DETAILED ACTION
Election/Restrictions
Claims 2, 7-10,12-13, 17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected groups, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 9/23/2025. Claims 19-20 have been rejoined.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Maeda (US PG Publication 2021/016600) in view of Verma (US PG Publication 2021/0195081) and Hong (US PG Publication 2005/00065655).
Regarding Claim 1, Maeda (US PG Publication 2021/016600) discloses a surveillance camera (monitoring device 110 is a monitoring camera with spot lighting, Fig. 1, 2A-C, [0026]-[0027]) comprising:
a camera (monitoring device 110 is a monitoring camera, has an image capturing unit 111, with spot lighting, Fig. 1, 2A-C, [0026]-[0027]) comprising a plurality of [] light emitting diodes (includes a plurality of sets of LEDs [0027]) corresponding to a plurality of illumination areas (captured image 300 of camera 111 is divided into blocks corresponding to the number of LEDs [0047], Fig. 3B);
and at least one processor (CPU 401 is an arithmetic device that executes various programs installed in the ROM 402 [0053]) configured to:
partition an image acquired through the camera (image capturing unit 111 captures an image of a subject [0028]; captured image 300 of camera 111 [0047]) into a plurality of blocks (captured image 300 of camera 111 is divided into blocks corresponding to the number of LEDs [0047], Fig. 3B; the image quality evaluation unit 505 divides the acquired captured image into a plurality of blocks [0065]),
determine an object block (e.g., blocks 2, 5, 8, 11 among all the blocks in the image frame, Fig. 6B, [0073]-[0078]) comprising at least one block that includes an object (Fig. 6B block 2 has object, block 5 has object, or blocks 8 and 11 have object, [0073]-[0078]),
calculate a brightness (calculates level of image quality [0065] and calculates the light emission intensity necessary for detecting the face area of the subject [0032]) of the object block (evaluates each block [0065]) as [] brightness of the object block (evaluates each block [0065]),
compare the brightness of the object block (evaluates whether or not each block has a level of image quality where a face area may be detected [0065]) …,
and in response to the brightness (calculates level of image quality [0065]) of the object block (evaluates each block [0065]) …, control a brightness of at least one target [] LED (adjustment unit 506 adjusts the light emission intensity of the LED that irradiates the image capturing range corresponding to the block according to the evaluation unit 505 [0066]), among the plurality of [] LEDs (LEDs 112, Fig. 2B, 3B), corresponding to an illumination area that includes the object block (obtain a captured image with the face area irradiated with spot lighting [0070]; the face detection unit 502 determines that the block numbers of the blocks including the face [0141]) such that the brightness of the object block (according to the evaluation unit 505 [0066]) reaches a predetermined brightness (obtain a captured image that has a level of image quality where a face area may be detected [0066]; appropriate brightness [0023]; constant brightness [0024]);
wherein the predetermined reference brightness (appropriate brightness [0023]; constant brightness [0024]) refers to a minimum intensity of light (image to be captured with appropriate brightness, constant brightness [0023]-[0024]) required for object recognition (level of image quality where a face area may be detected [0065]) in a low-light environment (some objects appear dark [0023]).
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches IR light (infrared range [0020] light source [0046]).
Maeda does not disclose, but Hong (US PG Publication 2005/00065655) teaches calculate a brightness as an average brightness (calculate the average intensity [0031]),
compare the brightness … with a predetermined reference brightness (compares the calculated average intensity to a predetermined reference value [0031]), and
in response to the brightness being lower than the predetermined reference brightness (When determined that light is in a dark state as a result of determination of the image intensity comparator 220… When determined that light is in a bright state as a result of determination of the image intensity comparator 220, [0042]), control a brightness (the light controller 270 turns on a light device increases an amount of light output from the light device… the light controller 270 turns off the light device or reduces the amount of light output from the light device [0042]) ….
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
One of ordinary skill in the art before the application was filed would have been motivated to control the LEDs of Maeda based on the intensity of the image of Maeda, as suggested by Hong, because Maeda adjust LEDs to capture images with constant brightness [0095], which requires knowing the actual brightness of the captured image.
