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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/14/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claims 2 and 12 are objected to because of the following informalities:
RE claim 2, the punctuation on line 4 of claim 2 is incorrect. The end of the limitation ends in a comma. It is recommended to end the limitation in a semicolon. Semicolons are used to separate complex items, in the case, the limitation of identifying a first and second object and the limitation of obtaining a third and fourth feature.
RE claim 12, the punctuation on line 3 of claim 12 is incorrect. The end of the limitation ends in a comma. It is recommended to end the limitation in a semicolon for the same reasons as stated above in regards to claim 2.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 8, 10-12, 17 are rejected under 35 U.S.C. 103 as being unpatentable over BENDALE et al. (US 20210201549 A1) in view of LEE et al. (US 2019/0266775 A1).
RE claim 1, Bendale teaches generating a digital avatar. Bendale teaches an electronic apparatus (see Fig. 15 [0119-0122]), comprising:
(a)
one or more processors; and
Fig. 13, computer system (1300) includes processor (1302) [0062].
(b)
at least one memory storing instructions that, when executed by the one or more processors, cause the electronic apparatus to:
Fig. 13, computer system (1300) includes memory (1304) and storage (1306) [0062]. Data in the data caches may be copies of data in memory (1304, 1306) for instructions executing at processor (1302) [0063].
(c)
obtain a plurality of image frames in a user-captured image stored in the at least one memory;
The digital humas/avatars (DH) may be created from statistical data recorded from real humans [0036]. The video (said plurality of image frames) input may be an incoming video feed. The video feed may be from recorded data [0038]. It is implied that recorded data is data that would be stored for later retrieval (said stored in the at least one memory).
(d)
identify an object which comprises motion information based on the plurality of image frames;
Bendale teaches the computer-based vision algorithms and functions (1410) may include algorithms or functions that may be suitable for automatically extracting information from images (e.g., video image) [0077]. The computer-based vision algorithms and functions (1410) may include image recognition algorithms (1434) and machine vision algorithms (1436). Image recognition algorithms (1434) may include algorithms that may be suitable for automatically identifying and/or classifying objects, places, people, etc., in one or more image frames or other displayed data [0077]. The machine vision algorithms (1436) may include algorithms that may be suitable for allowing computers to “see”, or, to rely on image sensors cameras with specialized optics to acquire images for processing, analyzing, and/or measuring various data characteristics for decision making purposes [0077]. Although Bendale does not specifically teach the algorithms (1434, 1436) as identifying motion, one may interpret the teachings as the image recognition algorithm (1434) capable of identifying an object and further the machine vision algorithm(1436) as identifying motion.
For competition, Lee teaches obtaining model information related to motion [abstract]. A 3D avatar can be created that provides expression corresponding to the user’s facial expressions using the plurality of images received from a camera module [0065]. Camera module (180) may capture moving images [0044]. The camera (210) may capture at least part of the user’s body, such as the face [0060-0061]. The camera may capture a plurality images, such as a first image and a second image, where the images include a plurality of feature points [0061]. The processor (220) may determine different and a plurality of feature points [0063]. Motion is determined based on the degree of variation of the plurality of feature points between the images (said comprises motion information) [0069-0070].
It would have been obvious before the effective filing date of the claimed invention to include the motion information of Lee with the avatar modeling of Bendale because the motion information ensures the avatar provides the corresponding expression (i.e., motion) as the user [Lee:0074]. This can ensure the output of the avatar has similar facial features as the user providing a realistic digital human [Lee: 0074].
(e)
obtain first feature information of the object from a first image frame from among the plurality of image frames;
The session (204) may capture sensing data from sensors capturing the user’s face (said plurality of image frames) [0038]. The digital human profile (224) may include user preferences about behavior, appearance, voice and other characteristics (said first feature information) of the digital human (DH) [0039].
(f)
obtain a first avatar image corresponding to the object based on the first feature information;
Fig. 8, one or more received inputs (504) may be fed into the modality relationship model for digital human (512) to generate a video of a digital human (804) [0053]. Fig. 5, input data (502) may comprise video input (504a) (said plurality of image frames), audio input (504b), text input (504c), and expressions input (504d) [0050]. The input data (502) may be fed into a plurality of machine learning models (506), which in turn are fed into joint modality relationship learning (508) and modality independent learning (510). The outputs are used to generate a modality relationship model for a digital human (512) which may be used to generate a digital human [0050]. Fig. 10 teaches generating a video of a digital avatar [0055]. A video comprises a plurality of image frames. Therefore, multiple avatar images would be generated with their corresponding characteristics (said first avatar image). Additionally, Fig. 11 discusses different semantic contexts that can be applied to the avatar that includes one or more expressions, behaviors, and actions (said based on first feature information) [0056].
