DETAILED ACTIONNotice 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 .
Applicant Response to Official Action
The response filed on 6/5/2026 has been entered and made of record.
Acknowledgment
Claims 17 and 19, canceled on 6/5/2026, are acknowledged by the examiner.
Claims 21-22, added on 6/5/2026, are acknowledged by the examiner.
Claims 1-2, 7-9,13, and 18, amended on 6/5/2026, are acknowledged by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, 18 and their dependent claims have been considered but they are moot in view of the new grounds of rejection necessitated by amendments initiated by the applicant. Examiner addresses the main arguments of the Applicant as below.
Regarding the drawing objection, in the spec amendment filed on 6/5/2026, the applicant add “such as the machine-learning model 160 and a second machine-learning model 305” in paragraph [0081]. However, component 305 has never previously mentioned in the specification. Hence it is a new matter. As a result, the drawing objection is maintained.
Regarding the U.S.C. 102 rejection, the Applicant amended the claim with “the estimate of the scene lighting conditions determined using a machine-learning model trained based on one or more shading profiles.” then argued that, “Applicant submits that the asserted references of record do not disclose, teach, or suggest the subject matter of this amendment” [Paragraph 4 on page 10 of the Remarks]. Examiner respectfully disagrees with the Applicant’s argument because it is not persuasive.
In his invention, Donohue discloses the processors in his invention can be configured to run one or more modules to perform calibration based on shading profiles (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]. The shading correction mechanism in his invention can operate in two stages: a training stage and a running stage (The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]. Donohue also discloses that his invention can perform a shading correction scheme (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction) [Donohoe: col. 10, line 66 – col. 11, line 4]. It analyzes multiple shading profiles (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41], and estimate the shading correction mesh (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]. Therefore, the Applicant’s argument is not persuasive. Accordingly, the Examiner respectfully maintains the rejections and applicability of the arts used.
Objections
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, “a second machine-learning model different from the machine-learning model” must be shown or the feature(s) must be canceled from the claims 1-20. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claims 1, 6, 8-12, 16, 18, 20-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Donohoe (US Patent US 9,270,872 B2), (“Donohoe”).
Regarding claim 1, Donohoe meets the claim limitations, as follows:
An apparatus (an apparatus) [Donohoe: col. 2, line 25; Fig. 3] comprising: one or more processors configured to (one or more processors) [Donohoe: col. 18, line 60; Fig. 3]: determine (the module is further configured to determine) [Donohoe: col. 2, line 40-41], for one or more images, correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] to mitigate (The shading effects refer to a phenomenon in which a brightness of an image is reduced) [Donohoe: col. 1, line 58-59] spatial nonuniformity and shading effects based on an estimate of scene lighting conditions of the one or more images ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which
the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 10, line 61 - col. 2, line 5]), the estimate of the scene lighting conditions determined using a machine-learning model ((the module is further configured to determine) [Donohoe: col. 2, line 40-41]; (shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum) [Donohoe: col. 10, line 23-25]) trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]); and apply the correction parameters to the one or more images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
Regarding claim 6, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the correction parameters are applied to the one or more images (a correction mesh for the image) [Donohoe: col. 2, line 35-36] per pixel location or per one or more blocks of pixel values of the one or more images (In some embodiments, when the image of the uniform surface is a color image, then the PU calibration module 314 can stack four adjacent pixels in a 2x2 grid, e.g., Gr 104, R 106, B 108, Gb 110, of the image Iᴨ,Єᴨ (x,y) to form a single pixel, thereby reducing the size of the image into a quarter, The stacked, four-dimensional image is referred to as a stacked input image Iᴨ,Єᴨ (x,y), where each pixel has four values, The PU calibration module 314 can then perform the above process to compute a four-dimensional correction mesh Ci,ᴨp (w,z), where each dimension is associated with one of the d/channel, R channel, B channel, and Gb channel) [Donohoe: col. 13, line 6-16].
