience according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 15, Alrod teaches in at least para. 0011 a computer program product (memory 228 of at least para. 0036-0037, and 0065) comprising: one or more computer-readable tangible storage media (para. 0036-0037, and 0065);
and program instructions stored on the one or more tangible storage media to perform operations (para. 0065) comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0023, 0028 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system);
determining a lighting goal based on the scene in the video stream, wherein the lighting goal is based on a context of the scene (the system further teaches in at least the Abstract and para. 0079 at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features according to an asserted target mean color further indicative of a lighting goal as further depicted para. 0023, 0028 and 0086, the target mean color of further para. 0011-0017 is based at least on a cited context of the participant and meeting scene);
comparing a plurality of lighting features of the scene
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein said lighting goal defines a plurality of required lighting specifications based on the context of the scene and comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
LU teaches the determining of a scene of a target endoscopic video image according to a deep learning model, where the scene of the target image is in vivo scene which target lighting source goal as cited at least in the Abstract is to ensure that “collected image brightness is suitable under different scenes” of the target endoscopic video image; the lighting brightness goals further defines at least in the disclosure and cited tables I and II definable plurality of required lighting specifications being further determined by deep learning model which may as understood in the art comprises obviously an artificial intelligence model; the lighting features in the different video scenes or frames of further cited tables I and II and the disclosure are further compared to at least one a plurality of lighting luminance/brightness features of the scene to the plurality of required lighting specifications/ranges within the lighting goal; where in a case the lighting brightness goal is not satisfied, the system as noted in the disclosure and the Abstract adapted for adjusting the lighting system according to the lighting goal. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of LU to include wherein said lighting goal defines a plurality of required lighting specifications based on lighting specifications within the reference video stream; and comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of LU are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with a video scene according to a lighting goal, LU’s combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the deep learning system controlled lighting coupled and matched with the lighting depicted in the streamed video scenes of LU when combined with the smart lighting control methods and systems of Alrod further enables continuous and iterative adjustment of the lighting system based on realtime video scenes or contents according to the said lighting goal wherein ultimately realizing a high quality lighting color and brightness video conference experience according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 1, 3-8, 11-15, 17-19, 21, and 23-24 is/are further rejected under 35 U.S.C. 103 as obvious over Alrod in view of Spero et al. (US 20150035440, A1).
Regarding claim 1, Alrod teaches a method (at least the Abstract and para. 0079 teaches at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features),
the method comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
determining a lighting goal wherein the lighting goal
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0013, 0023, 0028 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system):
comparing a plurality of lighting features of the scene within the lighting goal (further comparing the lighting intensity, color, brightness properties indicative of the plurality lighting features of further para. 0013, 0023, 0070 and 0086 to the target mean color indicative of the lighting goal for the one or more participant faces or facial features of those in the meeting video within said lighting goal); and
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0023, and 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein said lighting goal defines a plurality of required lighting specifications and is determined by an artificial intelligence model; comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
Spero teaches at least in Fig. 15 and para. 0132, 0183, 0191 and 0046 an artificial intelligence-controlled lighting system configured to identify a received scene lighting context and identify from the scene ie. Surfaces, objects and/or people, and further identifying the lighting system, and reached lighting luminance levels further indicative of the lighting goals further defining lighting best practice illumination levels, and cited luminance specification further indicative of the plurality of required lighting specifications determined by the artificial intelligence model by at least comparing the plurality of lighting intensity, luminance features of the scene to the plurality of required lighting specifications within the lighting goal for adjusting said lighting system according to the lighting goal in response to determining the lighting goal is not satisfied. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein said lighting goal defines a plurality of required lighting specifications and is determined by an artificial intelligence model; comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with an inputted scene based on a lighting goal, Spero’s combination of the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to the input scene complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the artificial intelligence lighting control methods of Spero coupled and matched with the lighting illumination requirements/specifications of further Spero when combined with the smart lighting control methods and systems of Alrod further enables the system to continuously provide iterative adjustment of the lighting system utilizing the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to an input scene context to provide optimal lighting features in a specific lighting application and/or direction which would further optimizes the meeting conference lighting system experience of Alrod according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 3 (according to claim 1), Alrod further teaches wherein the lighting goal includes a reference to a target video stream (lighting target color goal of at least para. 0086 and 0023 further includes a reference to a target video meeting stream).
Regarding claim 4 (according to claim 1), Alrod further teaches wherein evaluating the scene includes evaluating a sequence of multiple frames in the scene (evaluated meeting video session streams of further para. 0023 and 0086 further entails inherently evaluating scenes of at least para. 0044 further including evaluating a sequence of multiple video frames in the scene).