(g)
obtain second feature information of the object from a second image frame from among the plurality of image frames;
The rationale of claim 1(c) teaches the recorded video feed (said plurality of image frames) [0036]. A video would contain motion of a user captured with a starting frame and an ending frame. Additionally, the rationale of claim 1(e) discusses capturing the user’s face which in turn generates the digital human profile (224). The digital human profile (224) includes user preferences about behavior, appearance, voice and other characteristics (said feature information) of the digital human (DH) [0039]. Therefore, the video, i.e., the plurality of frames, would contain different characteristics (said second feature information) of the user since it is assumed the user is animated while being captured.
(h)
obtain a second avatar image corresponding to the object based on the second feature information; and
As discussed in the rationale of claim 1(g), Fig. 5 discusses the flow of generating a digital human from the input data (i.e., video feed) [0050]. Fig. 10 teaches generating a video of a digital avatar [0055]. A video comprises a plurality of image frames. Therefore, multiple avatar images would be generated with their corresponding characteristics (said second avatar image). Additionally, Fig. 11 discusses different semantic contexts that can be applied to the avatar that includes one or more expressions, behaviors, and actions (said based on second feature information) [0056].
obtain a virtual space image based on the first avatar image and the second avatar image.
Bendale teaches the one or more computing systems may send instructions to the client device (e.g., digital display) to present the video output of the digital avatar performing the sequence of actions (said virtual space image) [0034]. Thus, the video output would contain a sequence of image frames, i.e., a sequence of avatar images. Therefore, the display would render the claimed first avatar image and second avatar image as discussed in the rationale of claims 1(f) and 1(h).
RE claim 2, Bendale in view of Lee teaches wherein the one or more processors are configured to execute the instructions to cause the electronic apparatus to:
(a)
identify, based on the plurality of image frames,
(i)
a first object comprising first motion information and
Bendale teaches the digital humans may be created by capturing data from multiple individuals (said first object) [0028]. For instance, video from multiple individuals may be used to create the digital humans [0028].
In further view of Lee, Lee is relied upon as teaching motion information. Lee teaches obtaining a first image using the camera (210) [0066]. An avatar is created using a plurality of feature points on the user’s face included in the first image [0066]. It is determined whether a variation is made to the plurality of feature points included in the second image with respect to the first image (i.e., comparison) [0068]. A weight is determined for a plurality of reference models to represent a designated motion (e.g., an expression) (said first motion information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image [0069]. The variation in motion of the avatar corresponding to a variation in the user’s motion using the plurality of weight-reflected reference models is determined [0070].
(ii)
a second object comprising second motion information,
The limitation of claim 2(a)(ii) recites similar limitations as claim 2(a)(i) but in regards to a second object. Bendale teaches the digital humans may be created by capturing data from multiple individuals (said second object) [0028]. For instance, video from multiple individuals may be used to create the digital humans [0028]. Thus, the teachings in view of Lee of the rationale of claim 2(a)(i) could be applied when multiple individuals of Bendale are involved. Thus, the system of Lee would determine a weight a plurality of reference models to represent a designated motion (e.g., an expression) (said second motion information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image [0069].
(b)
obtain, from the first image frame,
(i)
third feature information of the first object based on a first extraction method and
In further view of Lee, Lee teaches tracking a plurality of feature points and obtain position information of each feature point to represent the facial features in 3D based on the tracked feature points [0073]. Lee teaches features can be the position and size of the eyes, ears, nose and mouth, the position and size of the eyebrows, the degree of darkness of the eyebrows, the position of wrinkles, inter-eyebrow distance, the position and size of the chin, the position and size of the upper chines, the position and size of the forehead, the contour and size of the face, skin color and tone, hair color and position, and/or the position and size of scars based on the plurality of feature points [0063]. Therefore, any of these features can be considered the claim third feature information that is different than the initial feature being extracted.