Regarding Claim 3, Maeda (US PG Publication 2021/016600) discloses the surveillance camera of claim 1, wherein the at least one processor is further configured to partition the image into M x N (rectangular [0046]) blocks (the image quality evaluation unit 505 divides the acquired captured image into a plurality of blocks [0065]; rectangular [0046]), each block comprising a plurality of pixels (inherent).
Regarding Claim 4, Maeda (US PG Publication 2021/016600) discloses the surveillance camera of claim 3, wherein the at least one processor is further configured to:
determine an [] brightness of the each block (evaluation unit 505 evaluates whether each block has quality [0065]) …;
determine the [] brightness of the object block based on the [] brightness each block (the face detection unit 502 determines that the block numbers of the blocks including the face [0141]);
and control the brightness of the at least one target (LED that irradiates the image capturing range corresponding to the block [0066]) [] LED (LEDs 112, Fig. 2B, 3B) based on the [] brightness of the object block (adjustment unit 506 adjusts the light emission intensity of the LED that irradiates the image capturing range corresponding to the block according to the evaluation unit 505 [0066]) ….
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches IR light (infrared range [0020] light source [0046]).
Maeda does not disclose, but Hong (US PG Publication 2005/00065655) teaches wherein the at least one processor is further configured to:
determine an average brightness (calculate the average intensity [0031]) based on a brightness of the plurality of pixels (intensity of every pixel divided by the number of pixels [0031]),
determine the average brightness… based on the average brightness (calculate the average intensity [0031]) …,
and control the brightness of the at least one target [] based on the average brightness (When determined that light is in a dark state as a result of determination of the image intensity comparator 220, the light controller 270 turns on a light device increases an amount of light output from the light device. When determined that light is in a bright state as a result of determination of the image intensity comparator 220), the light controller 270 turns off the light device or reduces the amount of light output from the light device [0042]) and the predetermined reference brightness (compares the calculated average intensity to a predetermined reference value [0031]).
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
One of ordinary skill in the art before the application was filed would have been motivated to control the LEDs of Maeda based on the brightness of the image of Maeda, as suggested by Hong, because Maeda adjust LEDs to capture images with constant brightness [0095], which requires knowing the actual brightness of the captured image.
Regarding Claim 5, Maeda (US PG Publication 2021/016600) discloses the surveillance camera of claim 4, … LED (LEDs [0027]).
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches wherein the at least one processor (controller 416, Fig. 4) is further configured to:
based on a limit brightness of the at least one target IR (infrared range [0020]) light emitting [] (light source [0046]) being less than the predetermined reference brightness (target light source illuminance level limited by predetermined threshold [0046]), compensate for the brightness of the object block by amplifying a gain of an image sensor included in the camera (target gain is calculated [0046]; includes increasing the gain to achieve the target image brightness [0049]).
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
Regarding Claim 6, Maeda (US PG Publication 2021/016600) discloses the surveillance camera of claim 5.
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches wherein the at least one processor is further configured to determine an amount of gain amplification of the image sensor according to the brightness of the object block (target gain, equation 7, [0059], is based on current brightness Bc [0031], eq. 7, and target brightness BT [0037], eq. 7).
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
Claim(s) 11, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maeda (US PG Publication 2021/016600) in view of Verma (US PG Publication 2021/0195081), Kaneda (US PG Publication 2007/0122036), and Hong (US PG Publication 2005/00065655).
