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 .
Claim Objections
Claim 5 is objected to because of the following informalities: claim 5 recites “complexity a number” which renders the claim incomplete. Applicant should amend the claim to separate “complexity” from “a number”. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: ‘SD” and “USB” should be spelled out for at least the first instance in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5, 26 and 27 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Athreya et al (US 20230048206).
As to claim 1, Athreya discloses a method (FIGS. 1-2) comprising:
determining, by a device, a power saving criteria associated with an amount of power consumed by the device for performing analytics (see [0025], determining 102 the environmental condition … indicate an illumination condition; see [0026], The apparatus may control 104 a machine learning model structure based on the environmental condition to control (or regulate, for example) apparatus power consumption associated with a processing load of the machine learning model structure … The apparatus may control 104 the machine learning model structure based on the environmental condition by controlling the number of machine learning models (e.g., neural networks) and/or the number of machine learning model components the machine learning model structure; FIG. 2, step 202; see [0030], L1, L2, … L5), wherein determining the power saving criteria comprises receiving, via a user interface on the device (see [0025]), a user input selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy (see [0076], receive an indication 568 of an environmental condition … the user may provide an indication 568 of a low set illumination level. This may cause a more complex machine learning model structure (e.g., deeper network) with higher precision to be selected to provide higher accuracy … a set illumination level or levels (e.g., IL1, IL2, etc.));
selecting, by the device, from among two or more pre-trained neural networks configured for performing the analytics under different power saving criteria, a neural network that is configured for performing the analytics under the received power saving criteria (see [0031], controlling 104 the machine learning model may include selecting a machine learning model or models from a machine learning model ensemble … The machine learning model ensemble may include multiple machine learning models (e.g., pre-trained deep neural networks (DNNs)), from which machine learning model or models are selected to reduce apparatus power consumption during inferencing; see [0049]; FIG. 2, steps 204-206), wherein the two or more pre-trained neural networks differ from one another in number of nodes and/or edges such that a neural network having fewer nodes or edges is selected responsive to selection of a more restrictive power-saving level power (see [0026]-[0028], A machine learning model structure may vary in processing load and/or power consumption based on a number of machine learning models included in the machine learning model structure and/or a number of machine learning model components (e.g., layers, nodes, connections, etc.) included in the machine learning model structure … the apparatus may control 104 the machine learning model structure by reducing machine learning model structure complexity when the environmental condition is favorable to inferencing accuracy (e.g., when the environmental condition may increase inferencing accuracy). Reducing the machine learning model structure complexity may reduce the processing load and/or power consumption associated with the machine learning model structure); and
using the selected neural network to perform the analytics by the device (see [0051]).
As to claim 2, Athreya further discloses wherein the device comprises a camera (see [0071]), wherein the analytics comprises image or video analytics (see [0051]).
As to claim 5, Athreya further discloses wherein the power saving criteria is associated with an amount of data captured by the camera under different environmental or scene complexity a number and types of objects detected in a scene captured by the camera, the power saving criteria specifying a target tradeoff between power consumption and analytics accuracy for the detected number and types of objects (see [0026], [0032]).
