DETAILED ACTION
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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over HOLZER (US 20210225065 A1, which claims priority of US-Provisional-Application US 62/961826, January 16, 2020); in view of ITAKURA (US 20180098047 A1), and further in view of TATE (US 20180144447 A1).
Re Claim 1, HOLZER discloses an information processing apparatus (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]), comprising:
at least one memory that stores instructions; and
at least one processor that executes the instructions to (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and Fig. 16, and, --The device can include at least one camera, a display, an IMU, a processor (CPU), memory, microphone, audio output devices, communication interfaces, a power supply, graphic processor (GPU), graphical memory and combinations thereof. The display is shown with images at three times 1606a, 1606b and 1606c. The display can be overlaid with a touch screen.--, in [0199]):
obtain object information representing positions and shapes of a plurality of objects in an image capturing region of which image is captured from different directions by a plurality of image capturing apparatuses (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,-- object segmentation may involve identifying one or more objects that are targeted for animation. For example, one or more wheels of a vehicle may be identified separately from the rest of a vehicle so that the wheels may be animated when constructing the action shot video.--, in [0050]; and, --the modeled representation may include information such as a three-dimensional model of one or more objects included in the scene, such as one or more trees.--, in [0072]-[0075], and, --rendering an object into a scene may involve identifying a viewpoint of an object that aligns with a viewpoint of the action shot base video. For instance, the multi-view capture may include a number of images of the object each captured from a respective perspective viewpoint, while the action shot base video may include a camera that pans around a point in space….rendering an object into a scene may involve positioning the object based on the 3D scene estimated at 404. Such information may be used to position the object realistically within the scene. For example, a vehicle may be positioned on the ground and in front of or behind a tree.--, in [0077]-[0078]; and, --the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and,
--the target can be cross hairs. In general, the target can be rendered as any shape or combinations of shapes. In some embodiments, via an input interface, a user may be able to adjust a position of the target….the target can be placed over an object that appears in the image, such as a face or a person. Then, the user can provide an additional input via an interface that indicates the target is in a desired location. For example, the user can tap the touch screen proximate to the location where the target appears on the display. Then, an object in the image below the target can be selected. As another example, a microphone in the interface can be used to receive voice commands which direct a position of the target in the image (e.g., move left, move right, etc.) and then confirm when the target is in a desired location (e.g., select target)--, in [0157]-[0158];
-- the one or more points may be tracked based on other image characteristics that appear in successive frames. For instance, edge tracking, corner tracking, or shape tracking may be used to track one or more points from frame to frame.--, in [0162], and, --the shape and position of the object 1502 in the captured pixel data--, in [0186]-[0189]);
HOLZER however does not explicitly disclose obtain object information of shapes from different directions;
ITAKURA discloses obtain object information of shapes from different directions (see ITAKURA: e.g., Fig. 1, and, -- obtaining a captured image obtained by each of a plurality of image capturing apparatuses by capturing an object; setting a virtual viewpoint of a reconstruction image; estimating a shape of the object by selecting, from the plurality of image capturing apparatuses, a group of selected image capturing apparatuses to be used for estimating the shape of the object and using each captured image obtained by the group of selected image capturing apparatuses--, in [0006]-[0008]; and, -- the first area 105 and the second area 106 cover almost the entire field 108. For example, the ratio in which the sum of the first area 105 and second area 106 occupies the field 108 is 80% or more in one embodiment, 90% or more in another embodiment, 95% or more in still another embodiment, and 100% in still another embodiment. This kind of an arrangement allows at least either the first group of image capturing apparatuses 102 or the second group of image capturing apparatuses 104 to capture an image from various directions for each object present in various positions in the field 108. As a result, a reconstruction image of each object present in various positions in the field 108 can be generated, and thus improve the viewpoint flexibility.--, in [0026]; and in [0056]-[0059]);
HOLZER and ITAKURA are combinable as they are in the same field of endeavor: generate a virtual fused image from the integrated features of plurality of images. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify HOLZER’s apparatus using ITAKURA’s teachings by including to obtain object information of shapes from different directions to HOLZER’s obtain object information of positions and shapes, such as tracking shapes and locations in order to obtain object information of positions and shapes from images captured from various directions for each object present in various positions (see ITAKURA: e.g., in [0006]-[0008], [0026], and[0056]-[0059]);
HOLZER as modified by ITAKURA also discloses determine visibility of a specific object based on the obtained object information, wherein the specific object is included in the plurality of objects (see HOLZER: e.g., -- neural network may be trained to determine the coordinates of the visible object pixels in an image of the object. The neural network may facilitate the determination of the pixel coordinates and the width, height, or other characteristics of the bounding box enclosing the object--, in [0060]-[0062]; and, -- Visibility angles are determined for each vertex of the object at 510. According to various embodiments, a visibility angle indicates the range of object angles with respect to the camera for which the vertex is visible.--, in [0103]-[0104], and, -- recordings of objects, persons, or parts of objects or persons, where only the object, person, or parts of them are visible, recordings of large flat areas,--, in [0138], and, -- some tracking points identified on the real object may go out of view as new portions of the real object come into view and other portions of the real object are occluded. Thus, in 1426, a determination may be made whether a tracking point is still visible in an image.--, in [0178], and, -- if another object is moved in front of a tracked object, it may not be possible to associate the target 1704a with the object. For example, if a person moves in front of the camera, a hand is passed in front of the camera or the camera is moved so the object no longer appears in the camera field of view, then the object which is being tracked will no longer be visible. --, in [0201], also see: in [0236], and [0263]-[0268]);