Regarding claim 8, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured to (The electronic device can be configured with one or more processors that process instructions and run software that may be stored in memory) [Donohoe: col. 18, line 59-62]: compare image statistics of the one or more images to one or more predetermined thresholds ((In other cases, the correction mesh can have a lower spatial dimensionality compared to the image sensor. Because the shading can vary
slowly across the image, the correction mesh does not need to have the same resolution as the image sensor to fully compensate for the shading effect. Instead, the correction mesh can have a lower spatial dimensionality compared to the
image sensor so that the correction mesh can be stored in a storage medium with a limited capacity, and can be up-sampled prior to being applied to correct the shading effect) [Donohoe: col. 8, line 34-44]; (The vignetting effect refers to a phenomenon in which less light reaches the corners of an image sensor compared to the center of an image sensor. This results in decreasing brightness as one moves away from the center of an image and towards the edges of the image. FIG. 2 illustrates a typical vignetting effect. When a camera is used to capture an image 200 of a uniform white surface, the vignetting effect can render the comers of the image 202 darker than the center of the image 204) [Donohoe: col. 2, line 10-17; Fig. 2]; and in response to the image statistics not satisfying the one or more predetermined thresholds ((When the correction filter C(x,y) is designed to have a lower resolution compared to the image sensor, the correction filter C( x,y) can be computed by inverting a sub-sampled version of the low-pass filtered image) [Donohoe: col. 9, line 15-18]; (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39]), estimate the scene lighting conditions (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29] or determine the correction parameters using the machine-learning model (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]).
Regarding claim 9, Donohoe meets the claim limitations, as follows:
A device (mobile device) [Donohoe: col. 3, line 4] comprising: a camera system (a camera) [Donohoe: col. 3, line 3] with one or more sensors (image sensors) [Donohoe: col. 3, line 34] and one or more processors (one or more processors) [Donohoe: col. 18, line 60; Fig. 3], the one or more processors (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] being collectively configured to ((In some embodiments, the prediction function is based on characteristics of image sensors having an identical image sensor type as an image sensor in the imaging module) [Donohoe: col. 3, line 33-35]; (The apparatus includes a processor configured to run one or more modules) [Donohoe: col. 4, line 16-17]): obtain image data for one or more images (the image captured under a first lighting spectrum from an imaging module over an interface of the computing system) [Donohoe: col. 3, line 34] from the one or more sensors (image sensors) [Donohoe: col. 3, line 33-35]; determine (the module is further configured to determine) [Donohoe: col. 2, line 40-41], for one or more images, correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] to mitigate ((the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]; (The shading effects refer to a phenomenon in which a brightness of an image is reduced) [Donohoe: col. 1, line 58-59]) spatial nonuniformity and shading effects based on an estimate of scene lighting conditions of the one or more images ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), the estimate of the scene lighting conditions determined using a machine-learning model ((the module is further configured to determine) [Donohoe: col. 2, line 40-41]; (shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum) [Donohoe: col. 10, line 23-25]) trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]); and apply the correction parameters to the one or more images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
Regarding claim 10, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more sensors (image sensors) [Donohoe: col. 3, line 34] of the camera system (a camera) [Donohoe: col. 3, line 3] comprises at least one of: a single red-green-blue (RGB) image sensor (an image sensor with a three-color (RGB) CFA) [Donohoe: col. 9, line 51-52]; multiple RGB image sensors (there are many image sensors of interest and when the image sensors) [Donohoe: col. 9, line 65-66]; one or more RGB image sensors in combination with at least one of an infrared (IR) image sensor or ambient light sensor; or
multiple RGB image sensors in a stereo camera configuration.
Regarding claim 11, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] to determine the estimate of scene lighting conditions using raw image data from the one or more sensors ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), preprocessed image data ((a predetermined input light spectrum) [Donohoe: col. 10, line 32]; (predetermining the spatial pattern of the shading effects) [Donohoe: col. 8, line 9-10]), or image statistics for the one or more images (the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 38-43].