Regarding claim 5 (according to claim 1), Alrod further teaches wherein adjusting includes reverting a previous adjustment that did not bring the scene closer to the lighting goal (adjusting of further para. 0086 further comprises in a case reverting a previous adjustment that did not bring the scene closer to the target color lighting goal).
Regarding claim 6 (according to claim 1), Alrod further teaches wherein the repeating further comprises repeating the analyzing and the repeating occurs until the lighting goal is met (the system further notes “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations).
Regarding claim 7 (according to claim 1), Alrod further implies wherein further comprising: pausing for a pause period wherein adjusting does not occur during the pause period (the adjustment period of at least para. 0086 may obviously comprises at least a period of analyzing and comparing during which the system may obviously pause said adjusting as a case where the adjusting does not occur during the pause period).
Regarding claim 8, Alrod teaches in at least para. 0011 a computer system comprising:
a processor set (any set of one, or more, storage media collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given medium of at least para. 0036-0037 and 0065),
one or more computer-readable storage media (memory 228 of at least para. 0036-0037, and 0065); and program instructions (para. 0036-0037, and 0065); stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
determining a lighting goal based on lighting features within a reference video stream (the system further teaches in at least the Abstract and para. 0079 at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features according to at least para. 0021-0023, 0028, 0071 and 0086 a target mean color as a lighting goal based on video feature contents and lighting properties of the participant faces detected in the meeting video);
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0023 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system):
comparing a plurality of lighting features of the scene within the lighting goal (further comparing the lighting intensity, color, brightness properties indicative of the plurality lighting features of further para. 0013, 0023, 0070 and 0086 to the target mean color indicative of the lighting goal for the one or more participant faces or facial features of those in the meeting video within said lighting goal); and
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein the lighting goal defines a plurality of required lighting specifications based on lighting specifications within the reference video stream; and comparing said plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
Spero teaches at least in Fig. 15 and para. 0132, 0183, 0191 and 0046 an artificial intelligence-controlled lighting system configured to identify a received scene lighting context and identify from the scene ie. Surfaces, objects and/or people, and further identifying the lighting system, and reached lighting luminance levels further indicative of the lighting goals further defining lighting best practice illumination levels, and cited luminance specification further indicative of the plurality of required lighting specifications determined by the artificial intelligence model by at least comparing the plurality of lighting intensity, luminance features of the scene to the plurality of required lighting specifications within the lighting goal for adjusting said lighting system according to the lighting goal in response to determining the lighting goal is not satisfied. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein the lighting goal defines a plurality of required lighting specifications based on lighting specifications within the reference video stream; and comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with an inputted scene based on a lighting goal, Spero’s combination of the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to the input scene complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the artificial intelligence lighting control methods of Spero coupled and matched with the lighting illumination requirements/specifications of further Spero when combined with the smart lighting control methods and systems of Alrod further enables the system to continuously provide iterative adjustment of the lighting system utilizing the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to an input scene context to provide optimal lighting features in a specific lighting application and/or direction which would further optimizes the meeting conference lighting system experience of Alrod according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 11 (according to claim 8), Alrod further teaches wherein evaluating the scene includes evaluating a sequence of multiple frames in the scene (evaluated meeting video session streams of further para. 0023 and 0086 further entails inherently evaluating scenes of at least para. 0044 further including evaluating a sequence of multiple video frames in the scene).
Regarding claim 12 (according to claim 8), Alrod further teaches wherein adjusting includes reverting a previous adjustment that did not bring the scene closer to the lighting goal (adjusting of further para. 0086 further comprises in a case reverting a previous adjustment that did not bring the scene closer to the target color lighting goal).
Regarding claim 13 (according to claim 8), Alrod further teaches wherein the repeating further comprises repeating the analyzing and the repeating occurs until the lighting goal is met (the system further notes “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations).
Regarding claim 14 (according to claim 8), Alrod further implies wherein further comprising: pausing for a pause period wherein adjusting does not occur during the pause period (the adjustment period of at least para. 0086 may obviously comprises at least a period of analyzing and comparing during which the system may obviously pause said adjusting as a case where the adjusting does not occur during the pause period).