HOLZER as modified by ITAKURA however does not explicitly disclose determine visibility of a specific object viewed from a specified viewpoint,
Tate teaches determine visibility of a specific object viewed from a specified viewpoint (see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0147]);
HOLZER (as modified by ITAKURA) and TATE are combinable as they are in the same field of endeavor: generate a virtual fused image from the integrated features of plurality of images. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify HOLZER (as modified by ITAKURA)’s apparatus using TATE’s teachings by including to determine visibility of a specific object viewed from a specified viewpoint to HOLZER (as modified by ITAKURA)’s determining visibility {such as the visible region and occluded region} in order to make a determination on whether or not an image is occluded (see TATE: e.g., in [0145]-[0147]);
HOLZER as modified by ITAKURA and TATE further disclose generate a virtual viewpoint image based on image data based on image capturing by the plurality of image capturing apparatuses and virtual viewpoint information indicating a position and a direction of a virtual viewpoint (see HOLZER: e.g., Fig. 4, and Fig. 13, and, --generating virtual data associated with a target using live image data--, in [0020], -- with respect to multi-view captures, to capture an action shot base video the camera may move along a path that is concave with respect to the captured imagery. For example, the camera may be moved along a 360-degree path around a point in space to allow the object to be virtually positioned at the point in space--, in [0040]; and, -- a neural network may be used to predict a 3D shape of an object from one or more perspective view images. As another example, a neural network may be used to perform space carving on an image to determine an object segmentation mask. [0054] In some implementations, a per-view texture map may be determined. Under such an approach, a viewpoint close to a virtual camera may be used for texturing a 3D model when rendering it into a new scene.
[0055] A determination is made at 210 as to whether to select an additional perspective view image for analysis. According to various embodiments, additional perspective view images may be selected until all suitable perspective view images within the multi-view capture have been analyzed. [0056] The action shot video of the object is generated at 212. Additional details regarding the generation of the action shot video are discussed with respect to the method 400 shown in FIG. 4.--, in [0053]-[0054], and [0111]-[0113]; and see ITAKURA: e.g., --0056] The viewpoint setting unit 620 performs setting of a virtual viewpoint of a reconstruction image. More specifically, the viewpoint setting unit 620 can set the three-dimensional position and the orientation (or the optical-axis direction) of the virtual viewpoint….[0057] The shape estimation unit 630 estimates the shape of the object based on each captured image obtained by the image obtaining unit 610. More specifically, the shape estimation unit 630 cuts out a desired object area from each captured image and estimates the three-dimensional position and shape of the object based on the obtained image. In this embodiment, the positional relationship of the plurality of image capturing apparatuses 509 and the plurality of image capturing apparatuses 510 is already known and has been stored in the storage unit 503 in advance. The method of estimating the three-dimensional position and shape of the object based on each captured image of the object obtained by such plurality of image capturing apparatuses 509 and such plurality of image capturing apparatuses 510.. a three-dimensional model of the object can be generated by using a stereo matching method or a volume intersection method. --, in [0056]-[0059];
also see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0147]);
and display the determined visibility of the specific object viewed from the specified viewpoint (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and, -- the one or more points may be tracked based on other image characteristics that appear in successive frames. For instance, edge tracking, corner tracking, or shape tracking may be used to track one or more points from frame to frame.--, in [0162], and, --the shape and position of the object 1502 in the captured pixel data--, in [0186]-[0189]; also see: in [0236], and [0263]-[0268]; also see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0148]).
Re Claim 2, HOLZER as modified by ITAKURA and TATE further disclose wherein the at least one processor executes the instructions to generate the virtual viewpoint image such that the determined visibility is superimposed on the virtual viewpoint image (see ITAKURA: e.g., -- by using captured images obtained by the group of selected image capturing apparatuses to estimate the shape of an object, the number of captured images used for estimation processing can be reduced. Hence, the estimation processing load can be reduced. On the other hand, since it is highly possible that an object to be included in a reconstruction image will be included in a captured image obtained by the group of selected image capturing apparatuses which was selected in accordance with the arrangement of the virtual viewpoint, the three-dimensional shape of the object can be obtained with high accuracy, and the high image quality can be maintained. Here, although a method of selecting a group of image capturing apparatuses based on the gaze point of the virtual viewpoint has been described, the present invention is not limited to this. For example, the shape estimation unit 630 may select a group of image capturing apparatuses whose gaze point is present in the field-of-view area of the virtual viewpoint or select a group of image capturing apparatuses that has a commonly viewed area which overlaps with the field-of-view area of the virtual viewpoint.--, in [0062]).
