DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the following communication: an amendment
filed on 02/20/2026.
Claims 1,2, 4,7,8-12, 15-17,20-26,29,30 are currently pending and presented for examination.
Response to Arguments
Applicant's arguments filed 02/20/2026 with respect to prior art rejection of claims have been fully considered and they are not persuasive.
On labeled pages 10-11 of remarks filed on 02/20/2026, the Applicant argues that prior art Cuban discloses “there may be two blobs detected that correspond to different type of objects”. However, classifying objects based on general blob shapes within a single image is not equivalent to “cluster[ing] a set of images into a plurality of image groups based on at least one similarity” as recited by the amended claim 1. Cuban also does not disclose “evaluating each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors”. Cuban fails to teach or suggest “train an artificial intelligence (A) or machine learning (ML)(AI/ML) model to identify one or more relevant objects in a field of view (FOB) of a camera based on the set of image statistics or the set of image capturing behaviors “ as recited by amended claim 1. The arguments set forth above apply equally to claim 11 since claim 11 recites features similar to those of claim 1.
On Labeled page 12-13, Prior art Maeda discloses camera system including "a detection unit that detects faces and eyes from an image," "an image forming state detection unit," and "a display unit that displays an indication superimposed on an image and corresponding to a position and size of the eye detected by the detection unit." However, detecting specified objects (e.g., faces or eyes) from an image is not equivalent to "cluster[ing] one or more scenes into a plurality of scene groups based on at least one similarity between the plurality of scene groups," as recited by amended claim 16. Furthermore, Maeda does not teach or
suggest "evaluate each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors," as recited by amended claim 16.
Maeda explains that "a detection unit detects eyes from an image," and that control logic determines "whether the size of the eye is larger than a first threshold" or "larger than a second threshold" in order to decide how autofocus control is performed (see Maeda, FIG. 11 and corresponding description). In this approach, camera mode selection is driven by real-time detection outcomes
and fixed thresholds. As such, Maeda does not teach or suggest "evaluate each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors," as recited by amended claim 16.
Similarly, as discussed above, Cuban also fails to teach or suggest at least "cluster one or more scenes into a plurality of scene groups based on at least one similarity between the plurality of scene groups; evaluate each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors; and train an artificial intelligence (AI) or machine learning (ML) (AI/ML) model to identify a camera mode or a set of parameters to be applied to a camera under a specific scene based on the set of mode selection statistics or the set of image capturing behaviors," as recited by amended claim 16.Independent claim 25 recites features similar to those of claim 16. Accordingly, the arguments set forth above apply equally to claim 25.
In response, the Examiner understands the Applicant’s arguments but respectfully disagrees for the reasons set forth below:
Figure 9 and Para 48, 65 of prior art Cuban discloses the image data is analyzed 904 to detect a representation of an object within the scene. The image data can be deleted 906 after the analysis. The representation of the object can be compared 908 to one or more object models to determine 910 an object type of the object based on the object models. Additional image data may be received 916. It is then determined 918 whether a new object is detected as being represented in the image data. if a new object is detected, a representation of the new object is compared 908 to the object models to determine 910 an object type for the new object. Therefore, not only objects within one image are detected , objects ( image portions of a set of images ) from multiple images (set of images) can be detected/classified into certain object group/type (image groups) based on feature points and similar shapes; the image portion being detected / recognized must be stored in the device in order to be displayed; therefore , the storage area wherein the image portions /objects being stored can be considered as image cluster since the detected and classified objects are saved in certain folder/ storage area. Therefore, Cuban does disclose “cluster[ing] a set of images into a plurality of image groups based on at least one similarity” in claim 1.
Furthermore, please note that “mode selection statistics” or “image capturing behaviors” are broad terms. Para 48,49,59,62-64 of prior art Cuban disclose each object detected in each (each image portion /image group) can be determined as objects of interest if a minimum confidence value has been reached, then relevant information can be determined for example, a human object might have descriptors relating to height, clothing color, gender, or other aspects discussed elsewhere herein. A vehicle, however, might have descriptors such as vehicle type and color, etc. The information can show the size of the objects/ items that the user tends to capture ; it can also indicate whether the user likes to capture human objects more or other objects including vehicle/animals more which reflects user’s image capturing behaviors. Therefore, Cuban does disclose “evaluating each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors” as disclosed in claim 1.