Regarding claim 12, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
the device (mobile device) [Donohoe: col. 3, line 4] further comprises a sensor controller ((The processor 310 can include any applicable processor such as a system-on-a-chip that combines one or more of a central processing unit (CPU)) [Donohoe: col. 11, line 52-55]; (one or more processors) [Donohoe: col. 18, line 60; Fig. 3]) controlling one or more operation characteristics of the one or more sensors (The typical characteristics of image sensors having the same image sensor type can include one or more correction meshes of typical image sensors of the image sensor type with which the particular image sensor is also associated. For example, the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 34-43]; and the one or more processors are further configured to (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] provide the estimate of scene lighting conditions to the sensor controller (the disclosed shading correction mechanism can analyze the captured image to determine an input lighting spectrum associated with the captured image. The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 18-29] to adjust the one or more operation characteristics for the one or more sensors (The typical characteristics of image sensors having the same image sensor type can include one or more correction meshes of typical image sensors of the image sensor type with which the particular image sensor is also associated. For example, the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 34-43].
Regarding claim 16, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured to (The electronic device can be configured with one or more processors that process instructions and run software that may be stored in memory) [Donohoe: col. 18, line 59-62] reuse, adjust (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39], smooth, or stabilize the correction parameters across the one or more images to output the one or more corrected images as a video ((The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]; (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36]).
Regarding claim 18, Donohoe meets the claim limitations, as follows:
A method (a method) [Donohoe: col. 3, line 4] comprising: determining, using a machine-learning model (the module is further configured to determine) [Donohoe: col. 2, line 40-41] trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]), an estimate of scene lighting conditions for one or more first images having spatial nonuniformity and shading effects (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29];
determining (the module is further configured to determine) [Donohoe: col. 2, line 40-41], by minimizing an error (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39] between the estimate of scene lighting conditions and actual scene lighting conditions associated with the one or more first images (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), tuning parameters of the machine-learning model (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]; determining (the module is further configured to determine) [Donohoe: col. 2, line 40-41], using the machine-learning model with the tuning parameters (The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2]), correction parameters based on the estimate of scene lighting conditions of one or more second images (The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra) [Donohoe: col. 12, line 1-5]; and applying the correction parameters to the one or more second images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
Regarding claim 20, Donohoe meets the claim limitations as set forth in claim 18. Donohoe further meets the claim limitations as follow.
wherein the error is minimized (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39] based on at least one of an average (The ST calibration module 316 is configured to generate one or more correction meshes for the image sensor type by averaging characteristics of representative image sensors associated with the image sensor type) [Donohoe: col. 14, line 23-26] or maximum error value per pixel values of the one or more first images, per pixel blocks of the pixel values, or per one or more regions of interest in the one or more first images (In some embodiments, when the image of the uniform surface is a color image, then the PU calibration module 314 can stack four adjacent pixels in a 2x2 grid, e.g., Gr 104, R 106, B 108, Gb 110, of the image Iᴨ,Єᴨ (x,y) to form a single pixel, thereby reducing the size of the image into a quarter, The stacked, four-dimensional image is referred to as a stacked input image Iᴨ,Єᴨ (x,y), where each pixel has four values, The PU calibration module 314 can then perform the above process to compute a four-dimensional correction mesh Ci,ᴨp (w,z), where each dimension is associated with one of the d/channel, R channel, B channel, and Gb channel) [Donohoe: col. 13, line 6-16].
Regarding claim 21, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein each shading profile of the one or more shading profiles includes correction parameters ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]) determined for a respective simulated lighting condition during sensor calibration (The ST calibration module 316 is configured to generate one or more correction meshes for the image sensor type by averaging characteristics of representative image sensors associated with the image sensor type) [Donohoe: col. 14, line 23-26].
Regarding claim 22, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the machine-learning model (((the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]; (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]) is trained to classify scene lighting conditions according to the one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-16, 18, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Donohoe (US Patent US 9,270,872 B2), (“Donohoe”), in view of Cella et al. (US Patent Application Publication US 2023/0176557 A1), (“Cella”).
