Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 04/28/2026 have been fully considered but they are not persuasive.
Regarding Claims 1, 8, & 15 as argued on pg. 5-9:
From pg. 5 through the first half of pg. 7, the Applicant is reciting claim language and the prior art rejections used in the previous Office Action. No response is necessary to this portion of the Applicant Arguments.
On pg. 7, section 1, first paragraph, Applicant asserts that “displayed image” is not equivalent to “camera rays.” Applicant argues that, “’camera rays’ are commonly understood in the field of computer graphics and computer vision to refer to mathematically generated rays for each pixel to create an image.” The Examiner has a duty to interpret claim language using the broadest reasonable interpretation, not the commonly understood terminology of two specific fields. Furthermore, this invention does not fall strictly within the fields of computer graphics and computer vision, but rather the field of digital cameras, which is a much broader scope to begin with. Incident rays of light become “camera rays” upon entering a camera through the lens, hitting the image sensor, and in turn being used to generate an image. Examiner deems this interpretation to satisfactorily fulfill the requirements of “based on camera rays.” However, Examiner respectfully would like to also direct Applicant’s attention to prior art provided in the previous Office Action. See Oh et al (US 20240212115 A1, hereinafter, "Oh"), Abstract, ln. 4-5, which read, “generating an HDR image by ray marching on a basis of the 3D HDR radiance fields.” This also teaches “first intensities of the scene based on camera rays for an image of the scene” as recited in Claim 1. For sake of simplicity, Examiner elected not to change to this citation in the rejection below due to the fact that virtually all digital camera functions are “based on camera rays” by the definition of “camera rays” provided by the Examiner above.
In the first two lines of pg. 8 from the paragraph beginning on the previous page, Applicant asserts that, “…Xue teaches brightness adjustment, not ‘determining first intensities.’” In the broadest reasonable interpretation in the field of digital cameras, “intensities” can be considered synonymous with “brightness.” It is inherent that, in order to adjust brightness, one must first determine it. Therefore, adjusting brightness includes determining first brightness (or intensity).
In the first full paragraph on pg. 8, Applicant quotes a rejection citation from Park & Kwak (KR 20200044182 A, hereinafter, "Park"), which reads, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images." Applicant then goes on to assert that Park is silent regarding “using one or more second neural networks each comprising a camera response function.” (emphasis by Applicant). Disregarding the fact that the broadest reasonable interpretation of the phrase “camera response function” can be any function performed by the camera in response to an input, using a stricter interpretation of the phrase “camera response function,” it is commonly known in the field of digital cameras that this includes – but is not limited to – white balance, which is specifically mentioned in the examples listed in the prior art citation. As such, Examiner respectfully disagrees with Applicant’s assertion that Park is silent on camera response functions. Furthermore, Examiner asserts that Park explicitly teaches a camera response function.
On pg. 8, section 2, Applicant asserts that there is no motivation to combine the references. In response, Examiner has modified the explanations of motivations accordingly as noted in the rejection below. Examiner would like to respectfully call to Applicant’s attention the first sentence of MPEP 1207.03(a)(II), which reads, “There is no new ground of rejection when the basic thrust of the rejection remains the same such that an appellant has been given a fair opportunity to react to the rejection.” Applicant has been given first and second non-final rejections for these claims exactly as worded without making claim language amendments of any kind. Based on this, Examiner is making this rejection final, as noted in the Conclusion below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 7, 8, 11, 14, 15, 18, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et al (US 20230308769 A1, hereinafter, "Xue") in view of Park, Oh, and Azarian et al (CN 114467098 A, hereinafter, "Azarian").