Para 48, 49, 59, 64-66; Fig. 10 of Cuban discloses optimizing display content under various conditions. In this example, sets of training data (e.g., data points) are obtained 1002, in which each set of training data includes i) a display content, ii) a condition, and iii) a value of a performance measure. A model can be trained 1004 can be trained using the obtained sets of training data. Specifically, image data can be received 1008 from a camera having a field of view, and a condition associated with the field of view can be determined 1010 from the image data where the condition may also refer to type of objects represented in the image data . The condition may also include a number of objects represented in the image data ; therefore, the optimizing display using trained data and machine learning based models is based on the condition of whether certain types of objects are detected /represented and the number of them which reflects the behavior of the user (certain types of objects the user would like to focus on and capture) ; therefore, Cuban also discloses “train an artificial intelligence (AI) or machine learning (ML) (AI/ML) model to identify one or more relevant objects in a field of view (FOV) of a camera based on the set of image statistics or the set of image capturing behaviors. Please see the responses above for arguments regarding claim 11, since claim 11 recites similar subject matter of claim 1.
Please note that amended claim 16 discloses “cluster[ing] one or more scenes into a plurality of scene groups based on at least one similarity between the plurality of scene groups”. The claim discloses one or more scenes wherein the object identified in an image can be considered as scenes and the claim does not disclose the plurality of scene groups need to be from different images. Para 9, 141, 197-209 of prior art Maeda discloses the shapes of the face, eye, and head are detected in a plurality of images which are continuously generated, and the position and size thereof are stored for each image. Further, relative information which is the relative position and relative size of each of the shapes of the face, eye, and head is stored. Since the detected image portions/types are stored with relative size and position information, the detected scene (face, eye, and head) are classified and clustered into scene groups ( face or eye or head); therefore, prior art Maeda does disclose “cluster[ing] one or more scenes into a plurality of scene groups based on at least one similarity between the plurality of scene groups”.
Furthermore, please note that “mode selection statistics “ and “ image capturing behaviors” are both broad terms. Para 141-144; figure 10(a) of prior art Maeda teaches evaluate the size of detected eye to determine whether the eye detection is accurate in order to perform AF processing , wherein the face-sized indication frame can also be used to show position of the detected face and be used for AF calculation/evaluation; therefore, the AF processing /setting can be considered as mode selection since it calculates different focus points P (different modes corresponding to different frame including face/eyes) or image capturing behaviors since it shows which object the user intends to focus on. In view of the above, prior art Maeda does disclose “evaluate each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors”. For remarks, regarding “train an artificial intelligence (AI) or machine learning (ML) (AI/ML) model to identify a camera mode or a set of parameters to be applied to a camera under a specific scene based on the set of mode selection statistics or the set of image capturing behaviors”. Please see response set forth for claim 1.
In view of the above, the rejection is maintained.
Claim Rejections - 35 USC § 102
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 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 1,2,4,7-9, 11, 12,15 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Cuban et al. (US Pub. No.: US 2019/0122082 A1).
Regarding claim 1, Cuban et al. discloses an apparatus (Para 19; Figs. 1-3; The intelligent content display system) for processing image data,
comprising: at least one memory (Para 19-23; The display device 100 may further include an onboard processor and memory.) ;
and at least one processor (Fig. 4; Para 34, 39; a primary processor 404 (e.g., at least one CPU) can be configured to execute instructions to perform various functionality discussed herein. The example content display system 400 also includes a microcontroller 406 to perform specific tasks with respect to the device) coupled to the at least one memory (Para 25; the processor may be contained in the same device body as the memory and the cameras, which may be communicable with each other via hardwired connections.) and, based at least in part on information stored in the at least one memory, the at least one processor, is configured to:
cluster a set of images into a plurality of image groups based on at least one similarity between the plurality of image groups ( Para 48, 65 ; Fig. 9; there may be two blobs detected that correspond to different types of objects. The first blob can have an outline or other aspect determined that a classifier might indicate corresponds to a human with 85% certainty. Certain classifiers might provide multiple confidence or certainty values, such that the scores provided might indicate an 85% likelihood that the blob corresponds to a human and a 5% likelihood that the blob corresponds to an automobile, based upon the correspondence of the shape to the range of possible shapes for each type of object, which in some embodiments can include different poses or angles, among other such options; the image data is analyzed 904 to detect a representation of an object within the scene. The image data can be deleted 906 after the analysis. The representation of the object can be compared 908 to one or more object models to determine 910 an object type of the object based on the object models. Additional image data may be received 916. It is then determined 918 whether a new object is detected as being represented in the image data. if a new object is detected, a representation of the new object is compared 908 to the object models to determine 910 an object type for the new object. objects ( image portions of a set of images ) from multiple images (set of images) can be detected/classified into certain object group/type (image groups) based on feature points and similar shapes; the image portion being detected / recognized must be stored in the device in order to be displayed; therefore , the storage area wherein the image portions /objects being stored can be considered as image cluster since the detected and classified objects are saved in certain folder/ storage area. );
