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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/21/2026 has been entered.
In the Instant Amendment, Claim(s) 1, 3, 6, 16-17 and 21 has/have been amended; Claim(s) 1, 16 and 21 is/are independent claims. Claims 1-21 have been examined and are pending in this application.
Information Disclosure Statement
The information disclosure statement(s) submitted on 9/2/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Arguments
The rejections of claims 1-21 under 35 U.S.C 112(a) are withdrawn because of the amendment and the persuasive argument in the remarks (pages 11-12).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 16 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1).
Regarding claim 1, Hayaishi teaches A camera device (Figs. 41-43), comprising:
an image sensor (imaging device 508) configured to capture an image (Fig. 41);
an object identifier (CPU 518) configured to identify an object included in the captured image (Fig. 42; S820);
a distance measurement device (CPU 518) configured to determine a distance between the camera device and the identified object based on an occupancy percentage of the identified object in the captured image (Figs. 6, 8; paras. 0102-0108, 0227; S830; “subject distance estimation unit 330 acquires information that is necessary to calculate the subject distance Sd using Equation (3)”, “Sd=(Wwi.times.Wf.times.f)/(Wfi.times.Wx) (3)”); and
a controller (CPU 518) configured to:
determine a focus range based on the distance determined by the distance measure device (para. 0230: “In step S850 (FIG. 42), the focal range setting unit 660 (FIG. 41) sets a focal range FR on the basis of the subject distance Sd that is estimated in step S830”); and
determine an optimal focus location where a reference value is the largest while moving a lens in a focus range (Fig. 42; S850-S860; paras. 0230-0231),
but fails to teach
wherein the distance measurement device is further configured to determine the distance by additionally reflecting both a location of the identified object and lens distortion information.
However, in the same field of endeavor Asano teaches
wherein the distance measurement device is further configured to determine the distance by additionally reflecting both a location of the identified object and lens distortion information (Fig. 29; pages 13-15, 4; calculate a distance to a detected object based on a detected size of the detected object corrected in consideration of both a location of the identified object and lens distortion information).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Asano in Hayaishi to have wherein the distance measurement device is further configured to determine the distance by additionally reflecting both a location of the identified object and lens distortion information for improving accuracy of distance estimation yielding a predicted result.
Regarding claim 2, the combination of Hayaishi and Asano teaches everything as claimed in claim 1. In addition, Hayaishi teaches wherein the reference value comprises contrast data or edge data (paras. 0230-0231).
Regarding claim 16, the method of claim 16 reciting steps corresponding to claim 1 is also rejected for the same reasons above.
Regarding claim 21, claim 21 reciting features corresponding to claim 1 is also rejected for the same reasons above. In addition, Hayaishi teaches A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor (paras. 0257-0258) to: (corresponding features as claimed in claim 1).
Claim(s) 3-4 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) or, in the alternative, and further in view of Reddy (US 9927974 B2).
Regarding claim 3, the combination of Hayaishi and Asano teaches everything as claimed in claim 1. In addition, Hayaishi teaches further comprising a storage configured to store specification information of the camera device, wherein the distance measurement device is further configured to obtain an angle of view (the angle of view .theta. or focal length f) in a vertical direction from the specification information (Figs. 6, 8; paras. 0102-0108); and
determine the distance (distance Sd) between the camera device and the identified object based on the obtained angle of view in the vertical direction, a ratio of a size of the identified object, and a physical size of the identified object, and wherein the ratio of the size of the identified object corresponds a ratio of a size of at least a portion of the identified object in the vertical direction to a size of the captured image in the vertical direction (Fig. 8; paras. 0102-0108).
Or in the alternative, in the same field of endeavor Reddy teaches
further comprising a storage configured to store specification information of the camera device, wherein the distance measurement device is further configured to obtain an angle of view in a vertical direction from the specification information; and determine the distance between the camera device and the identified object based on the obtained angle of view in the vertical direction, a ratio of a size of the identified object, and a physical size of the identified object, and wherein the ratio of the size of the identified object corresponds a ratio of a size of at least a portion of the identified object in the vertical direction to a size of the captured image in the vertical direction (Fig. 1A, 4D; col. 7; from the equation EQ(2) in step 463, the distance d can be calculated).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Reddy in the combination to have further comprising a storage configured to store specification information of the camera device, wherein the distance measurement device is further configured to obtain an angle of view in a vertical direction from the specification information; and determine the distance between the camera device and the identified object based on the obtained angle of view in the vertical direction, a ratio of a size of the identified object, and a physical size of the identified object, and wherein the ratio of the size of the identified object corresponds a ratio of a size of at least a portion of the identified object in the vertical direction to a size of the captured image in the vertical direction for utilizing an alternative object distance calculation configuration allowing distance to be determined with optimized parameters yielding a predicted result.