Regarding claim 15, Alrod teaches in at least para. 0011 a computer program product (memory 228 of at least para. 0036-0037, and 0065) comprising: one or more computer-readable tangible storage media (para. 0036-0037, and 0065);
and program instructions stored on the one or more tangible storage media to perform operations (para. 0065) comprising:
identifying a lighting system (identifying and control in at least para. 0021-0023, 0028, and 0086 one or more lighting elements and lighting configuration further indicative of said lighting system according to detected user participant faces in a video scene);
analyzing a scene in a video stream, wherein one or more lighting features of the video stream are illuminated by the lighting system (analyze further in at least para. 0023, 0028 and 0086 said scene in a video stream, wherein one or more lighting properties or features of the video stream are illuminated by the lighting system);
determining a lighting goal based on the scene in the video stream, wherein the lighting goal is based on a context of the scene (the system further teaches in at least the Abstract and para. 0079 at least a smart controlled light system employing the light controller of at least para. 0011 to control and adjust light features according to an asserted target mean color further indicative of a lighting goal as further depicted para. 0023, 0028 and 0086, the target mean color of further para. 0011-0017 is based at least on a cited context of the participant and meeting scene);
comparing a plurality of lighting features of the scene
in response to determining the lighting goal is not satisfied, adjusting the lighting system according to the lighting goal (adjusting further in at least para. 0086 in response to determining the lighting target goal mean is not satisfied, said lighting system according to the lighting goal);
repeating the analyzing and the comparing through one or more iterations at different frames of the video stream (the system further notes at least in para. 0023 and the disclosure citing “varying the lighting element(s) properties (e.g., on/off state, color, intensity, brightness, etc.) as well as exposure of the image capture device until one or more of the detected face images is approximately equal to a target mean color for the participant face or facial feature (e.g., skin tone)” further insinuating the system repeating said analyzing and said comparing through one or more implied iterations through continuous adjustment of lighting and continuous iterative adjustments based on real-time feedback from a video stream at implied different frames of the video stream).
Alrod teaches in at least para. 0011-0013, 0021-0023 and 0086 the claimed invention except for the above lined-out items such as wherein said lighting goal defines a plurality of required lighting specifications based on the context of the scene and comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal.
Spero teaches at least in Fig. 15 and para. 0132, 0183, 0191 and 0046 an artificial intelligence-controlled lighting system configured to identify a received scene lighting context and identify from the scene ie. Surfaces, objects and/or people, and further identifying the lighting system, and reached lighting luminance levels further indicative of the lighting goals further defining lighting best practice illumination levels, and cited luminance specification further indicative of the plurality of required lighting specifications determined by the artificial intelligence model by at least comparing the plurality of lighting intensity, luminance features of the scene to the plurality of required lighting specifications within the lighting goal for adjusting said lighting system according to the lighting goal in response to determining the lighting goal is not satisfied. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein said lighting goal defines a plurality of required lighting specifications based on the context of the scene and comparing a plurality of lighting features of the scene to the plurality of required lighting specifications within the lighting goal, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with an inputted scene based on a lighting goal, Spero’s combination of the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to the input scene complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the artificial intelligence lighting control methods of Spero coupled and matched with the lighting illumination requirements/specifications of further Spero when combined with the smart lighting control methods and systems of Alrod further enables the system to continuously provide iterative adjustment of the lighting system utilizing the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to an input scene context to provide optimal lighting features in a specific lighting application and/or direction which would further optimizes the meeting conference lighting system experience of Alrod according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 17 (according to claim 15), Alrod further teaches wherein the lighting goal includes a reference to a target video stream (lighting target color goal of at least para. 0086 and 0023 further includes a reference to a target video meeting stream).
Regarding claim 18 (according to claim 15), Alrod further teaches wherein evaluating the scene includes evaluating a sequence of multiple frames in the scene (evaluated meeting video session streams of further para. 0023 and 0086 further entails inherently evaluating scenes of at least para. 0044 further including evaluating a sequence of multiple video frames in the scene).
Regarding claim 19 (according to claim 15), Alrod further teaches wherein adjusting includes reverting a previous adjustment that did not bring the scene closer to the lighting goal (adjusting of further para. 0086 further comprises in a case reverting a previous adjustment that did not bring the scene closer to the target color lighting goal).
Regarding claim 21 (according to claim 1), Alrod is silent regarding wherein the artificial intelligence model is trained on feedback obtained from past iterations of continuous light adjustment.