evaluate each image group in the plurality of image groups for a set of image statistics or a set of image capturing behaviors (Para 48, 49, 59, 64; different classifiers can be used that are trained on different data sets and/or utilize different libraries, where specific classifiers can be utilized to attempt to identify or recognize specific types of objects. For example, a human classifier might be used with a feature extraction algorithm to identify specific feature points of a foreground object, and then analyze the spatial relations of those feature points to determine with at least a minimum level of confidence that the foreground object corresponds to a human. The feature points located can correspond to any features that are identified during training to be representative of a human, such as facial features and other features representative of a human in various poses. Similar classifiers can be used to determine the feature points of other foreground object in order to identify those objects as vehicles, bicycles, or other objects of interest. please note that “mode selection statistics” or “image capturing behaviors” are broad terms. Para 48,49,59,62-64 of prior art Cuban disclose each object detected in each (each image portion /image group) can be determined as objects of interest if a minimum confidence value has been reached, then relevant information can be determined for example, a human object might have descriptors relating to height, clothing color, gender, or other aspects discussed elsewhere herein. A vehicle, however, might have descriptors such as vehicle type and color, etc. The information can show the size of the objects/ items that the user tends to capture ; it can also indicate whether the user likes to capture human objects more or other objects including vehicle/animals more which reflects user’s image capturing behaviors. ); and
train an artificial intelligence (AI) or machine learning (ML) (AI/ML) model to identify the one or more relevant objects in the FOV of the camera based on the set of image statistics or the set of images capturing behaviors (Para 48, 49, 59, 64; in various embodiments, the object type may be detected using various machine learning based models, such as artificial neural networks, trained to classify detected objects (e.g., group of feature points) as belonging to one or more object types. In various embodiments in which the object is detected to be a person, the group of feature points representing the object (a subset thereof) may also be analyzed using real-time inference techniques to determine an emotional state of the person, which may be used to data collection or content selection. Steps 806 through 812 may be performed for any or all of the plurality of objects detected at step 804. Accordingly, one or more object types of the plurality of objects are determined 814. For example, it may be the case that the objects are determined to belong to the same object type, or different object types. Para 48, 49, 59, 64-66; Fig. 10 of Cuban discloses optimizing display content under various conditions. In this example, sets of training data (e.g., data points) are obtained 1002, in which each set of training data includes i) a display content, ii) a condition, and iii) a value of a performance measure. A model can be trained 1004 can be trained using the obtained sets of training data. Specifically, image data can be received 1008 from a camera having a field of view, and a condition associated with the field of view can be determined 1010 from the image data where the condition may also refer to type of objects represented in the image data . The condition may also include a number of objects represented in the image data ; therefore, the optimizing display using trained data and machine learning based models is based on the condition of whether certain types of objects are detected /represented and the number of them which reflects the behavior of the user (certain types of objects the user would like to focus on and capture)).
identify, using the AI/ML model, the one or more relevant objects in the FOV of the camera (Para 40; The display device 502 is positioned with the front face substantially vertical, and the detection device at an elevated location, such that the field of view 504 of the cameras of the device and the display is directed towards a region of interest 508, where that region is substantially horizontal (although angled or non-planar regions can be analyzed as well in various embodiments). The video data can be analyzed using any appropriate object recognition process, computer vision algorithm, artificial neural network (ANN), or other such mechanism for analyzing image data (i.e., for a frame of video data) to detect objects in the image data. The detection can include, for example, determining feature points or vectors in the image data that can then be compared against patterns or criteria for specific types of objects, in order to identify or recognize objects of specific types. Such an approach can enable objects such as benches or tables to be distinguished from people or animals, such that only information for the types of object of interest can be processed; Para 40; such that the field of view 504 of the cameras of the device and the display is directed towards a region of interest 508, where that region is substantially horizontal (although angled or non-planar regions can be analyzed as well in various
embodiments). As mentioned, the cameras can be angled such that a primary axis 512 of each camera is pointed towards a central portion of the region of interest);
and output an indication of the one or more relevant objects in the FOV of the camera ( Para 41-43; Fig. 6; The process can then determine a location of each person, such as by determining a boundary, centroid location, or other such location identifier. The process can then provide this data as output, where the output can include information such as an object identifier, which can be assigned to each unique object in the video data, a timestamp for the video frame(s), and coordinate data indicating a location of the object at that timestamp. In one embodiment, a location (x, y, z) timestamp (t) can be generated as well as a set of descriptors (d1, d2, . . . ) specific to the object or person being detected and/or tracked. Object matching across different frames within a field of view, or across multiple fields of view, can then be performed using a multidimensional vector (e.g., x, y, z, t, d1, d2, d3, . . . ). The coordinate data can be relative to a coordinate of the detection device or relative to a coordinate set or frame of reference previously determined for the detection device. the dotted lines represent people 602 who are contained within the field of view of the cameras of a detection device, and thus represented in the captured video data. After recognition and analysis, the people can be represented in the output data by bounding box 604 coordinates or centroid coordinates 606, among other such options.).