Regarding Claim 11, Maeda (US PG Publication 2021/016600) discloses a surveillance camera (image capturing unit 111 [0048]) comprising:
a camera (image capturing unit 111 [0048]) comprising a plurality of [] light emitting diodes (12 sets of LEDs and diffractive optical elements are arranged around the image capturing unit 111 [0048], the captured image 300 of the image capturing unit 111 is divided into 12 blocks [0048]);
and at least one processor (CPU 401 is an arithmetic device that executes various programs installed in the ROM 402 [0053]) configured to:
recognize an object (image processing unit 121 detects a face [0030]) … from an image obtained through the camera (image capturing unit 111 captures an image of a subject [0028]; captured image 300 of camera 111 [0047],
partition an image acquired through the camera (image capturing unit 111 captures an image of a subject [0028]; captured image 300 of camera 111 [0047]) into a plurality of blocks (captured image 300 of camera 111 is divided into blocks corresponding to the number of LEDs [0047], Fig. 3B; the image quality evaluation unit 505 divides the acquired captured image into a plurality of blocks [0065]),
determine at least one target [] LED (the LED that irradiates the image capturing range corresponding to the block [0066]) corresponding to coordinate information of the object (the face detection unit 502 determines that the block numbers of the blocks including the face [0141]) among the plurality of [] LEDs (12 sets of LEDs and diffractive optical elements are arranged around the image capturing unit 111 [0048]),
determine an object block (e.g., blocks 2, 5, 8, 11 among all the blocks in the image frame, Fig. 6B, [0073]-[0078]) comprising at least one block that includes an object (Fig. 6B block 2 has object, block 5 has object, or blocks 8 and 11 have object, [0073]-[0078]),
calculate a brightness (calculates level of image quality [0065] and calculates the light emission intensity necessary for detecting the face area of the subject [0032]) of the object block (evaluates each block [0065]) as [] brightness of the object block (evaluates each block [0065]),
compare the brightness of the object block (evaluates whether or not each block has a level of image quality where a face area may be detected [0065]) …, and
in response to the brightness (calculates level of image quality [0065]) of the object block (evaluates each block [0065]) …, control a brightness of the at least one target [] LED (adjustment unit 506 adjusts the light emission intensity of the LED that irradiates the image capturing range corresponding to the block [0066]) such that the brightness of the object block (according to the evaluation unit 505 [0066]) reaches a predetermined brightness (obtain a captured image that has a level of image quality where a face area may be detected [0066]; appropriate brightness [0023]; constant brightness [0024]);
wherein the predetermined reference brightness (appropriate brightness [0023]; constant brightness [0024]) refers to a minimum intensity of light (image to be captured with appropriate brightness, constant brightness [0023]-[0024]) required for object recognition (level of image quality where a face area may be detected [0065]) in a low-light environment (some objects appear dark [0023]).
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches IR light (infrared range [0020] light source [0046]).
Maeda does not disclose, but Kaneda (US PG Publication 2007/0122036) teaches recognize an object through a deep learning-based object recognition algorithm (using a CNN to detect a face [0085] – [0135]).
Maeda does not disclose, but Hong (US PG Publication 2005/00065655) teaches calculate a brightness as an average brightness (calculate the average intensity [0031]),
compare the brightness … with a predetermined reference brightness (compares the calculated average intensity to a predetermined reference value [0031]), and
in response to the brightness being lower than the predetermined reference brightness (When determined that light is in a dark state as a result of determination of the image intensity comparator 220… When determined that light is in a bright state as a result of determination of the image intensity comparator 220, [0042]), control a brightness (the light controller 270 turns on a light device increases an amount of light output from the light device… the light controller 270 turns off the light device or reduces the amount of light output from the light device [0042]) ….
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
One of ordinary skill in the art before the application was filed would have been motivated to detect the faces of Maeda using a CNN as taught in Kaneda because using CNNs to detect faces has been common-place since the early 2000s, and was, at the time of application-filing, routinely implementable and known to produce accurate results.
One of ordinary skill in the art before the application was filed would have been motivated to control the LEDs of Maeda based on the intensity of the image of Maeda, as suggested by Hong, because Maeda adjust LEDs to capture images with constant brightness [0095], which requires knowing the actual brightness of the captured image.
Regarding Claim 15, the claim is rejected on the grounds provided in Claim 4.
Regarding Claim 16, the claim is rejected on the grounds provided in Claim 5.
Regarding Claim 18, the claim is rejected on the grounds provided in Claim 11.
Regarding Claim 19, Maeda (US PG Publication 2021/016600) discloses the control method of claim 18, wherein, based on an arrangement of the plurality of [] LEDs (arrangement of Fig. 2B), an image capture area of the surveillance camera is divided into a plurality of areas according to respective illumination areas of the plurality of [] LEDs (captured image 300 of camera 111 is divided into blocks corresponding to the number of LEDs [0047], Fig. 3B; the image quality evaluation unit 505 divides the acquired captured image into a plurality of blocks [0065]; the captured image 300 of the image capturing unit 111 is divided into 12 blocks, each block is numbered, and the corresponding set of LED and diffractive optical element is indicated by a lead line [0048]), and wherein the method further comprises:
obtaining a location of the object and a location of the object block (the face detection unit 502 determines that the block numbers of the blocks including the face [0141]);
determining the at least one target [] LED corresponding to the illumination area that includes the location of the object block (the LED that irradiates the image capturing range corresponding to the block according to the evaluation unit 505 [0066]) among the plurality of [] LEDs (12 sets of LEDs and diffractive optical elements are arranged around the image capturing unit 111 [0048]); and
controlling the brightness (adjustment unit 506 adjusts the light emission intensity of [0066]) of the at least one target [] LED (the LED that irradiates the image capturing range corresponding to the block according to the evaluation unit 505 [0066]) ….