(ii)
fourth feature information of the second object based on a second extraction method;
As discussed in the rationale of claim 2(a)(ii), Bendale teaches a plurality of individuals (said second object). Additionally, the teachings of Lee can be applied to the plurality of individuals of Bendale. Therefore, as taught in the rationale of claim 2(b)(i), the tracking of the different features can be extended to the claimed fourth feature information for a second object.
(c)
obtain, from the second image frame,
(i)
fifth feature information of the first object based on the first extraction method and
In further view of Lee, Lee teaches the plurality of images may include a first image and a second image. The second image may also include a plurality of feature points included in the user’s face [0061]. Lee teaches determining the feature points in the second image in order to compare the variation with the first image to indicate motion [0066-0068]. Therefore, a comparison is made between the same features for the first image as in the second image (said fifth feature information).
(ii)
sixth feature information of the second object based on the second extraction method; and
As discussed in the rationales of claims 2(a)(ii) and 2(b)(ii), Bendale teaches multiple individuals (said second object). As in the rationale of claim 2(c)(i), Lee teaches the plurality of images may include a first image and a second image. The second image may also include a plurality of feature points included in the user’s face [0061]. Lee teaches determining the feature points in the second image in order to compare the variation with the first image to indicate motion [0066-0068]. Therefore, a comparison is made between the same features for the first image as in the second image (said sixth feature information).
(d)
obtain a third avatar image corresponding to the first object based on the third feature information and the fifth feature information, and
In further view of Lee, Lee teaches determining a weight for a plurality of reference models to represent a designated motion (e.g., an expression) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image (said fifth feature information) as compared to the first (said third feature information) [0069]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) (said obtain a third avatar image) [0070].
(e)
obtain a fourth avatar image corresponding to the second object based on the fourth feature information and the sixth feature information.
As discussed in the rationales of claims 2(a)(ii)-2(c)(ii), Bendale teaches multiple individuals (said second object). As in the rationale of claim 2(d)(i), Lee teaches determining a weight for a plurality of reference models to represent a designated motion (e.g., an expression) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image (said sixth feature information) as compared to the first (said fourth feature information) [0069]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) (said obtain a fourth avatar image) [0070].
The same motivation to combine as taught in the rationale of claim 1 is incorporated herein.
RE claim 3, Bendale in view of Lee teaches wherein the one or more processors are configured to execute the instructions to cause the electronic apparatus to:
(a)
obtain from the first image frame,
(i)
based on a first object comprising first motion information and
Bendale teaches the digital humans may be created by capturing data from multiple individuals (said first object) [0028]. For instance, video from multiple individuals may be used to create the digital humans [0028].
In further view of Lee, Lee is relied upon as teaching motion information. Lee teaches obtaining a first image using the camera (210) [0066]. An avatar is created using a plurality of feature points on the user’s face included in the first image [0066]. It is determined whether a variation is made to the plurality of feature points included in the second image with respect to the first image (i.e., comparison) [0068]. A weight is determined for a plurality of reference models to represent a designated motion (e.g., an expression) (said first motion information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image [0069]. The variation in motion of the avatar corresponding to a variation in the user’s motion using the plurality of weight-reflected reference models is determined [0070].
(ii)
a second object comprising second motion information being identified based on the plurality of image frames,
The limitation of claim 3(a)(ii) recites similar limitations as claim 3(a)(i) but in regards to a second object. Bendale teaches the digital humans may be created by capturing data from multiple individuals (said second object) [0028]. For instance, video from multiple individuals may be used to create the digital humans [0028]. Thus, the teachings in view of Lee of the rationale of claim 2(a)(i) could be applied when multiple individuals of Bendale are involved. Thus, the system of Lee would determine a weight a plurality of reference models to represent a designated motion (e.g., an expression) (said second motion information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image [0069].
(iii)
third feature information of the first object and
In further view of Lee, Lee teaches tracking a plurality of feature points and obtain position information of each feature point to represent the facial features in 3D based on the tracked feature points [0073]. Lee teaches features can be the position and size of the eyes, ears, nose and mouth, the position and size of the eyebrows, the degree of darkness of the eyebrows, the position of wrinkles, inter-eyebrow distance, the position and size of the chin, the position and size of the upper chines, the position and size of the forehead, the contour and size of the face, skin color and tone, hair color and position, and/or the position and size of scars based on the plurality of feature points [0063]. Therefore, any of these features can be considered the claim third feature information that is different than the initial feature being extracted.