Re Claim 3, HOLZER as modified by ITAKURA and TATE further disclose wherein the at least one processor further executes the instructions to receive an operation of specifying the specific object from a user (see HOLZER: e.g., , --the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and,
--the target can be cross hairs. In general, the target can be rendered as any shape or combinations of shapes. In some embodiments, via an input interface, a user may be able to adjust a position of the target….the target can be placed over an object that appears in the image, such as a face or a person. Then, the user can provide an additional input via an interface that indicates the target is in a desired location. For example, the user can tap the touch screen proximate to the location where the target appears on the display. Then, an object in the image below the target can be selected. As another example, a microphone in the interface can be used to receive voice commands which direct a position of the target in the image (e.g., move left, move right, etc.) and then confirm when the target is in a desired location (e.g., select target)--, in [0157]-[0158]).
Re Claim 4, HOLZER as modified by ITAKURA and TATE further disclose wherein the at least one processor executes the instructions to display that the specific object is invisible in a case where the specific object is determined to be invisible due to occlusion by an object other than the specific object (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and, -- the one or more points may be tracked based on other image characteristics that appear in successive frames. For instance, edge tracking, corner tracking, or shape tracking may be used to track one or more points from frame to frame.--, in [0162], and, --the shape and position of the object 1502 in the captured pixel data--, in [0186]-[0189]; also see: in [0236], and [0263]-[0268]; also see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0148]).
Re Claim 5, HOLZER as modified by ITAKURA and TATE further disclose wherein the at least one processor executes the instructions to display that the specific object is visible in a case where the specific object is determined to be visible (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and, -- the one or more points may be tracked based on other image characteristics that appear in successive frames. For instance, edge tracking, corner tracking, or shape tracking may be used to track one or more points from frame to frame.--, in [0162], and, --the shape and position of the object 1502 in the captured pixel data--, in [0186]-[0189]; also see: in [0236], and [0263]-[0268]; also see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0148]).
Re Claim 6, HOLZER as modified by ITAKURA and TATE further disclose wherein the at least one processor executes the instructions to further display a period in which the specific object is invisible (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; and, -- the one or more points may be tracked based on other image characteristics that appear in successive frames. For instance, edge tracking, corner tracking, or shape tracking may be used to track one or more points from frame to frame.--, in [0162], and, --the shape and position of the object 1502 in the captured pixel data--, in [0186]-[0189]; also see: in [0236], and [0263]-[0268]; also see Tate: e.g., -- the image processing apparatus 10 separately includes an occluded image determination unit that makes a determination on whether or not an image is occluded. Upon integration by the feature integration unit 103, features of images determined to be occluded are removed for integration. Calculations for the integration of features at this point in time are assumed to be the operation of averaging values according to the dimension. Moreover, if the occluded image determination unit outputs the value of likelihood of occlusion, the feature integration unit 103 may integrate features not by averaging but by the weighted sum method…. [0147] As an embodiment of the occluded image determination unit here, for example, discrimination learning is performed by a discriminator such as a support vector machine, using features of NNs of each image.--, in [0145]-[0148]).
Re Claim 7, claim 7 is the corresponding method claims to claim 1 respectively. Thus, claim 7 is rejected for the similar reasons as for claim 1. Furthermore, HOLZER as modified by ITAKURA and TATE further disclose a method for processing information of performing the steps (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]).
Re Claim 8, claim 8 is the corresponding medium claims to claim 1 respectively. Thus, claim 8 is rejected for the similar reasons as for claim 1. Furthermore, HOLZER as modified by ITAKURA and TATE further disclose a non-transitory computer-readable storage medium storing a program for causing a computer to execute a method for processing information (see HOLZER: e.g., Figs. 6-8, and, –structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for processing visual data.--, in [0007], and,--the augmented reality system can be implemented on a mobile device, such as a cell phone. In particular, the live camera data, which is output to a display on the mobile device, can be augmented with virtual objects. The virtual objects can be rendered into the live camera data. In some embodiments, the virtual objects can provide a user feedback when images are being captured for a MVIDMR. [0151] FIGS. 13 and 14 illustrate an example of a process flow for capturing images in a MVIDMR using augmented reality. In 1302, live image data can be received from a camera system. For example, live image data can be received from one or more cameras on a hand-held mobile device, such as a smartphone. The image data can include pixel data captured from a camera sensor.--, in [0150]-[151]; also see Tate: e.g., --realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s).--, in [0153]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI WEN YANG whose telephone number is (571)270-5670. The examiner can normally be reached on 8:00 - 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WEI WEN YANG/Primary Examiner, Art Unit 2662
Prosecution Insights