As to claim 26, Athreya discloses a computing device (FIG. 3) comprising:
a memory storing instructions (FIG. 3, memory 306; see [0055]); and
a processor communicatively coupled with the memory and configured to execute the instructions (FIG. 3, processor 304; see [0054]) to:
determine, by a device, a power saving criteria associated with an amount of power consumed by the device for performing analytics (see [0025], determining 102 the environmental condition … indicate an illumination condition; see [0026], The apparatus may control 104 a machine learning model structure based on the environmental condition to control (or regulate, for example) apparatus power consumption associated with a processing load of the machine learning model structure … The apparatus may control 104 the machine learning model structure based on the environmental condition by controlling the number of machine learning models (e.g., neural networks) and/or the number of machine learning model components the machine learning model structure; FIG. 2, step 202; see [0030], L1, L2, … L5), wherein to determine the power saving criteria the processor is configured to receive, via a user interface on the device (see [0025]), a user input selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy (see [0076], receive an indication 568 of an environmental condition … the user may provide an indication 568 of a low set illumination level. This may cause a more complex machine learning model structure (e.g., deeper network) with higher precision to be selected to provide higher accuracy … a set illumination level or levels (e.g., IL1, IL2, etc.));
select, by the device, from among two or more pre-trained neural networks configured for performing the analytics under different power saving criteria, a neural network that is configured for performing the analytics under the received power saving criteria (see [0031], controlling 104 the machine learning model may include selecting a machine learning model or models from a machine learning model ensemble … The machine learning model ensemble may include multiple machine learning models (e.g., pre-trained deep neural networks (DNNs)), from which machine learning model or models are selected to reduce apparatus power consumption during inferencing; see [0049]; FIG. 2, steps 204-206), wherein the two or more pre-trained neural networks differ from one another in number of nodes and/or edges such that a neural network having fewer nodes or edges is selected responsive to selection of a more restrictive power-saving level (see [0026]-[0028], A machine learning model structure may vary in processing load and/or power consumption based on a number of machine learning models included in the machine learning model structure and/or a number of machine learning model components (e.g., layers, nodes, connections, etc.) included in the machine learning model structure … the apparatus may control 104 the machine learning model structure by reducing machine learning model structure complexity when the environmental condition is favorable to inferencing accuracy (e.g., when the environmental condition may increase inferencing accuracy). Reducing the machine learning model structure complexity may reduce the processing load and/or power consumption associated with the machine learning model structure); and
use the selected neural network to perform the analytics by the device (see [0051]).
As to claim 27, Athreya discloses a non-transitory computer-readable medium storing instructions executable by a processor of a computing device (see [0062]-[0063]), wherein the instructions, when executed, cause to the processor to:
determine, by a device, a power saving criteria associated with an amount of power consumed by the device for performing analytics (see [0025], determining 102 the environmental condition … indicate an illumination condition; see [0026], The apparatus may control 104 a machine learning model structure based on the environmental condition to control (or regulate, for example) apparatus power consumption associated with a processing load of the machine learning model structure … The apparatus may control 104 the machine learning model structure based on the environmental condition by controlling the number of machine learning models (e.g., neural networks) and/or the number of machine learning model components the machine learning model structure; FIG. 2, step 202; see [0030], L1, L2, … L5), wherein to determine the power saving criteria the instructions cause the processor to receive, via a user interface on the device (see [0025]), a user input selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy (see [0076], receive an indication 568 of an environmental condition … the user may provide an indication 568 of a low set illumination level. This may cause a more complex machine learning model structure (e.g., deeper network) with higher precision to be selected to provide higher accuracy … a set illumination level or levels (e.g., IL1, IL2, etc.));
select, by the device, from among two or more pre-trained neural networks configured for performing the analytics under different power saving criteria, a neural network that is configured for performing the analytics under the received power saving criteria (see [0031], controlling 104 the machine learning model may include selecting a machine learning model or models from a machine learning model ensemble … The machine learning model ensemble may include multiple machine learning models (e.g., pre-trained deep neural networks (DNNs)), from which machine learning model or models are selected to reduce apparatus power consumption during inferencing; see [0049]; FIG. 2, steps 204-206), wherein the two or more pre-trained neural networks differ from one another in number of nodes and/or edges such that a neural network having fewer nodes or edges is selected responsive to selection of a more restrictive power-saving level (see [0026]-[0028], A machine learning model structure may vary in processing load and/or power consumption based on a number of machine learning models included in the machine learning model structure and/or a number of machine learning model components (e.g., layers, nodes, connections, etc.) included in the machine learning model structure … the apparatus may control 104 the machine learning model structure by reducing machine learning model structure complexity when the environmental condition is favorable to inferencing accuracy (e.g., when the environmental condition may increase inferencing accuracy). Reducing the machine learning model structure complexity may reduce the processing load and/or power consumption associated with the machine learning model structure); and
use the selected neural network to perform the analytics by the device (see [0051]).
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.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Yang et al (US 20220094847).