(iv)
fourth feature information of the second object;
As discussed in the rationale of claim 3(a)(ii), Bendale teaches a plurality of individuals (said second object). Additionally, the teachings of Lee can be applied to the plurality of individuals of Bendale. Therefore, the rationale of claim 3(a)(iii) teaches determining additional feature information where the tracking of the different features can be extended to the claimed fourth feature information for a second object.
(b)
obtain from the second image frame,
(i)
fifth feature information of the first object and
In further view of Lee, Lee teaches the plurality of images may include a first image and a second image. The second image may also include a plurality of feature points included in the user’s face [0061]. Lee teaches determining the feature points in the second image in order to compare the variation with the first image to indicate motion [0066-0068]. Therefore, a comparison is made between the same features for the first image as in the second image (said fifth feature information).
(ii)
sixth feature information of the second object;
As discussed in the rationales of claims 3(a)(ii), Bendale teaches multiple individuals (said second object). As in the rationale of claim 3(b)(i), Lee teaches the plurality of images may include a first image and a second image. The second image may also include a plurality of feature points included in the user’s face [0061]. Lee teaches determining the feature points in the second image in order to compare the variation with the first image to indicate motion [0066-0068]. Therefore, a comparison is made between the same features for the first image as in the second image (said sixth feature information).
(c)
obtain a third avatar image corresponding to the third feature information and the fifth feature information based on first motion generating information, and
In further view of Lee, Lee teaches determining a weight for a plurality of reference models to represent a designated motion (e.g., an expression) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image (said fifth feature information) as compared to the first (said third feature information) [0069]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) (said first motion generating information) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) (said obtain a third avatar image) [0070].
(d)
obtain a fourth avatar image corresponding to the fourth feature information and the sixth feature information based on second motion generating information.
As discussed in the rationales of claims 3(a)(ii), Bendale teaches multiple individuals (said second object). As in the rationale of claim 2(c), Lee teaches determining a weight for a plurality of reference models to represent a designated motion (e.g., an expression) (said second motion generating information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image (said sixth feature information) as compared to the first (said fourth feature information) [0069]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) (said obtain a fourth avatar image) [0070].
The same motivation to combine as taught in the rationale of claim 1 is incorporated herein.
RE claim 8, Bendale teaches
(a)
further comprising a display,
Bendale teaches the one or more computing system may send instructions to present the video output to a client device. A user may interface the one or more computing systems at a client devices. The user may be interfacing the one or more computing systems through a digital display [0034].
(b)
wherein the one or more processors are configured to execute the instructions to cause the electronic apparatus to control the display to display the virtual space image.
Bendale teaches the one or more computing system may send instructions to present the video output to a client device [0034]. After the one or more computing systems generates the video output, the one or more computing system may send instructions to the client device (e.g., digital display) to present the video output of the digital avatar performing the sequence of actions [0034].
RE claim 10, claim 10 recites similar limitations as claim 1 but in process form. Therefore, the same rationale used for claim 1 is applied.
RE claim 11, claim 11 recites similar limitations as claim 2 but in process form. Therefore, the same rationale used for claim 2 is applied.
RE claim 12, claim 12 recites similar limitations as claim 3 but in process form. Therefore, the same rationale used for claim 3 is applied.
RE claim 17, claim 17 recites similar limitations as claim 8 but in process form. Therefore, the same rationale used for claim 8 is applied.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over BENDALE et al. (US 20210201549 A1) in view of LEE et al. (US 2019/0266775 A1) as applied to claim 3 and 12 respectively, and in further view of WEDIG et al. (US 2019/0362529 A1).
RE claim 4, Bendale in view of Lee teaches motion information in regards to the generated avatars. However, the prior art does not discuss motion constrain information. Wedig is made of record as teaching the art of determining skinning parameters using an optimization process subject to constraints based on human-understandable or anatomically-motivated relationships among skeletal joints [abstract]. Wedig teaches wherein
(a)
the first motion generating information comprises first motion constraint information,
In further view of Wedig, Wedig teaches a virtual avatar may be a virtual representation of a real or fictional person (or creature or personified object) in an AR/VR/MR environment [0027]. Wedig teaches to animate a virtual character where its mesh can be deformed by moving some or all of its vertices to new positions in space at various instants in time [0127]. Skinning refers to the process of actually deforming the mesh, using the assigned weights based on transforms applied to the joints in a skeletal system [0139]. A constraint is typically a system where a particular object or joint transform controls one or more components of a transform applied to another joint or object [0147]. Wedig lists the different types of constraints that may be applied [0189-0203]. An example is bone-like behaviors (said first motion constraint) [0191,].