Regarding Claim 1, Xue teaches an apparatus for light estimation of a scene, the apparatus comprising: at least one memory (Xue, Fig. 7A, [0081], ln. 5, "….stored in a memory of device 705."); and at least one processor coupled to the at least one memory and configured to: determine, using a first neural network, first intensities of the scene based on camera rays for an image of the scene (Xue, Fig. 3, [0056], ln. 3-5, "First HDRnet 310 is applied to displayed image 305 to generate a first bilateral grid 325 for the non-target region that includes at least a portion of [e.g., an entirety of] the image outside the target region." In the broadest reasonable interpretation, the "displayed image 305" Xue refers to is "based on camera rays," and the "first bilateral grid 325 for the non-target region" can be considered "first intensities."). Xue does not teach determine, using one or more second neural networks each comprising a camera response function. However, Park teaches determine, using one or more second neural networks each comprising a camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images." Examiner's note: White balance is considered a camera response function.). It would have been obvious at the time of the invention to persons having ordinary skill in the art to combine the teachings of Park with those of Xue because it is widely known in the art that a neural network can be used to determine a camera response function such as white balance (Park, pg. 14, para. 1, ln. 1-2, "…a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101…"), and that it can be done with either a single neural network or a plurality of neural networks (Park, pg. 14, para. 1, ln. 4-7, "The third artificial intelligence model may be separate models separate from the first artificial intelligence model and the second artificial intelligence model. In addition, the third artificial intelligence model may constitute one model with the first artificial intelligence model and the second artificial intelligence model."). Xue and Park do not teach second intensities of the scene based on the first intensities of the scene. However, Oh teaches second intensities of the scene based on the first intensities of the scene (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function." Examiner's note: "Second intensities" would be the tone-mapped LDR image, and "first intensities" would be the HDR image.). It would have been obvious at the time of the invention to combine the teachings of Oh with the teachings of Xue and Park because it is widely known in the art to determine second intensities of an LDR image based on first intensities of an HDR image to calculate a loss value based on the LDR images and modify the parameters of the HDR radiance fields so as to maximally minimize the loss value (Oh, Abstract, ln. 5-8, "generating a tone-mapped LDR image from the HDR image through a tone mapping module; calculating a loss value on a basis of the LDR images captured at the various viewpoints and the tone-mapped LDR image; and modifying parameters of the 3D HDR radiance fields and parameters of the tone-mapping module, so as to maximally minimize the loss value."). Xue and Oh fail to teach wherein the one or more second neural networks are trained to learn the camera response function. However, Park teaches wherein the one or more second neural networks are trained to learn the camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images."). Xue, Oh, and Park fail to teach based on a regularization loss. However, Azarian teaches based on a regularization loss (Azarian, Fig. 5, pg. 12, para. 1, ln. 2-4, "As shown in FIG. 5, the frame 502, process 500 based on the classification loss and regularization loss function to determine a trimming threshold for trimming a first set of pre-training weights in a plurality of pre-training weights."). It is widely known in the art that convolutional neural networks - such as those used in this invention - require pre-training. It would have been obvious at the time of the invention to combine the teachings of Azarian with those of Xue, Oh, and Park because trimming pre-training weights would save memory and reduce power consumption (Azarian, Background, ln. 3-4, "Some conventional neural network are pruned and quantized to reduce the use of processor and memory.").
Regarding Claim 4, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 1 as noted above. Oh teaches the first intensities have a higher dynamic range than the second intensities (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function." Secondary LDR [Low Dynamic Range] intensities are generated from a first HDR [High Dynamic Range] intensity.). It would have been obvious at the time of the invention to combine the teachings of Oh with the teachings of Xue and Park because it is widely known in the art to determine second intensities of an LDR (low dynamic range) image based on first intensities of an HDR (high dynamic range) image to calculate a loss value based on the LDR images and modify the parameters of the HDR radiance fields so as to maximally minimize the loss value (Oh, Abstract, ln. 5-8, "generating a tone-mapped LDR image from the HDR image through a tone mapping module; calculating a loss value on a basis of the LDR images captured at the various viewpoints and the tone-mapped LDR image; and modifying parameters of the 3D HDR radiance fields and parameters of the tone-mapping module, so as to maximally minimize the loss value.").
Regarding Claim 7, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 1 as noted above. Oh teaches the first neural network is trained to learn a radiance field model based on a plurality of multi-view images with the second intensities (Oh, [0062]-[0063], all lines, "In step 220, the image processing device may construct 3D HDR radiance fields from the LDR images captured from the various viewpoints. [0063] The 3D HDR radiance fields may include information required to calculate how an object will appear on a screen when the object is viewed from a particular location. The 3D HDR radiance fields may be a concept introduced in a known paper [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]."). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Oh with those of Xue, Park, Oh, and Azarian because it is widely known in the art to teach a neural network a radiance field model based on LDR images (second intensities) in order to construct an HDR radiance field. (Oh, [0062], all lines, “"In step 220, the image processing device may construct 3D HDR radiance fields from the LDR images captured from the various viewpoints.”).