Regarding claim 2, Cuban et al. discloses the apparatus of claim 1, wherein to output the indication of the one or more relevant objects in the FOV of the camera, the at least one processor is configured to: consume the indication of the one or more relevant objects in the FOV of the camera (Para 43; the people can be represented in the output data by bounding box 604 coordinates or centroid coordinates 606, among other such options. As mentioned, each person (or other type of object of interest) can also be assigned a unique identifier 608 that can be used to distinguish that object, as well as to track the position or movement of that specific object over time.) ; store the indication of the one or more relevant objects in the FOV of the camera ( Para 43; Where information about objects is stored on the detection device for at least a minimum period of time, such an identifier can also be used to identify a person that has walked out of, and back into, the field of view of the camera.) ; or transmit the indication of the one or more relevant objects in the FOV of the camera (Para 43; such information could be transmitted if desired and permitted in at least certain embodiments.).
Regarding claim 4, Cuban et al. discloses the apparatus of claim 1, wherein the set of images is a set of photographs or a set of frames associated with a video (Para 40,41; Figs. 5, 6; the cameras capture video data which can then be processed by at least one processor on the detection device. Object matching across different frames within a field of view).
Regarding claim 7, Cuban et al. discloses the apparatus of claim 1, wherein the plurality of image groups includes:
a first image group for at least one specified animal (Para 15, 28; one or more feature values may be determined from the representation of the scene to determine a representation of one or more objects, such as humans, animals, vehicles, etc.) ,
a second image group for at least one specified person ( Para15, 28; The detected objects may be classified as belonging to one or more object types, and display content can be selected based on the one or more objects of the objects appearing in the scene. For example, the system may detect a group of approximately teenage boys appearing in the scene and select content to display that is likely to appeal to the teenage boys. one or more feature values may be determined from the representation of the scene to determine a representation of one or more objects, such as humans, animals, vehicles, etc.),
a third image group for at least one specified outdoor environment ( Para 28; one or more feature values may be determined from the representation of the scene to determine a representation of one or more objects, such as humans, animals, vehicles, etc.),
a fourth image group for at least one specified indoor environment ( Para 55; a number of people inside a particular store in a shopping plaza may be detected, ) ,
a fifth image group for at least one specified image subject (Para 28; one or more feature values may be determined from the representation of the scene to determine a representation of one or more objects, such as humans, animals, vehicles, etc.),
a sixth image group for at least one specified scenario (Para 15,66; Various embodiments enable detection of certain conditions of an environment or scene (e.g., viewer demographics, weather conditions, traffic conditions) and selection of display content based at least in part on the detected conditions. Specifically, systems and method provided herein enable the detection of objects appearing in a scene captured by an image sensor such as a camera. The detected objects may be classified as belonging to one or more object types, and display content can be selected based on the one or more objects of the objects appearing in the scene. For example, the system may detect a group of approximately teenage boys appearing in the scene and select content to display that is likely to appeal to the teenage boys. The system may subsequently detect an adult female enter the scene and update the display-to-display content that may be more likely to appeal to the adult female. Other scenarios and conditions may be taken into account, such as combinations of object types, number of objects, travel direction of objects, among others.), or
a combination thereof (Para 15, 28, 55,66; Other scenarios and conditions may be taken into account, such as combinations of object types, number of objects, travel direction of objects, among others.).