Regarding claim 4, the combination of Hayaishi, Asano and Reddy teaches everything as claimed in claim 3. In addition, Hayaishi teaches wherein the object comprises a person, and wherein the portion of the object comprises a face of the person (Hayaishi: Figs. 6, 8).
Regarding claim 17, claim 17 reciting features corresponding to claim 3 is also rejected for the same reasons above.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) as applied to claim 3 above, and further in view of Park et al (US 20200372794 A1).
Regarding claim 5, the combination of Hayaishi and Asano teaches everything as claimed in claim 3, but fails to teach
wherein the object comprises a vehicle, and wherein the portion of the object comprises a license plate of the vehicle.
However, in the same field of endeavor Park teaches
wherein the object comprises a vehicle, and wherein the portion of the object comprises a license plate of the vehicle (Fig. 2; para. 0056: “distance (D) between vehicle and camera=f×P/(s×p) [Equation 5] (where f is the focal length of the camera, P is the actual size of the license plate, s is the pixel size, and p is the size of the license plate detected in the image)”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Park in the combination to have wherein the object comprises a vehicle, and wherein the portion of the object comprises a license plate of the vehicle for acquiring focused image data of a license plate of the vehicle for enabling a vehicle-speed measuring system yielding a predicted result.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) as applied to claim 3 above, and further in view of Kim et al (US 20160094791 A1).
Regarding claim 6, the combination of Hayaishi and Asano teaches everything as claimed in claim 3, but fails to teach
wherein the controller is further configured to: obtain locus data from the specification information stored in the storage; and determine the focus range based on the distance determined by the distance measurement device and based on the locus data, and wherein the locus data corresponds to a focus location determined based on a distance to the object at a specific zoom magnification.
However, in the same field of endeavor Kim teaches
wherein the controller is further configured to: obtain locus data from the specification information stored in the storage; and determine the focus range based on the distance determined by the distance measurement device and based on the locus data, and wherein the locus data corresponds to a focus location determined based on a distance to the object at a specific zoom magnification (paras. 0021, 0061-0072, 0101).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Kim in the combination to have wherein the controller is further configured to: obtain locus data from the specification information stored in the storage; and determine the focus range based on the distance determined by the distance measurement device and based on the locus data, and wherein the locus data corresponds to a focus location determined based on a distance to the object at a specific zoom magnification for maintaining focus while minimizing a time that elapses for changing zoom power yielding a predicted result.
Claim(s) 7-9, 11 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) as applied to claim 1 above, and further in view of Gum (US 20130258167 A1).
Regarding claims 7-9 and 11, the combination of Hayaishi and Asano teaches everything as claimed in claim 1, but fails to teach
Claim 7: The camera device of claim 1, wherein the captured image comprises a plurality of objects, and wherein the identified object is an object selected among the plurality of objects.
Claim 8: The camera device of claim 7, wherein the identified object is selected as an object closest to a center of the captured image among the plurality of objects.
Claim 9: The camera device of claim 7, wherein the identified object is selected as an object comprising a size that is standardized among the plurality of objects.
Claim 11: The camera device of claim 7, wherein the controller is further configured to set a window in the captured image around the identified object, and wherein the controller is configured to determine the optimal focus location in the set window.
However, in the same field of endeavor Gum teaches
Claim 7: The camera device of claim 1, wherein the captured image comprises a plurality of objects, and wherein the identified object is an object selected among the plurality of objects (paras. 0038, 0063, 0074).
Claim 8: The camera device of claim 7, wherein the identified object is selected as an object closest to a center of the captured image among the plurality of objects (paras. 0038, 0063, 0074; “one particular object may be selected for focus at least in part if the object is located closer to the center of the frame than other objects of the same color”).
Claim 9: The camera device of claim 7, wherein the identified object is selected as an object comprising a size that is standardized among the plurality of objects (paras. 0066-0067; “objects smaller than a threshold size may not be considered for focus prioritization. This may avoid some spurious effects that could occur if the imaging device or camera attempted to focus on very small objects in a scene”).
Claim 11: The camera device of claim 7, wherein the controller is further configured to set a window (priority object region) in the captured image around the identified object, and wherein the controller is configured to determine the optimal focus location in the set window (paras. 0057, 0077; “Autofocusing on the higher priority object or objects may include selecting a lens focus position that provides for increased contrast of the moving object within the scene”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Gum in the combination to have the features of these claims for providing prioritizing autofocusing on a desirable object over multiple objects so that a better focus can be obtained automatically to a desirable object yielding a predicted result.