Regarding Claim 8, Xue, Park, Oh, and Azarian teach a method of light estimation of a scene, the method comprising: determining, using a first neural network, first intensities of the scene based on camera rays for an image of the scene (Xue, Fig. 3, [0056], ln. 3-5, "First HDRnet 310 is applied to displayed image 305 to generate a first bilateral grid 325 for the non-target region that includes at least a portion of (e.g., an entirety of) the image outside the target region." In the broadest reasonable interpretation, the "displayed image 305" Xue refers to is "based on camera rays," and the "first bilateral grid 325 for the non-target region" can be considered "first intensities."); and determining, using one or more second neural networks each comprising a camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images."), second intensities of the scene based on the first intensities of the scene (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function."), wherein the one or more second neural networks are trained to learn the camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images.") based on a regularization loss (Azarian, Fig. 5, pg. 12, para. 1, ln. 2-4, "As shown in FIG. 5, the frame 502, process 500 based on the classification loss and regularization loss function to determine a trimming threshold for trimming a first set of pre-training weights in a plurality of pre-training weights."). The motivations to combine are identical to those provided in Claim 1.
Regarding Claim 11, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 8 as noted above. Oh teaches the first intensities have a higher dynamic range than the second intensities (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function." Secondary LDR [Low Dynamic Range] intensities are generated from a first HDR [High Dynamic Range] intensity.). Motivations to combine are identical to those provided in Claim 4.
Regarding Claim 14, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 8 as noted above. Oh teaches the first neural network is trained to learn a radiance field model based on a plurality of multi-view images with the second intensities (Oh, [0062]-[0063], all lines, "In step 220, the image processing device may construct 3D HDR radiance fields from the LDR images captured from the various viewpoints. [0063] The 3D HDR radiance fields may include information required to calculate how an object will appear on a screen when the object is viewed from a particular location. The 3D HDR radiance fields may be a concept introduced in a known paper [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]."). Motivations to combine are identical to those provided in Claim 7.
Regarding Claim 15, , Xue, Park, Oh, and Azarian teach A non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to: determine, using a first neural network, first intensities of a scene based on camera rays for an image of the scene (Xue, Fig. 3, [0056], ln. 3-5, "First HDRnet 310 is applied to displayed image 305 to generate a first bilateral grid 325 for the non-target region that includes at least a portion of (e.g., an entirety of) the image outside the target region." In the broadest reasonable interpretation, the "displayed image 305" Xue refers to is "based on camera rays," and the "first bilateral grid 325 for the non-target region" can be considered "first intensities."); and determine, using one or more second neural networks each comprising a camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images."), second intensities of the scene based on the first intensities of the scene (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function."), wherein the one or more second neural networks are trained to learn the camera response function (Park, pg. 14, para. 1, ln. 1-3, "…the third artificial intelligence model may be a neural network model that learns shooting setting information [eg, exposure, white balance, and focus] of the camera 101 and RGB images.") based on a regularization loss (Azarian, Fig. 5, pg. 12, para. 1, ln. 2-4, "As shown in FIG. 5, the frame 502, process 500 based on the classification loss and regularization loss function to determine a trimming threshold for trimming a first set of pre-training weights in a plurality of pre-training weights."). Motivations to combine are identical to those in Claim 1.
Regarding Claim 18, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 15 as noted above. Oh teaches the first intensities have a higher dynamic range than the second intensities (Oh, Fig. 5, [0084], ln. 3-5, "…the tone mapping module may be a module for generating a tone-mapped LDR image by adjusting the white balance of the HDR image and then applying the camera response function." Secondary LDR [Low Dynamic Range] intensities are generated from a first HDR [High Dynamic Range] intensity.). Motivations to combine are identical to those in Claim 4.
Regarding Claim 20, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 15 as noted above. Oh teaches the first neural network is trained to learn a radiance field model based on a plurality of multi-view images with the second intensities (Oh, [0062]-[0063], all lines, "In step 220, the image processing device may construct 3D HDR radiance fields from the LDR images captured from the various viewpoints. [0063] The 3D HDR radiance fields may include information required to calculate how an object will appear on a screen when the object is viewed from a particular location. The 3D HDR radiance fields may be a concept introduced in a known paper [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis]."). Motivations to combine are identical to those provided in Claim 7.
Claims 2, 9, & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xue in view of Park, Oh, Azarian, and Tao & Kim (US 20160381335 A1, hereinafter, "Tao").