Regarding claim 8, Cuban et al. discloses the apparatus of claim 1, wherein the at least one processor, is further configured to:
determine whether the AI/ML model is capable of identifying the one or more relevant objects in the FOV of the camera with a confidence level exceeding a confidence threshold (Para 50,62; After processing using a computer vision algorithm with the appropriate classifiers, libraries, or descriptors, for example, a result can be obtained that is an identification of each potential object of interest with associated confidence value(s). One or more confidence thresholds or criteria can be used to determine which objects to select as the indicated type. objects with at least a minimum confidence value that corresponding to specified types of objects may be selected as objects of interest); and
deploy the AI/ML model for identifying the one or more relevant objects in the FOV of the camera if the AI/ML model has the confidence level exceeding the confidence threshold,
Regarding claim 9, Cuban et al. discloses wherein the at least one processor, is further configured to:
store the set of images on a storage associated with the camera or on a cloud server (Para 23; the memory can have sufficient capacity to store a certain number of frames of video content from both cameras 104, 106 for analysis. In various embodiments, the frames are discarded or detected from memory immediately upon analysis thereof in a subset of embodiments, the persistent storage may have sufficient capacity to store a limited amount of video data, such as video for a particular event or occurrence detected by the device. Para 36; then transmit the stored data in a batch to the target destination.).
Regarding claim 11, 12,15, the subject matter disclosed in claims 11,12,15 are similar to the subject matter disclosed in claims 1,2,8 respectively; therefore, claims 11,12,15 are rejected for the same reason as set forth in claims 1,2,8 respectively.
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 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.
Claims 16,17,20,21,23,25,26,29,30 are rejected under 35 U.S.C. 102(a) (1) as being unpatentable over Maeda et al. (US Pub. No.: US 2022/0086361 A1), in view Cuban et al. (US Pub. No.: US 2019/0122082 A1).
Regarding claim 16, Maeda et al. discloses an apparatus for processing image data (Para 28; camera system 1), comprising:
at least one memory (Para 31; The body-side control unit 230 includes a storage unit 235 that stores a control program or the like which is executed by the body-side control unit 230,) ;
and at least one processor (Para 31; The body-side control unit 230 is constituted by a microcomputer, its peripheral circuits, and the like. The body-side control unit 230 includes a storage unit 235 that stores a control program or the like which is executed by the body-side control unit 230 ) coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, is configured to:
cluster one or more scenes into a plurality of scene groups based on at least one similarity between the plurality of scene groups (Para 9, 141, 209; detects face and eyes from the image; in addition to the eye, objects that can be detected such as lips, soccer balls, food, or rings may be able to be set by a camera. the reliability of the detected eye is evaluated by comparing the size of the eye with the second threshold. Para 9, 141, 197-209 of prior art Maeda discloses the shapes of the face, eye, and head are detected in a plurality of images which are continuously generated, and the position and size thereof are stored for each image. Further, relative information which is the relative position and relative size of each of the shapes of the face, eye, and head is stored. Since the detected image portions/types are stored with relative size and position information, the detected scene (face, eye, and head) are classified and clustered into scene groups ( face or eye or head););
evaluate each scene group in the plurality of scene groups for a set of mode selection statistics or a set of image capturing behaviors (Para 141-142; figure 10(a); the reliability of the detected eye is evaluated by comparing the size of the eye with the second threshold. In step S380, the body-side control unit 230 causes the AF signal processing unit 270b to perform an AF calculation on the basis of the eye-sized indication frame 51 and ends the processing according to FIG. 11. The AF signal processing unit 270b calculates the amount of defocus on the basis of the focus points P corresponding to the indication frame 51 , Para 196; focus detection using the contrast method may be performed in a case where the size of the detected eye is smaller than a predetermined value, and focus detection using the phase difference detection method may be performed in a case where the size of the detected eye is larger than the predetermined value. In this manner, in a case where the size of the detected eye is small or a case where focus points P are not disposed at a position corresponding to the detected eye, it is possible to perform focus detection appropriately by performing focus detection using the contrast method.) ; and
identify the camera mode or the set of parameters to be applied to the camera under the specific scene based on the set of mode selection statistics or the set of image capturing behaviors ( Para 141-142, 196; Para 73-77, para 209; in a case where the camera has image capturing modes corresponding to a plurality of scenes, a target for detecting its focusing state may be changed in accordance with the image capturing modes. please note that “mode selection statistics “ and “ image capturing behaviors” are both broad terms. Para 141-144; figure 10(a) of prior art Maeda teaches evaluate the size of detected eye to determine whether the eye detection is accurate in order to perform AF processing , wherein the face-sized indication frame can also be used to show position of the detected face and be used for AF calculation/evaluation; therefore, the AF processing /setting can be considered as mode selection since it calculates different focus points P (different modes corresponding to different frame including face/eyes) or image capturing behaviors since it shows which object the user intends to focus on. ) .
However, Maeda et al. does not disclose train an artificial intelligence (AI ) or machine learning (ML)(AI/ML) model.