As to claim 3, Athreya fails to explicitly disclose wherein determining the power saving criteria comprises selecting between a day-time operation and a night-time operation, wherein the two or more neural networks comprise:
a first neural network configured for performing the image or video analytics during the day-time operation; and
a second neural network configured for performing the image or video analytics during the night-time operation.
However, Yang teaches wherein determining the power saving criteria comprises selecting between a day-time operation and a night-time operation (FIG. 6, day mode at 202 and night mode at 210), wherein the two or more neural networks comprise:
a first neural network configured for performing the image or video analytics during the day-time operation (FIG. 6, step 206; see [0071]); and
a second neural network configured for performing the image or video analytics during the night-time operation (FIG. 6, step 214; see [0072]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Yang’s teachings to include wherein determining the power saving criteria comprises selecting between a day-time operation and a night-time operation, wherein the two or more neural networks comprise: a first neural network configured for performing the image or video analytics during the day-time operation; and a second neural network configured for performing the image or video analytics during the night-time operation in order to reduce power consumption and to provide a high performance image processing and computer vision pipeline in minimal area and with minimal power consumption (Yang; [0050], [0054]).
As to claim 4, the combination of Athreya and Yang further discloses wherein the camera is configured to capture colored image or video during the day-time operation (Yang; see [0071]), wherein the camera is configured to capture black and white image or video during the night-time operation (Yang; see [0072]), wherein the first neural network has a greater number of nodes or edges as compared to the second neural network (Athreya: see [0035]-[0036]; Yang: see [0072]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Kim et al (US 10120428).
As to claim 6, Athreya further discloses wherein determining the power saving criteria comprises receiving a selection of a power saving mode via a user interface displayed on the camera (see (see [0025], [0076]).
Athreya fails to explicitly disclose the user interface concurrently displaying real-time measurements of CPU usage and power consumption of the camera at the time the selection is received.
However, Kim teaches the user interface concurrently displaying real-time measurements of CPU usage and power consumption of the camera at the time the selection is received (FIG. 15, FIG. 18).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Kim’s teachings to include the user interface concurrently displaying real-time measurements of CPU usage and power consumption of the camera at the time the selection is received in order to improve power management in an electronic device (Kim; col. 1, lines 40-41).
Claim(s) 7-8 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Gigot (US 9906722).
As to claim 7, Athreya fails to explicitly disclose further comprising:
determining, by the camera, whether the image or video analytics performed at the camera has returned a detection result within a threshold period of time; and
placing, by the camera, the image or video analytics in a sleep mode responsive to an absence of any detection results returned by the image or video analytics.
However, Gigot teaches determining, by the camera, whether the image or video analytics performed at the camera has returned a detection result within a threshold period of time (FIG. 7, step 330); and
placing, by the camera, the image or video analytics in a sleep mode responsive to an absence of any detection results returned by the image or video analytics (FIG. 7, step 304).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Gigot’s teachings to include determining, by the camera, whether the image or video analytics performed at the camera has returned a detection result within a threshold period of time; and placing, by the camera, the image or video analytics in a sleep mode responsive to an absence of any detection results returned by the image or video analytics in order to conserve power (Gigot; col. 14, lines 55-64).
As to claim 8, the combination of Athreya and Gigot further discloses further comprising:
determining, by the camera, subsequent to placing the image or video analytics in the sleep mode, whether a motion is detected in a vicinity of the camera (Gigot; FIG. 7, step 308); and
resuming, by the camera, the image or video analytics responsive to detection of the motion in the vicinity of the camera (Gigot; FIG. 7, step 320).
As to claim 15, Although Athreya discloses a peripheral connection port of the camera, the port comprising one of an SD slot, a mini SD slot, a micro SD slot, or a USB port (see [0025]), Athreya fails to explicitly disclose further comprising:
determining, by the camera, whether the peripheral connection port of the camera is connected to any peripheral devices; and
placing, by the camera, the peripheral connection port in a low power state responsive to determining that the peripheral connection port is not connected to any peripheral devices.