Regarding Claim 2, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 1 as noted above. Xue teaches based on training the one or more second neural networks using the regularization loss (Xue, Figs. 2 & 3, [0054], ln. 5-7, "For example, a modified version of a trained neural network [e.g., a convolutional neural network or ‘CNN’], such as Google's HDRnet tone mapping algorithm, may be utilized." Fig. 3 shows second neural network used. It is widely known that HDRnet is a neural network which uses regularization loss.). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Xue with those of Xue, Park, Oh, and Azarian because it is widely known in the art to use regularization loss to train neural networks in order to stabilize the training process (Oh, [0101], ln. 2-5, “The process of applying the masking may be seen as a process of performing regularization of the spherical harmonic coefficients. By applying the masking, a speed at which the spherical harmonic coefficients are modified in the training process may be adjusted. In this way, the training process may be stabilized.”). Xue does not teach the camera response function is monotonically increasing. However, Tao teaches the camera response function is monotonically increasing (Tao, Figs. 5A-5C, [0050], ln. 1-3, "FIGS. 5A, 5B, and 5C illustrate example GCI-C and LCI-C tone mapping functions according to various embodiments of the present disclosure. In the various embodiments, the functions ƒ and g are monotonically increasing functions."). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Tao with those of Xue, Park, Oh, and Azarian because it is widely known in the art to monotonically increase a camera response function to process a transition range between focal distances (Tao, [0056], ln. 9-11, “The value of Z 630 monotonically dilates from Y to X as the pixels are processed in the transition range 740 using the transition weight factor 615.”).
Regarding Claim 9, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 8 as noted above. Xue and Tao teach based on training the one or more second neural networks using the regularization loss (Xue, Figs. 2 & 3, [0054], ln. 5-7, "For example, a modified version of a trained neural network [e.g., a convolutional neural network or “CNN”], such as Google's HDRnet tone mapping algorithm, may be utilized." Fig. 3 shows second neural network used.), the camera response function is monotonically increasing (Tao, Figs. 5A-5C, [0050], ln. 1-3, "FIGS. 5A, 5B, and 5C illustrate example GCI-C and LCI-C tone mapping functions according to various embodiments of the present disclosure. In the various embodiments, the functions ƒ and g are monotonically increasing functions."). Motivations for combination are identical to those in Claim 2.
Regarding Claim 16, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 15 as noted above. Xue and Tao teach based on training the one or more second neural networks using the regularization loss (Xue, Figs. 2 & 3, [0054], ln. 5-7, "For example, a modified version of a trained neural network [e.g., a convolutional neural network or “CNN”], such as Google's HDRnet tone mapping algorithm, may be utilized." Fig. 3 shows second neural network used.), the camera response function is monotonically increasing (Tao, Figs. 5A-5C, [0050], ln. 1-3, "FIGS. 5A, 5B, and 5C illustrate example GCI-C and LCI-C tone mapping functions according to various embodiments of the present disclosure. In the various embodiments, the functions ƒ and g are monotonically increasing functions."). Motivations for combination are identical to those in Claim 2.
Claims 3, 10, & 17 are rejected under 35 U.S.C. 103 as being unpatentable over Xue in view of Park, Oh, Azarian, and Nakata et al (US 20230140768 A1, hereinafter, "Nakata").
Regarding Claim 3, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 1 as noted above. Nakata teaches a camera ray comprises a ray origin and a ray direction (Nakata, Fig. 2, [0109], ln. 1-2, "The light receiving unit 100 includes, for example, a lens 110, an infrared ray cut filter (IRCF 112), and an imaging element 114." Imaging element 114 is the origin of the camera ray, which extends in the direction of lens 110.). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Nakata with those of Xue, Park, Oh, and Azarian because it is widely known in the art that all camera rays comprise a ray origin and a ray direction (Nakata, Fig. 2, 110 & 114).
Regarding Claim 10, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 8 as noted above. Nakata teaches a camera ray comprises a ray origin and a ray direction (Nakata, Fig. 2, [0109], ln. 1-2, "The light receiving unit 100 includes, for example, a lens 110, an infrared ray cut filter (IRCF 112), and an imaging element 114." Imaging element 114 is the origin of the camera ray, which extends in the direction of lens 110.). Motivations to combine are identical to Claim 3.
Regarding Claim 17, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 15 as noted above. Nakata teaches a camera ray comprises a ray origin and a ray direction (Nakata, Fig. 2, [0109], ln. 1-2, "The light receiving unit 100 includes, for example, a lens 110, an infrared ray cut filter (IRCF 112), and an imaging element 114." Imaging element 114 is the origin of the camera ray, which extends in the direction of lens 110.). Motivations to combine are identical to Claim 3.