Cuban et al. discloses train an artificial intelligence (AI ) or machine learning (ML)(AI/ML) model to identify the camera mode or the set of parameters to be applied to the camera under the specific scene based on the set of mode selection statistics or the set of image capturing behaviors (Para 29; 38, the abovementioned optimization model may be a machine learning based model such as one including one or more neural network that have been trained using training data. The training data may include a plurality of sets of training data, in which each set of training data represents one data point. Para 38; different frame rates may be appropriate for different applications. For example, thirty frames per second may be more than sufficient for tracking person movement in a library, but sixty frames per second may be needed to get accurate information for a highway or other high speed location. Depends how which objects being detected/tracked as ROI, the frame rates can be selected differently ; perform autofocusing. Para 48, 49, 59, 64-66; Fig. 10 of Cuban discloses optimizing display content under various conditions. In this example, sets of training data (e.g., data points) are obtained 1002, in which each set of training data includes i) a display content, ii) a condition, and iii) a value of a performance measure. A model can be trained 1004 can be trained using the obtained sets of training data. Specifically, image data can be received 1008 from a camera having a field of view, and a condition associated with the field of view can be determined 1010 from the image data where the condition may also refer to type of objects represented in the image data . The condition may also include a number of objects represented in the image data ; therefore, the optimizing display using trained data and machine learning based models is based on the condition of whether certain types of objects are detected /represented and the number of them which reflects the behavior of the user (certain types of objects the user would like to focus on and capture)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Maeda et al. with the teaching of Cuban et al. to utilize machine learning or artificial intelligence (AI) in order to
perform object detection to choose and select focus modes/methods to accurately perform focus control to improve image quality.
The combination of Maeda and Cuban teaches identify, using the AI/M model ( Cuban; Para 29; 38, Para 48, 49, 59, 64-6; Fig. 10 of Cuban discloses optimizing display content under various conditions.) , the camera mode or the set of parameters to be applied to the camera under the specific scene (Para 209; an eye may be detected and the focusing state of the detected eye may be detected in a case where the image capturing mode of the camera is a portrait mode, and a ball may be detected and the focusing state of the detected ball may be detected in a case where it is a sport mode. Wherein the camera mode is associated with the objects being detected);
and output an indication of the camera mode or the set of parameters to be applied to the camera under the specific scene (Maeda; Para 109; Para 209;The body-side control unit 230 selects the face 57 located on the right side of the face 55, detects the right and left eyes of the face 57, selects an eye on the left side facing the face 57 (hereinafter referred to as a left eye), and causes the display unit 290 to display an indication frame 81 (an AF frame) indicating the left eye. The AF signal processing unit 270b calculates the amount of defocus on the basis of focus points P corresponding to the indication frame 81.The focusing state is associated with the indicated detected object according to different camera modes ; wherein eye detection is associated with portrait mode).
Regarding claim 17, Maeda et al. discloses the apparatus of claim 16, wherein to output the indication, the at least one processor, is configured to:
consume the indication of the camera mode or the set of parameters ( Para 73-77; The “AF area mode” includes, for example, “single point AF” for calculating the amount of defocus using only one focus point P selected by a user's operation, “wide area AF” for calculating the amount of defocus using a plurality of focus points P having a wider range than the single point AF, “auto area AF” in which a camera detects a subject from a range where all the focus points P are disposed and the amount of defocus is calculated using focus points P located in the range of the subject, and the like; the body-side control unit 230 drives the focus lens 361 to display an indication (an AF frame) indicating one focused focus point Pa superimposed on the live-view image displayed on the display unit 290. Para 196; focus detection using the contrast method may be performed in a case where the size of the detected eye is smaller than a predetermined value, and focus detection using the phase difference detection method may be performed in a case where the size of the detected eye is larger than the predetermined value.);
store the indication of the camera mode or the set of parameters; or transmit
the indication of the camera mode or the set of parameters (Para 76; The body-side control unit 230 transmits the driving signal of the focus lens 361 to the interchangeable lens 3 so as to focus on a portion of the person corresponding to the selected focus point. In order to transmit the driving signal, the driving signal must be calculated and stored so that the information can be transmitted).
Regarding claim 20, Maeda et al. discloses wherein the plurality of scene groups includes:
a first scene group for at least one specified event (Para 93-95; the body-side control unit 230 is assumed to detect three faces 55, 56, and 57 included in the live-view image illustrated in FIG. 5, select the face 55 located closest to the center of a screen, detect the right and left eyes of the face 55, select a left eye facing the face (hereinafter referred to as a left eye) determined to be large, and cause the display unit 290 to display the indication frame 51 indicating the left eye. Wherein faces detected from images can be considered as specified event.),
Regarding claim 21, Maeda et al. discloses at least one processor (Para 31; The body-side control unit 230 is constituted by a microcomputer, its peripheral circuits, and the like. The body-side control unit 230 includes a storage unit 235 that stores a control program or the like which is executed by the body-side control unit 230) .