Regarding claim 18, claim 18 reciting features corresponding to claims 7-8 is also rejected for the same reasons above.
Regarding claim 19, claim 19 reciting features corresponding to claims 7 and 9 is also rejected for the same reasons above.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) and Gum (US 20130258167 A1) as applied to claim 7 or 16 above, and further in view of Huang et al (US 20210192756 A1).
Regarding claim 10, the combination of Hayaishi, Asano and Gum teaches everything as claimed in claim 7, but fails to teach
wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the plurality of objects based on the deep learning-based object detection algorithm, and wherein the identified object is selected as an object having higher accuracy among the plurality of objects.
However, in the same field of endeavor Huang teaches
wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the plurality of objects based on the deep learning-based object detection algorithm, and wherein the identified object is selected as an object having higher accuracy among the plurality of objects (paras. 0044, 0059).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Huang in the combination to have wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the plurality of objects based on the deep learning-based object detection algorithm, and wherein the identified object is selected as an object having higher accuracy among the plurality of objects for utilizing improved techniques capable of utilizing the detection accuracy of deep-learning based object detection while reducing computational cost yielding a predicted result.
Regard claim 20, claim 20 reciting features corresponding to claim 10 is also rejected for similar reasons as in claim 10.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) as applied to claim 3 above, and further in view Huang et al (US 20210192756 A1).
Regard claim 12, the combination of Hayaishi and Asano teaches everything as claimed in claim 1, but fails to teach
wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the identified object based on the deep learning-based object detection algorithm, and wherein the focus range is set based on the obtained accuracy.
However, in the same field of endeavor Huang teaches
wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the identified object based on the deep learning-based object detection algorithm (Huang: paras. 0044, 0059), and wherein the focus range is set based on the obtained accuracy (Hayaishi: the focus range is set based on an identified object [fig. 42]; Huang: the identified object is detected based on the obtained accuracy).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Huang in Hayaishi to have wherein the object identifier is configured to identify the object based on a deep learning-based object detection algorithm, wherein the object identifier is configured to obtain an accuracy of the identified object based on the deep learning-based object detection algorithm, and wherein the focus range is set based on the obtained accuracy for utilizing improved techniques capable of utilizing the detection accuracy of deep-learning based object detection while reducing computational cost yielding a predicted result.
Claim(s) 11 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayaishi (US 20090028390 A1) in view of Asano et al (JP-WO2014171052-A1) as applied to claim 3 above, and further in view of Park (US 10182196 B2).
Regarding claim 13, Hayaishi teaches everything as claimed in claim 1, but fails to teach
Claim 13. The camera device of claim 1, wherein the controller is further configured to set a window in the captured image based on a movement of the identified object, and wherein the controller is configured to determine the optimal focus location in the set window.
Claim 14. The camera device of claim 13, wherein the controller is further configured to change a size of the window based on the movement of the identified object with respect to the image sensor.
Claim 15. The camera device of claim 13, wherein the controller is further configured to moves and set the window to a predicted location based on movement of the identified object to another location in the captured image.
However, in the same field of endeavor Park teaches
Claim 13. The camera device of claim 1, wherein the controller is further configured to set a window in the captured image based on a movement of the identified object, and wherein the controller is configured to determine the optimal focus location in the set window (Figs. 11-12; col. 21, line 35 to col. 22, line 7; movement of the subject is tracked and a window/ROI block is enlarged, reduced or moved in response to the movement).
Claim 14. The camera device of claim 13, wherein the controller is further configured to change a size of the window based on the movement of the identified object with respect to the image sensor (Figs. 11-12; col. 21, line 35 to col. 22, line 7).
Claim 15. The camera device of claim 13, wherein the controller is further configured to moves and set the window to a predicted location based on movement of the identified object to another location in the captured image (Figs. 11-12; col. 21, line 35 to col. 22, line 7).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Park in the combination to have features of these claims for allowing a subject to be quickly and accurately tracked increasing the accuracy of the 3A algorithm yielding predicted result.
Regarding claim 11, claim 11 (addressed by the combination of Hayaishi, Asano and Gum) reciting features corresponding to claim 13, in the alternative, can also be rejected further in combination with Park for the same reason as presented in claim 13.
Additional Rejections
Terashima (US 20070286590 A1) also teaches the same as Hayashi above. Terashima teaches identifying a face, determining a distance to the face using equation 1.1 (para. 0172), determining a focus range based on the determined distance and determining an optimal focus (Figs. 4-6). Terashima can be used in placed of Hayashi to address the claims for the same reasons.
Conclusion
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/Quan Pham/Primary Examiner, Art Unit 2637
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