Claims 5, 6, 12, 13, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Xue in view of Park, Oh, Azarian, and Koga et al (US 20210235005 A1, hereinafter, "Koga").
Regarding Claim 5, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 1 as noted above. Koga teaches the at least one processor is configured to determine the second intensities using the one or more second neural networks further based on an exposure of the image (Koga, Fig. 5, [0065], ln. 3-8, "The first operation procedure example is an example in which a subject to be captured by the monitoring camera 1 is a person and a camera parameter is adjusted [changed] so that a face of the person can be detected by the AI processing. Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted [changed] in FIG. 5, it is needless to say that the camera parameter is not limited thereto."). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Koga with those of Xue, Park, Oh, and Azarian because it is widely known in the art to configure a processor to utilize a neural network to determine second intensities based on image exposure to calculate a loss value based on the LDR images and modify the parameters of the HDR radiance fields so as to maximally minimize the loss value (Oh, Abstract, ln. 5-8, "generating a tone-mapped LDR image from the HDR image through a tone mapping module; calculating a loss value on a basis of the LDR images captured at the various viewpoints and the tone-mapped LDR image; and modifying parameters of the 3D HDR radiance fields and parameters of the tone-mapping module, so as to maximally minimize the loss value.").
Regarding Claim 6, Xue, Park, Oh, Azarian, and Koga teach the limitations of dependent Claim 5 as noted above. Koga teaches the exposure is a combination of an exposure time and a gain of a camera used to capture the image (Koga, Fig. 5, [0065], ln. 3-8, "The first operation procedure example is an example in which a subject to be captured by the monitoring camera 1 is a person and a camera parameter is adjusted [changed] so that a face of the person can be detected by the AI processing. Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted [changed] in FIG. 5, it is needless to say that the camera parameter is not limited thereto."). It would have been obvious to a person having ordinary skill in the art at the time of the invention to combine the teachings of Koga with those of Xue, Park, Oh, Azarian, and Koga because it is widely known in the art that exposure is a combination of time and gain of a camera among other things (Koga, Fig. 5, [0065], ln. 6-8, “Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted (changed) in FIG. 5, it is needless to say that the camera parameter is not limited thereto.”).
Regarding Claim 12, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 8 as noted above. Koga teaches the second intensities are determined using the one or more second neural networks further based on an exposure of the image (Koga, Fig. 5, [0065], ln. 3-8, "The first operation procedure example is an example in which a subject to be captured by the monitoring camera 1 is a person and a camera parameter is adjusted [changed] so that a face of the person can be detected by the AI processing. Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted [changed] in FIG. 5, it is needless to say that the camera parameter is not limited thereto."). Motivations to combine are identical to Claim 5.
Regarding Claim 13, Xue, Park, Oh, Azarian, and Koga teach the limitations of dependent Claim 12 as noted above. Koga teaches the exposure is a combination of an exposure time and a gain of a camera used to capture the image (Koga, Fig. 5, [0065], ln. 3-8, "The first operation procedure example is an example in which a subject to be captured by the monitoring camera 1 is a person and a camera parameter is adjusted [changed] so that a face of the person can be detected by the AI processing. Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted [changed] in FIG. 5, it is needless to say that the camera parameter is not limited thereto."). Motivations to combine are identical to Claim 6.
Regarding Claim 19, Xue, Park, Oh, and Azarian teach the limitations of dependent Claim 15 as noted above. Koga teaches the instructions, when executed by the at least one processor, cause the at least one processor to determine the second intensities using the one or more second neural networks further based on an exposure of the image (Koga, Fig. 5, [0065], ln. 3-8, "The first operation procedure example is an example in which a subject to be captured by the monitoring camera 1 is a person and a camera parameter is adjusted [changed] so that a face of the person can be detected by the AI processing. Although exposure time and a gain of the image sensor 12 and a tone curve are exemplified as camera parameters to be adjusted [changed] in FIG. 5, it is needless to say that the camera parameter is not limited thereto."). Motivations to combine are identical to Claim 5.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN DANIEL BARRY whose telephone number is (571)270-0432. The examiner can normally be reached M-Th 0730-1630.
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/STEVEN DANIEL BARRY/Examiner, Art Unit 2638
/LIN YE/Supervisory Patent Examiner, Art Unit 2638
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