However, Gigot teaches determining, by the camera, whether a peripheral connection port of the camera is connected to any peripheral devices (col. 7, lines 53-59: motion sensors 102a-102b, the ambient light sensor 104); and
placing, by the camera, the peripheral connection port in a low power state responsive to determining that the peripheral connection port is not connected to any peripheral devices (col. 7, lines 53-67).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Gigot’s teachings to include determining, by the camera, whether a peripheral connection port of the camera is connected to any peripheral devices; and placing, by the camera, the peripheral connection port in a low power state responsive to determining that the peripheral connection port is not connected to any peripheral devices in order to reduce power consumption (Gigot; col. 7, lines 53-59).
As to claim 16, the combination of Athreya and Gigot further discloses wherein placing the peripheral connection port in the low power state comprises turning off the peripheral connection port (Gigot; col. 7, lines 53-67: disabling devices).
As to claim 17, the combination of Athreya and Gigot further discloses wherein placing the peripheral connection port in the low power state comprises reducing a polling rate of the peripheral connection port for data (Gigot; col. 7, lines 53-67: disabling devices).
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Lim et al (US 9838641).
As to claim 9, Athreya further discloses further comprising:
receiving, by the camera, via a user interface on the camera, a power saving criteria associated with an amount of power consumed by the camera for performing the image or video analytics (see [0025]-[0026]).
Athreya fails to explicitly disclose performing, by the camera, the image or video analytics on image or video data having a frames per second “FPS” value or an image resolution value configured to meet the power saving criteria.
However, Lim teaches performing, by the camera, the image or video analytics on image or video data having a frames per second “FPS” value or an image resolution value configured to meet the power saving criteria (col. 6, lines 24-51).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Lim’s teachings to include performing, by the camera, the image or video analytics on image or video data having a frames per second “FPS” value or an image resolution value configured to meet the power saving criteria in order to reduce power consumption (Lim; col. 6, lines 17-21).
As to claim 10, the combination of Athreya and Lim further discloses wherein receiving the power saving criteria comprises receiving the FPS value or the image resolution value via the user interface on the camera (Lim; col. 6, lines 24-51).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Abramson et al (US 20190042483).
As to claim 13, Athreya fails to explicitly disclose further comprising:
determining, by the camera, whether all streams in a video pipeline of the camera are being used to stream video data output by the camera; and
closing, by the camera, one or more streams and one or more associated buffers responsive to determining that the one or more streams are not being used.
However, Abramson teaches determining, by the camera, whether all streams in a video pipeline of the camera are being used to stream video data output by the camera (see [0049], [0084]); and
closing, by the camera, one or more streams and one or more associated buffers responsive to determining that the one or more streams are not being used (see [0049], [0084]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Abramson’s teachings to include determining, by the camera, whether all streams in a video pipeline of the camera are being used to stream video data output by the camera; and closing, by the camera, one or more streams and one or more associated buffers responsive to determining that the one or more streams are not being used in order to reduce power consumption (Abramson; [0049]).
As to claim 14, the combination of Athreya and Abramson further discloses further comprising: closing the video pipeline responsive to determining that no streams in the video pipeline are being used (Abramson; [0049], [0084]).
Claim(s) 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Liu et al (US 20200307455).
As to claim 18, Athreya fails to explicitly disclose further comprising:
determining, by the camera, whether a defogging of a lens of the camera is required; and
controlling, by the camera, a heater configured to defog the lens of the camera responsive to determining that the defogging of the lens of the camera is required.
However, Liu teaches determining, by the camera, whether a defogging of a lens of the camera is required (see [0012]); and
controlling, by the camera, a heater configured to defog the lens of the camera responsive to determining that the defogging of the lens of the camera is required (see [0012]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Liu’s teachings to include determining, by the camera, whether a defogging of a lens of the camera is required; and controlling, by the camera, a heater configured to defog the lens of the camera responsive to determining that the defogging of the lens of the camera is required in order to perform defogging process of the lens to increase image quality (Liu; [0011]-[0012]).
As to claim 19, the combination of Athreya and Liu further discloses wherein determining whether the defogging is required comprises analyzing a blurriness or a sharpness of images captured by the camera (Liu; [0012]).