Regarding claim 1, Donohoe meets the claim limitations, as follows:
An apparatus (an apparatus) [Donohoe: col. 2, line 25; Fig. 3] comprising: one or more processors configured to (one or more processors) [Donohoe: col. 18, line 60; Fig. 3]: determine (the module is further configured to determine) [Donohoe: col. 2, line 40-41], for one or more images, correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] to mitigate (The shading effects refer to a phenomenon in which a brightness of an image is reduced) [Donohoe: col. 1, line 58-59] spatial nonuniformity and shading effects based on an estimate of scene lighting conditions of the one or more images ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which
the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 10, line 61 - col. 2, line 5]), the estimate of the scene lighting conditions determined using a machine-learning model ((the module is further configured to determine) [Donohoe: col. 2, line 40-41]; (shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum) [Donohoe: col. 10, line 23-25]) trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a
predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]); and apply the correction parameters to the one or more images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
the machine-learning model;
However, in the same field of endeavor Cella further explicitly discloses this claim limitation as follows:
the machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 2, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the machine-learning model determines (the module is further configured to determine) [Donohoe: col. 2, line 40-41] the estimate of scene lighting conditions of the one or more images by associating the scene lighting conditions to multiple shading profiles including the one or more shading profiles (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]) and providing a confidence value for each shading profile of the multiple shading profiles (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41].
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
a confidence value
However, in the same field of endeavor Cella further discloses the deficient claim limitations as follows:
a machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
a confidence value ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 3, Donohoe meets the claim limitations as set forth in claim 2. Donohoe further meets the claim limitations as follow.
wherein the correction parameters are determined (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] based on at least one of: a subset of the multiple shading profiles (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]) with highest confidence values; or the multiple shading profiles having confidence values (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]) above a predetermined threshold value.
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
with highest confidence values; or
above a predetermined threshold value.
However, in the same field of endeavor Cella further discloses the deficient claim limitations as follows:
with highest confidence values (setting a maximum voltage, a threshold based on a sensed maximum threshold) [Cella: para: 0483]; or
above a predetermined threshold value (an input signal from exceeding an input protection threshold) [Cella: para: 0475].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 4, Donohoe meets the claim limitations as set forth in claim 2. Donohoe further meets the claim limitations as follow.
wherein at least one of the estimate of scene lighting conditions (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13], confidence values, or the correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] determined by the machine-learning model (the module is further configured to determine) [Donohoe: col. 2, line 40-41] are combined (The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra) [Donohoe: col. 12, line 1-5] with at least one of another estimate of scene lighting conditions (The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra) [Donohoe: col. 12, line 1-5], other confidence values, or other correction parameters (The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2], respectively, determined by a second machine-learning model different from the machine-learning model ((The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2]; (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13].
In the same field of endeavor Cella further discloses the claim limitations as follows:
a second machine-learning model different than the machine-learning model ((training one or more machine-learned models using the sensor data) [Cella: para: 1668]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 5, Donohoe meets the claim limitations as set forth in claim 2. Donohoe further meets the claim limitations as follow.
wherein the correction parameters are determined ((The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]; (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36]), by applying adaptive weights to each correction parameter associated with each shading profile of a subset of the multiple shading profiles (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]), the adaptive weights obtained by normalizing each confidence value with a sum of confidence values for the subset of the multiple shading profiles (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41].
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
applying adaptive weights.
the confidence value
However, in the same field of endeavor Cella further discloses the deficient claim limitations as follows:
applying adaptive weights (a threshold value may be used to adjust a weighting of other factors being processed) [Cella: para: 2064].
the confidence value ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 6, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the correction parameters are applied to the one or more images (a correction mesh for the image) [Donohoe: col. 2, line 35-36] per pixel location or per one or more blocks of pixel values of the one or more images (In some embodiments, when the image of the uniform surface is a color image, then the PU calibration module 314 can stack four adjacent pixels in a 2x2 grid, e.g., Gr 104, R 106, B 108, Gb 110, of the image Iᴨ,Єᴨ (x,y) to form a single pixel, thereby reducing the size of the image into a quarter, The stacked, four-dimensional image is referred to as a stacked input image Iᴨ,Єᴨ (x,y), where each pixel has four values, The PU calibration module 314 can then perform the above process to compute a four-dimensional correction mesh Ci,ᴨp (w,z), where each dimension is associated with one of the d/channel, R channel, B channel, and Gb channel) [Donohoe: col. 13, line 6-16].