Spero further teaches at least in para. 0036, 0111, and 0122 the artificial intelligence-controlled lighting system further learn/train and recalibrates itself based at least on provided on feedback obtained from obviously past iterations of continuous light adjustment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein the artificial intelligence model is trained on feedback obtained from past iterations of continuous light adjustment, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting light feature effects associated with a video scene according to a lighting goal, Spero’s combination of artificial intelligence controlled lighting corresponding to feedback obtained from past iterations of continuous light adjustment complements the smart lighting control methods and systems of Alrod, in the sense that said combination of artificial intelligence controlled lighting coupled and matched with adapted lighting application scenes of Spero when combined with the smart lighting control methods and systems of Alrod further enables continuous and iterative adjustment of the lighting system based on realtime input scenes or contents according to the said lighting goal wherein providing personalized lighting control according to context of an input scene, thereby ultimately as and when combined with the methods and systems of Alrod would provide a high quality user video conference experience according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 23 (according to claim 1), Alrod is silent regarding wherein determining the lighting goal is not satisfied comprises: determining, based on the comparing, whether each respective lighting feature of the scene is within a threshold distance of each respective required lighting specification within the lighting goal.
Spero further teaches the artificial intelligence-controlled lighting system of at least in Fig. 15 and para. 0132, 0191 and 0046 further configured to individually control and identify whether each respective lighting feature of the scene as further depicted in Fig. 15 is within a threshold distance of each respective required lighting specification within the lighting goal, and in a case it is not met to cause adjustment of the lighting system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein determining said lighting goal is not satisfied comprises: determining, based on the comparing, whether each respective lighting feature of the scene is within a threshold distance of each respective required lighting specification within the lighting goal, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with an inputted scene based on a lighting goal, Spero’s combination of the artificial intelligence lighting control methods coupled and matched with detection and comparison of individual lighting illumination element performance corresponding to lighting requirements/specifications further complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the artificial intelligence lighting control methods coupled and matched with detection and comparison of individual lighting illumination element performance corresponding to lighting requirements/specifications of Spero when combined with the smart lighting control methods and systems of Alrod further enables the system to continuously provide iterative adjustment of the lighting system individually and/or as a whole utilizing the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to an input scene context to provide optimal lighting features in a specific lighting application and/or direction which would further optimizes the meeting conference lighting system experience of Alrod according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 24 (according to claim 23), Alrod is silent regarding wherein the determining the lighting goal is not satisfied is based on a lighting feature of the scene not being within the threshold distance of a respective lighting specification within the lighting goal.
Spero further teaches the artificial intelligence-controlled lighting system of at least in Fig. 15 and para. 0132, 0191 and 0046 further configured to individually control and identify whether each respective lighting feature of the scene as further depicted in Fig. 15 is within a threshold distance of each respective required lighting specification within the lighting goal, and in a case lighting goal is not satisfied based on a lighting intensity or luminance feature of the scene not being within the threshold distance of a respective lighting specification to cause adjustment of the lighting system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Alrod in in view of Spero to include wherein determining the lighting goal is not satisfied is based on a lighting feature of the scene not being within the threshold distance of a respective lighting specification within the lighting goal, as discussed above, as Alrod in in view of Spero are in the same field of endeavor of employing a smart lighting control methods and systems for controlling and adjusting determined light feature associated with an inputted scene based on a lighting goal, Spero’s combination of the artificial intelligence lighting control methods coupled and matched with detection and comparison of individual lighting illumination element performance corresponding to lighting requirements/specifications further complements the smart lighting control methods and systems of Alrod, in the sense that said combination of the artificial intelligence lighting control methods coupled and matched with detection and comparison of individual lighting illumination element performance corresponding to lighting requirements/specifications of Spero when combined with the smart lighting control methods and systems of Alrod further enables the system to continuously provide iterative adjustment of the lighting system individually and/or as a whole utilizing the artificial intelligence lighting control methods coupled and matched with the lighting illumination requirements/specifications corresponding to an input scene context to provide optimal lighting features in a specific lighting application and/or direction which would further optimizes the meeting conference lighting system experience of Alrod according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claims Standings
Claims 22 and 25 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 prior arts of record do not appear to teach: claim 22. (New) The method of claim 1, further comprising: determining a new lighting goal to apply to a new scene within the video stream, wherein the new lighting goal defines a second plurality of required lighting specifications; and adjusting the lighting system according to the new lighting goal based on a comparison between a second plurality of lighting features of the new scene and the second plurality of required lighting specifications within the new lighting goal.
25. (New) The method of claim 23, wherein the determining the lighting goal is not satisfied is based on at least two lighting features of the scene not being within the threshold distance of at least two respective lighting specifications within the lighting goal.
Conclusion
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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 07/22/2026
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