As to claim 20, the combination of Athreya and Liu further discloses wherein controlling the heater comprises:
analyzing a blurriness or a sharpness of images captured by the camera (Liu; [0012]); and
controlling a power supplied to the heater based on the blurriness or the sharpness of the images captured by the camera (Liu; [0012]).
As to claim 21, the combination of Athreya and Liu further discloses wherein controlling the heater comprises controlling a power supplied to the heater according to a stored power curve or table stored on the camera (Liu; [0017]).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya et al (US 20230048206) in view of Andrei et al (US 20200349681).
As to claim 24, Athreya fails to explicitly disclose wherein the two or more neural networks comprise a generative adversarial network “GAN” that comprises a generator neural network and a discriminator neural network.
However, Andrei teaches wherein the two or more neural networks comprise a generative adversarial network “GAN” that comprises a generator neural network and a discriminator neural network (see [0056], [0094]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Athreya using Andrei’s teachings to include wherein the two or more neural networks comprise a generative adversarial network “GAN” that comprises a generator neural network and a discriminator neural network in order to perform image enhancement while conserving available power (Andrei; [0032]).
Allowable Subject Matter
Claims 11, 12, 22, 23 and 25 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.
Response to Arguments
Applicant's arguments filed on 04/29/2026 have been fully considered but they are not persuasive.
Applicant first argues that Athreya does not disclose “receiving, via a user interface on the device, a user input selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy.” The examiner respectfully disagrees. Applicant’s specification (see paragraphs [0048]-[0049] of the published specification) recites “generating and training neural network models for low light levels, thus reducing CPU usage and power consumption during low light operation as compared to normal light operation … wo separate neural networks may be installed on the camera 104, one for use during day-time operation and another one for use during night-time operation.” Therefore, the “selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy” encompasses neural networks for different light levels. Athreya discloses in [0025], receiving an indication of the environmental condition … input devices may include a touch screen; and in [0076], the user may provide the indication 568 that indicates a set environmental condition … The set environmental condition may depend on illumination … the user may provide an indication 568 of a low set illumination level. This may cause a more complex machine learning model structure (e.g., deeper network) with higher precision to be selected to provide higher accuracy … a set illumination level or levels (e.g., IL1, IL2, etc.). Therefore, Athreya discloses “receiving, via a user interface on the device, a user input selecting the power saving criteria from among a plurality of predefined power saving levels representing different tradeoffs between power consumption and analytics accuracy.”
Applicant also argues that Athreya does not disclose pre-trained networks that differ specifically in node or edge count and that are selected as a function of a user-selected level along a power-vs.-accuracy continuum. The examiner respectfully disagrees. Athreya discloses in [0026]-[0027], A machine learning model structure may vary in processing load and/or power consumption based on a number of machine learning models included in the machine learning model structure and/or a number of machine learning model components (e.g., layers, nodes, connections, etc.) included in the machine learning model structure … the apparatus may control 104 the machine learning model structure by reducing machine learning model structure complexity when the environmental condition is favorable to inferencing accuracy (e.g., when the environmental condition may increase inferencing accuracy). Reducing the machine learning model structure complexity may reduce the processing load and/or power consumption associated with the machine learning model structure. When the environmental condition is favorable to inferencing accuracy, inferencing accuracy may be maintained while the machine learning model structure complexity is reduced. In some examples, the apparatus may increase machine learning model structure complexity when the environmental condition is unfavorable to inferencing accuracy (e.g., when the environmental condition may decrease inferencing accuracy). When the environmental condition is unfavorable to inferencing activity, inferencing accuracy may be maintained (e.g., inferencing errors may be avoided) by increasing the machine learning model structure complexity … For instance, a higher inferencing level may be associated with greater machine learning model structure complexity, and a lower inferencing level may be associated with lesser machine learning model complexity. Different inferencing levels may correspond to or may be mapped to different environmental conditions. Therefore, Athreya discloses pre-trained networks that differ specifically in node or edge count and that are selected as a function of a user-selected level along a power-vs.-accuracy continuum.
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 BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at 5712727331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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