Regarding claim 7, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
the one or more processors comprise multiple processors (The electronic device can be configured with one or more processors that process instructions and run software that may be stored in memory) [Donohoe: col. 18, line 59-62]; and a single processor of the multiple processors employs (The processor also communicates with the memory and interfaces to communicate with other devices. The processor can be any applicable processor such as a system-on-a-chip that combines a CPU, an application processor, and flash memory) [Donohoe: col. 18, line 62-66] the machine-learning model to determine the estimate of scene lighting conditions or determine the correction parameters ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]; (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]).
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
the machine-learning model;
However, in the same field of endeavor Cella further explicitly discloses this claim limitation as follows:
the machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 8, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured to (The electronic device can be configured with one or more processors that process instructions and run software that may be stored in memory) [Donohoe: col. 18, line 59-62]: compare image statistics of the one or more images to one or more predetermined thresholds ((In other cases, the correction mesh can have a lower spatial dimensionality compared to the image sensor. Because the shading can vary
slowly across the image, the correction mesh does not need to have the same resolution as the image sensor to fully compensate for the shading effect. Instead, the correction mesh can have a lower spatial dimensionality compared to the
image sensor so that the correction mesh can be stored in a storage medium with a limited capacity, and can be up-sampled prior to being applied to correct the shading effect) [Donohoe: col. 8, line 34-44]; (The vignetting effect refers to a phenomenon in which less light reaches the corners of an image sensor compared to the center of an image sensor. This results in decreasing brightness as one moves away from the center of an image and towards the edges of the image. FIG. 2 illustrates a typical vignetting effect. When a camera is used to capture an image 200 of a uniform white surface, the vignetting effect can render the comers of the image 202 darker than the center of the image 204) [Donohoe: col. 2, line 10-17; Fig. 2]; and in response to the image statistics not satisfying the one or more predetermined thresholds ((When the correction filter C(x,y) is designed to have a lower resolution compared to the image sensor, the correction filter C( x,y) can be computed by inverting a sub-sampled version of the low-pass filtered image) [Donohoe: col. 9, line 15-18]; (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39]), estimate the scene lighting conditions (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29] or determine the correction parameters using the machine-learning model (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]).
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
the machine-learning model;
However, in the same field of endeavor Cella further explicitly discloses this claim limitation as follows:
the machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 9, Donohoe meets the claim limitations, as follows:
A device (mobile device) [Donohoe: col. 3, line 4] comprising: a camera system (a camera) [Donohoe: col. 3, line 3] with one or more sensors (image sensors) [Donohoe: col. 3, line 34] and one or more processors (one or more processors) [Donohoe: col. 18, line 60; Fig. 3], the one or more processors (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] being collectively configured to ((In some embodiments, the prediction function is based on characteristics of image sensors having an identical image sensor type as an image sensor in the imaging module) [Donohoe: col. 3, line 33-35]; (The apparatus includes a processor configured to run one or more modules) [Donohoe: col. 4, line 16-17]): obtain image data for one or more images (the image captured under a first lighting spectrum from an imaging module over an interface of the computing system) [Donohoe: col. 3, line 34] from the one or more sensors (image sensors) [Donohoe: col. 3, line 33-35]; determine (the module is further configured to determine) [Donohoe: col. 2, line 40-41], for one or more images, correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36] to mitigate ((the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]; (The shading effects refer to a phenomenon in which a brightness of an image is reduced) [Donohoe: col. 1, line 58-59]) spatial nonuniformity and shading effects based on an estimate of scene lighting conditions of the one or more images ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), the estimate of the scene lighting conditions determined using a machine-learning model ((the module is further configured to determine) [Donohoe: col. 2, line 40-41]; (shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum) [Donohoe: col. 10, line 23-25]) trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]); and apply the correction parameters to the one or more images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
the machine-learning model;
However, in the same field of endeavor Cella further explicitly discloses this claim limitation as follows:
the machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 10, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more sensors (image sensors) [Donohoe: col. 3, line 34] of the camera system (a camera) [Donohoe: col. 3, line 3] comprises at least one of: a single red-green-blue (RGB) image sensor (an image sensor with a three-color (RGB) CFA) [Donohoe: col. 9, line 51-52]; multiple RGB image sensors (there are many image sensors of interest and when the image sensors) [Donohoe: col. 9, line 65-66]; one or more RGB image sensors in combination with at least one of an infrared (IR) image sensor or ambient light sensor; or
multiple RGB image sensors in a stereo camera configuration.