Maeda does not disclose, but Verma (US PG Publication 2021/0195081) teaches IR light (infrared range [0020] light source [0046]).
Maeda does not disclose, but Hong (US PG Publication 2005/00065655) teaches controlling the brightness (When determined that light is in a dark state as a result of determination of the image intensity comparator 220, the light controller 270 turns on a light device increases an amount of light output from the light device. When determined that light is in a bright state as a result of determination of the image intensity comparator 220), the light controller 270 turns off the light device or reduces the amount of light output from the light device [0042]) … based on the predetermined reference brightness (compares the calculated average intensity to a predetermined reference value [0031]).
One of ordinary skill in the art before the application was filed would have been motivated to modify Maeda to use sensor gain when LED control is non-viable to achieve the desired uniform brightness because Verma teaches that it would preserve the safety of the object being imaged [0062] while still satisfying the requirements of brightness than permits image analysis.
One of ordinary skill in the art before the application was filed would have been motivated to control the LEDs of Maeda based on the brightness of the image of Maeda, as suggested by Hong, because Maeda desires capturing images with constant brightness [0095], which requires knowing the actual brightness of the captured image.
Regarding Claim 20, the claim is rejected on the grounds provided in Claim 5.
Response to Argument
Applicant’s remarks filed 1/9/2026 are unpersuasive.
Applicant argues that Maeda does not disclose “determine an object block comprising at least one object” based on Fig. 9B. Remarks at 12. This is not persuasive because Applicant has not considered Maeda Fig. 6B which demonstrates the detection of faces in different blocks of the image.
Applicant argues that Maeda does not disclose “calculate a brightness of the object block…,”and that although Maeda discloses calculating “quality,” no examples or definitions are disclosed. Remarks at 12. Examiner agrees that no definitions are disclosed, but disagrees that one of ordinary skill in the art could not infer a definition. For example, Maeda discloses obtaining an image with “appropriate brightness” and “constant brightness.” Maeda at [0023]-[0024]. Maeda also discloses adjusting the light emission intensity as a result of the “quality” evaluation. Maeda at [0065]-[0066]. There is no reason to change light emission intensity if lighting is not the problem in the image. Therefore, one of ordinary skill in the art can conclude that Maeda is evaluating image brightness as “quality.”
Applicant argues that the combination of references does not disclose “in response to the brightness of the object block being lower than a predetermined reference brightness, control the brightness…” and “the predetermined reference brightness refers to a minimum intensity…” because Maeda discloses Table 510, which associates LED intensity with image regions—rather than reference brightness and average brightness. Remarks at 13-14. This is not persuasive because in addition to Table 510, Maeda discloses image quality evaluation unit 505 and light emission intensity output unit 507 which adjusts the light emission intensity not just based on Table 510, but also on the quality evaluation of each block from unit 505 and the adjustment from adjustment unit 506. Maeda at Fig. 5.
Maeda does not explicitly disclose a “predetermined reference brightness” and a “minimum intensity of light,” see Remarks at 16, but Maeda does disclose a level of image quality where a face can be detected, and that some faces appear darker based on distance, and that constant and appropriate brightness is desired. There is enough to conclude that Maeda evaluates quality to change the emission light intensity when an image is too dark, meaning that there is a predetermined/minimum/reference light intensity as claimed. Hong also teaches these features, as cited in the office action.
Because Applicant’s arguments regarding Maeda’s Table 510 (and neglecting of evaluation unit 505 and adjustment unit 506) are unpersuasive, Applicant’s arguments against the combination of Maeda and Hong are also unpersuasive. See remarks at 16-17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190364182 A1
US 20230050340 A1
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.
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/SHADAN E HAGHANI/Examiner, Art Unit 2485