However, Maeda does not disclose wherein the at least one processor, is further configured to:
determine whether the AI/ML model is capable of identifying the camera mode or the set of parameters to be applied to the camera under the specific scene with a confidence level exceeding a confidence threshold; and deploy the AI/ML model for identifying the camera mode or the set of parameters to be applied to the camera under the specific scene if the AI/ML model has the confidence level exceeding the confidence threshold, or deploy an existing AI/ML model for identifying the camera mode or the set of parameters to be applied to the camera under the specific scene if the AI/ML model has the confidence level that does not exceed the confidence threshold.
Cuban et al. discloses wherein the at least one processor, is further configured to:
determine whether the AI/ML model is capable of identifying the camera mode or the set of parameters to be applied to the camera under the specific scene with a confidence level exceeding a confidence threshold (Para 50,62; After processing using a computer vision algorithm with the appropriate classifiers, libraries, or descriptors, for example, a result can be obtained that is an identification of each potential object of interest with associated confidence value(s). One or more confidence thresholds or criteria can be used to determine which objects to select as the indicated type. objects with at least a minimum confidence value that corresponding to specified types of objects may be selected as objects of interest. Wherein the object of interest determined which region to perform focusing on which indicates the camera modes ); and deploy the AI/ML model for identifying the camera mode or the set of parameters to be applied to the camera under the specific scene if the AI/ML model has the confidence level exceeding the confidence threshold, confidence value that corresponding to specified types of objects may be selected as objects of interest.) (please note that the strikethrough limitation is not being considered due to alternative wording “or).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Maeda et al. with the teaching of Cuban et al. to determine reliability of object detection performed by AI/ML model in order to reduce errors and improve focus performance and improve image quality.
Regarding claim 23, the combination of Maeda et al. and Cuban et al. teaches wherein the identified camera mode or the identified set of parameters corresponds to a highest probability among the set of camera modes (Maeda et al.; Para 174-185; Fig. 14; the body-side control unit 230 calculates a distance R from the position of the detected face to the position of detected eye on the face. FIG.15 is a schematic diagram illustrating calculation of the distance R. In FIG.15, the position of a detected face 161 is, for example, a central position (centroid position) CA in the range of the detected face 161, and the position of a detected eye 162 is, for example, a central position (centroid position) CB in the range of the detected eye 162. The distance R is a distance between the point CA and the point CB in FIG.15. In step S370A following step S345, the body-side control unit 230 determines whether the distance R is larger than a threshold determined in advance. For example, in a case where the distance R is larger than R′×k1 obtained by multiplying the length R′ of the eye in the X-axis direction (horizontal direction) by a coefficient k1, the body-side control unit 230 makes an affirmative determination in step S370A and proceeds to step S380.wherein the body-side control unit can determine whether the detected object are faces or eyes with the highest probability face or eye by calculation and comparing the distance R to a threshold).
Regarding claim 25, and claim 26, the subject matter disclosed in claims 25 and 26 are similar to the subject matter disclosed in claims 16, 17 respectively; therefore, claims 25 and 26 are rejected for the similar reasons as set forth in claims 16 and 17 respectively.
Regarding claims 29,30, the subject matter of claims 29,30 are similar to the subject matter disclosed in claims 20,21 respectively, therefore, claims 29,30 are rejected for the same reasons as set forth in claims 20, 21 respectively.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Cuban et al. (US Pub. No.: US 2019/0122082 A1), in view of Rutherford (US Pub. No.: US 2019/0122082 A1).
Regarding claim 10, Cuban et al. discloses at least one processor (Fig. 4; Para 34, 39; a primary processor 404 (e.g., at least one CPU) can be configured to execute instructions to perform various functionality discussed herein. The example content display system 400 also includes a microcontroller 406 to perform specific tasks with respect to the device) , output the indication (Para 37; The network communications components 420 can be used to transfer data to a remote system or service, where that data can include information such as count, object location, and tracking data, among other such options, as discussed herein.).
However, Cuban et al. does not disclose the apparatus of claim 1, further comprising at least one of a transceiver or an antenna coupled to the at least one processor, wherein to output the indication, the at least one processor, individually or in any combination, is configured to: output, via at least one of the transceiver or the antenna, the indication.