Regarding claim 11, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] to determine the estimate of scene lighting conditions using raw image data from the one or more sensors ((The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), preprocessed image data ((a predetermined input light spectrum) [Donohoe: col. 10, line 32]; (predetermining the spatial pattern of the shading effects) [Donohoe: col. 8, line 9-10]), or image statistics for the one or more images (the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 38-43].
Regarding claim 12, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
the device (mobile device) [Donohoe: col. 3, line 4] further comprises a sensor controller ((The processor 310 can include any applicable processor such as a system-on-a-chip that combines one or more of a central processing unit (CPU)) [Donohoe: col. 11, line 52-55]; (one or more processors) [Donohoe: col. 18, line 60; Fig. 3]) controlling one or more operation characteristics of the one or more sensors (The typical characteristics of image sensors having the same image sensor type can include one or more correction meshes of typical image sensors of the image sensor type with which the particular image sensor is also associated. For example, the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 34-43]; and the one or more processors are further configured to (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] provide the estimate of scene lighting conditions to the sensor controller (the disclosed shading correction mechanism can analyze the captured image to determine an input lighting spectrum associated with the captured image. The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 18-29] to adjust the one or more operation characteristics for the one or more sensors (The typical characteristics of image sensors having the same image sensor type can include one or more correction meshes of typical image sensors of the image sensor type with which the particular image sensor is also associated. For example, the typical characteristics of image sensors having the same image sensor type can include one or more correction meshes associated with an "average" image sensor of the image sensor type for a predetermined set of input light spectra, which may or may not include the determined input lighting spectrum for the captured image) [Donohoe: col. 10, line 34-43].
Regarding claim 13, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more processors (one or more processors) [Donohoe: col. 18, line 60; Fig. 3] use a machine-learning model to determine at least one of: the estimate of scene lighting conditions (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which
the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; confidence values in associating the estimate of scene lighting conditions to each shading profile of multiple candidate shading profiles including the one or more shading profiles (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]; an array of confidence values in associating the estimate of scene lighting conditions in multiple different locations of the one or more images; or the correction parameters (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36].
Donohoe does not explicitly disclose the following claim limitations (Emphasis added).
confidence values;the machine-learning model;
However, in the same field of endeavor Cella further discloses the deficient claim limitations as follows:
confidence values ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
the machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 14, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein outputs from the machine-learning model are combined with second outputs (The interface 326 can provide an input and/or output mechanism to communicate with other network devices. The interface can be implemented in hardware to send and receive signals in a variety of mediums, such as optical, copper, and wireless, and in a number of different protocols, some of which may be non-transient) [Donohoe: col. 11, line 58-63] determined by a second machine-learning model different than the machine-learning model ((The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2]; (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13].
In the same field of endeavor Cella further discloses the claim limitations as follows:
a second machine-learning model different than the machine-learning model ((training one or more machine-learned models using the sensor data) [Cella: para: 1668]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 15, Donohoe meets the claim limitations as set forth in claim 14. Donohoe further meets the claim limitations as follow.
the machine-learning model and the second machine-learning model (The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2] use different algorithmic approaches (in a number of different protocols) [Donohoe: col. 11, line 58-63] including a support vector machine (support
vector machine techniques) [Donohoe: col. 16, line 21-21], a convolutional neural network, a recurrent neural network, a graph neural network, or a multilayer perceptron neural network (The one or more modules can include any other suitable module or combination of modules) [Donohoe: col. 12, line 11-13].