Rutherford discloses at least one of a transceiver or an antenna coupled to the at least one processor, the at least one processor, individually or in any combination, is configured to: output, via at least one of the transceiver or the antenna ( Para 42; the central station output device 136 of the central computer station 126 may be, for example, a transceiver to transmit the status information to an external device (e.g. the operator's smartphone or tablet), a microphone to provide status announcements or alerts, or, as in the illustrated example, a screen to display the status information) .
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cuban et al. with the teaching of Rutherford et al. to have a built-in transceiver in the device in order to communicate to external device more conveniently to record the location of certain subjects for surveillance purpose.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US Pub. No.: US 2022/0086361 A1), in view of Cuban et al. (US Pub. No.: US 2019/0122082 A1), and further in view of Sharma et al. (US Pub. No.: US 2024/0031680 A1).
Regarding claim 22, the combination of Maeda et al. and Cuban et al. does not teach wherein the AI/ML model is associated with a reinforcement learning model.
Sharma et al. discloses the AI/ML model is associated with a reinforcement learning model (Para 68; The training 402 can be supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, and/or the like, including combinations and/or multiples thereof) .
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Maeda et al. and Cuban with the teaching of Sharma et al. to update methods using feedback to improve decision making in detecting region of interest and associated camera modes in order to improve image quality with higher focus performance.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Maeda et al. (US Pub. No.: US 2022/0086361 A1), ), in view of Cuban et al. (US Pub. No.: US 2019/0122082 A1) and further in view of Rutherford (US Pub. No.: US 2022/0124238 A1).
Regarding claim 24, Maeda discloses the at least one processor, output the indication (Para 31; The body-side control unit 230 is constituted by a microcomputer, its peripheral circuits, and the like. The body-side control unit 230 includes a storage unit 235 that stores a control program or the like which is executed by the body-side control unit 230 ; Para109; Para 209; The body-side control unit 230 selects the face 57 located on the right side of the face 55, detects the right and left eyes of the face 57, selects an eye on the left side facing the face 57 (hereinafter referred to as a left eye), and causes the display unit 290 to display an indication frame 81 (an AF frame) indicating the left eye. The AF signal processing unit 270b calculates the amount of defocus on the basis of focus points P corresponding to the indication frame 81.The focusing state is associated with the indicated detected object according to different camera modes ; wherein eye detection is associated with portrait mode).
However, Maeda does not disclose the apparatus of claim 16, further comprising at least one of a transceiver or an antenna coupled to the at least one processor, wherein to output the indication, the at least one processor, individually or in any combination, is configured to: output, via at least one of the transceiver or the antenna, the indication.
Rutherford discloses at least one of a transceiver or an antenna coupled to the at least one processor, the at least one processor, individually or in any combination, is configured to: output, via at least one of the transceiver or the antenna (Para 42; the central station output device 136 of the central computer station 126 may be, for example, a transceiver to transmit the status information to an external device (e.g. the operator's smartphone or tablet), a microphone to provide status announcements or alerts, or, as in the illustrated example, a screen to display the status information).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Maeda with the teaching of Rutherford et al. to have a built-in transceiver in the device in order to communicate to external device more conveniently to record the location of certain subject and certain focus modes for machine training purpose to improve training database and improve device performance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kuo et al. (US Pub. No.: 2014/0218555 A1) disclosed an electronic device and an image selection method thereof are provided. The electronic device is configured to obtain a series of images; determine camera states corresponding to the series of images; select first images from the series of images according to the camera states; determine object states corresponding to the first images; divide the first images into a number of groups according to the object states; and select a candidate image from each of the number of groups. The image selection method is applied to the electronic device to implement the aforesaid operations.
Kohstall et al. (US Pub. No.: 2018/0359411 A1) disclosed cameras with autonomous adjustment and learning functions and associated systems and methods are disclosed. A camera in accordance with a particular embodiment includes a system that determines a parameter of each of a plurality of existing photographs, the parameter being representative of a characteristic of each existing photograph, determines a figure of merit associated with each existing photograph, the figure of merit being representative of a price, popularity, and/or reputation of each existing photograph, and correlates the figures of merit with the parameters to determine a target parameter. A camera in accordance with another embodiment can include a system that analyzes a preview image from the camera, classifies the preview image, determines a parameter of the preview image associated with the classification, compares the parameter to the target parameter, and adjusts the camera to cause the preview image to have the target parameter.
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 XI WANG whose telephone number is (469)295-9155. The examiner can normally be reached 9:00 am-5:00 pm.
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/XI WANG/Primary Examiner, Art Unit 2637
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