In the same field of endeavor Cella further discloses the claim limitations as follows:
the machine-learning model and the second machine-learning model ((training one or more machine-learned models using the sensor data) [Cella: para: 1668]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 16, Donohoe meets the claim limitations as set forth in claim 9. Donohoe further meets the claim limitations as follow.
wherein the one or more processors are further configured to (The electronic device can be configured with one or more processors that process instructions and run software that may be stored in memory) [Donohoe: col. 18, line 59-62] reuse, adjust (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39], smooth, or stabilize the correction parameters across the one or more images to output the one or more corrected images as a video ((The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]; (determine a correction mesh for the image) [Donohoe: col. 2, line 35-36]).
Regarding claim 18, Donohoe meets the claim limitations, as follows:
A method (a method) [Donohoe: col. 3, line 4] comprising: determining, using a machine-learning model (the module is further configured to determine) [Donohoe: col. 2, line 40-41] trained based on one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]), an estimate of scene lighting conditions for one or more first images having spatial nonuniformity and shading effects (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29];
determining (the module is further configured to determine) [Donohoe: col. 2, line 40-41], by minimizing an error (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39] between the estimate of scene lighting conditions and actual scene lighting conditions associated with the one or more first images (One of the prominent spatially varying shading effects is referred to as the color non-uniformity effect. The color non-uniformity effect refers to a phenomenon in which a color of a captured image varies spatially, even when the physical properties of the light (e.g., the amount of light and/or the wavelength of the captured light) captured by the image sensor is uniform across spatial locations in the image sensor. A typical symptom of a color non-uniformity effect can include a green tint at the center of an image, which fades into a magenta tint towards the edges of an image. This particular symptom has been referred to as the "green spot" issue.) [Donohoe: col. 1, line 61 - col. 2, line 5]), tuning parameters of the machine-learning model (The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra. The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include the correction module 320 that is configured to apply the predicted correction mesh to remove the shading effect in images captured by the image sensor 306. The one or more modules can include any other suitable module or combination of modules.) [Donohoe: col. 12, line 1-13]; determining (the module is further configured to determine) [Donohoe: col. 2, line 40-41], using the machine-learning model with the tuning parameters (The one or more modules can also include a sensor type calibration module 316) [Donohoe: col. 12, line 1-2]), correction parameters based on the estimate of scene lighting conditions of one or more second images (The one or more modules can also include a prediction function estimation module 318 configured to estimate the prediction function for the image sensor 306. The one or more modules can also include a sensor type calibration module 316 configured to determine one or more correction meshes for typical image sensors of the same type as the particular image sensor 306 for a predetermined set of lighting spectra) [Donohoe: col. 12, line 1-5]; and applying the correction parameters to the one or more second images to generate one or more corrected images (receive a per-unit correction mesh for adjusting images captured by the imaging module under a second lighting spectrum; determine a correction mesh for the image captured under the first lighting spectrum based on the per-unit correction mesh for the second lighting spectrum; and operate the correction mesh on the image to remove the shading effect from the image.) [Donohoe: col. 2, line 33-39].
In the same field of endeavor Cella further explicitly discloses the claim limitations as follows:
a machine-learning model ((The machine-learned model may output a prediction or classification and a degree of confidence) [Cella: para: 1710]; (The AI module 29124 may then generate a feature vector that includes one or more instances of sensor data and may feed the feature vector into the selected model. In response to the feature vector, the selected model may output a prediction or classification, and a degree of confidence (e.g., a confidence score)) [Cella: para: 1729]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Donohoe with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Donohoe with Cella will enable the system to tum on or
off one or more sensors in a multi-sensor data collector in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), and achieve the highest value utilization of a data collector. In addition, these techniques can be used to handle optimization of transport of data in the platform by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring
network security elements), and the like [Cella: para: 0239].
Regarding claim 20, Donohoe meets the claim limitations as set forth in claim 18. Donohoe further meets the claim limitations as follow.
wherein the error is minimized (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39] based on at least one of an average (The ST calibration module 316 is configured to generate one or more correction meshes for the image sensor type by averaging characteristics of representative image sensors associated with the image sensor type) [Donohoe: col. 14, line 23-26] or maximum error value per pixel values of the one or more first images, per pixel blocks of the pixel values, or per one or more regions of interest in the one or more first images (In some embodiments, when the image of the uniform surface is a color image, then the PU calibration module 314 can stack four adjacent pixels in a 2x2 grid, e.g., Gr 104, R 106, B 108, Gb 110, of the image Iᴨ,Єᴨ (x,y) to form a single pixel, thereby reducing the size of the image into a quarter, The stacked, four-dimensional image is referred to as a stacked input image Iᴨ,Єᴨ (x,y), where each pixel has four values, The PU calibration module 314 can then perform the above process to compute a four-dimensional correction mesh Ci,ᴨp (w,z), where each dimension is associated with one of the d/channel, R channel, B channel, and Gb channel) [Donohoe: col. 13, line 6-16].
Regarding claim 21, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein each shading profile of the one or more shading profiles includes correction parameters ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]) determined for a respective simulated lighting condition during sensor calibration (The ST calibration module 316 is configured to generate one or more correction meshes for the image sensor type by averaging characteristics of representative image sensors associated with the image sensor type) [Donohoe: col. 14, line 23-26].
Regarding claim 22, Donohoe meets the claim limitations as set forth in claim 1. Donohoe further meets the claim limitations as follow.
wherein the machine-learning model (((the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]; (the one or more modules is configured to minimize, in part, a sum of squared differences between values of the first correction mesh associated with the first lighting spectrum and values of the plurality of correction meshes associated with the second lighting spectrum) [Donohoe: col. 4, line 35-39]) is trained to classify scene lighting conditions according to the one or more shading profiles ((The disclosed shading correction mechanism can operate in two stages: a training stage and a run-time stage. In the training stage, which may be performed during the device production and/or at a laboratory, the disclosed shading correction mechanism can determine a computational function
that is capable of generating a correction mesh for an image sensor of interest. In some cases, the computational function can be determined based on characteristics of image sensors that are similar to the image sensor of interest) [Donohoe: col. 12, line 19-27]; (The processor 310 can be configured to run one or more modules. The one or more modules can include the per-unit calibration module 314 configured to determine a correction mesh for the image sensor 306 for a specific lighting profile) [Donohoe: col. 11, line 64-67]; (This shading correction scheme is useful and efficient because the shading correction scheme can determine the correction mesh Ci,ᴨp of the particular sensor i only once for a predetermined spedrum ᴨP, and adapt it to be applicable to a wide range of lighting profiles ᴨD using the prediction function fᴨD. This scheme can reduce the number of correction meshes to be determined for a particular imaging module 302, thereby improving the efficiency of the shading correction.) [Donohoe: col. 10, line 66 – col. 11, line 4]; (The input lighting spectrum can refer to a lighting profile of a light source used to shine the scene captured by the image. Subsequently, the disclosed shading correction mechanism can estimate an appropriate correction mesh for the determined input lighting spectrum based on (1) known characteristics about the particular image sensor with which the image was captured and (2) typical characteristics of image sensors having the same image sensor type as the particular image sensor) [Donohoe: col. 10, line 21-29]; (the ST calibration module 316 is configured to receive images Iᴨ,Єᴨ (x,y) of a uniform, monochromatic surface taken by a set of image sensors that are representative of an image sensor associated with an image sensor type, These images Iᴨ,Єᴨ (x,y) are taken under one ᴨc of a predetermined set of lighting profiles ᴨ. The predetermined set of lighting profiles ᴨ can include one or more lighting profiles that often occur in realworld settings, For example, the predetermined set of lighting profiles ᴨ can include "Incandescent 2700K," "Fluorescent 2700K," "Fluorescent 4000K," "Fluorescent 6500K," "Outdoor midday sun ( 6500K)," or any other profiles of interest) [Donohoe: col. 2, line 40-41]).
Reference Notice
Additional prior arts, included in the Notice of Reference Cited, made of record and not relied upon is considered pertinent to applicant's disclosure.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip Dang whose telephone number is (408) 918-7529. The examiner can normally be reached on Monday-Thursday between 8:30 am - 5:00 pm (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached on 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Philip P. Dang/Primary Examiner, Art Unit 2488
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