(b)
the second motion generating information comprises second motion constraint information, and
In continuation of the rationale of claim 4(a), an additional type of constraint can be a smoothness constraint that aids in smoother skin deformations (said second motion constraint) [0193].
(c)
the first motion constraint information is different from the second motion constraint information.
As taught in the rationale of claims 4(a) and 4(b), in further view of Wedig, different constraints can be applied in regards to the animation of the avatar, such as smoothness constraint and bone-like behaviors (said first and second different).
It would have been obvious before the effective filing date of the claimed invention to include the constraints of Wedig with the avatar animation of Bendale in view of Lee because applying joint constraints during the decomposition process can be advantageous and lead to more biometrically reasonable joint decompositions [Wedig: 0194].
RE claim 13, claim 13 recites similar limitations as claim 4 but in process form. Therefore, the same rationale used for claim 4 is applied.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over BENDALE et al. (US 20210201549 A1) in view of LEE et al. (US 2019/0266775 A1) as applied to claims 1 and 10 respectively, and in further view of HOLZER et al. (US 2018/0225517 A1).
RE claim 9, Bendale in view of Lee teaches wherein the one or more processors are configured to execute the instructions to cause the electronic apparatus to:
(a)
obtain a first avatar motion image corresponding to the object based on the first feature information;
As taught in the rationale of claim 1(f), Fig. 10 of Bendale teaches generating a video of a digital avatar [0055]. A video comprises a plurality of image frames. Therefore, multiple avatar images would be generated with their corresponding characteristics (said first avatar image). Additionally, Fig. 11 discusses different semantic contexts that can be applied to the avatar that includes one or more expressions, behaviors, and actions (said based on first feature information) [0056].
As modified by Lee, Lee is relied upon as teaching motion information. Lee teaches obtaining a first image using the camera (210) [0066]. An avatar is created using a plurality of feature points on the user’s face included in the first image [0066]. It is determined whether a variation is made to the plurality of feature points included in the second image with respect to the first image (i.e., comparison) [0068]. A weight is determined for a plurality of reference models to represent a designated motion (e.g., an expression) (said first motion information) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image [0069]. The variation in motion of the avatar corresponding to a variation in the user’s motion using the plurality of weight-reflected reference models is determined [0070]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) (said obtain a first avatar motion image) [0070]. Therefore, each frame of the video would be an avatar motion image of the object with a certain expression related to that specific frame (said motion, based on second feature information).
(b)
obtain a second avatar motion image corresponding to the object based on the second feature information;
As discussed in the rationale of claim 1(h), Fig. 5 discusses the flow of generating a digital human from the input data (i.e., video feed) [0050]. Fig. 10 teaches generating a video of a digital avatar [0055]. A video comprises a plurality of image frames. Therefore, multiple avatar images in relation to the video frames would be generated with their corresponding characteristics during that video frame (said second avatar image). Additionally, Fig. 11 discusses different semantic contexts that can be applied to the avatar that includes one or more expressions, behaviors, and actions (said based on second feature information) [0056].
As modified by Lee, Lee is relied upon as teaching motion information. Lee teaches obtaining a first image using the camera (210) [0066]. Lee teaches the plurality of images may include a first image and a second image. The second image may also include a plurality of feature points included in the user’s face [0061]. Lee teaches determining a weight for a plurality of reference models to represent a designated motion (e.g., an expression) based on the degree of variation (or the degree of motion) in the plurality of feature points in the second image as compared to the first [0069]. The processor (220) may create an avatar in which at least one of the plurality of feature points have been varied (e.g., an avatar that has changed its expression) by combining the plurality of weight-reflected reference models. The processor (220) may provide the motion-varied (e.g., expression-varied) avatar through the display (260) [0070]. Therefore, each frame of the video would be an avatar motion image of the object with a certain expression related to that specific frame (said motion, based on second feature information).
Bendale teaches the one or more computing systems may send instructions to the client device (e.g., digital display) to present the video output of the digital avatar performing the sequence of actions (said virtual space image) [0034]. However, Bendale in view of Lee fail to teach applying temporal filtering to the first and second avatar motion image. Holzer is made of record as teaching:
(c)
obtain the virtual space image by applying temporal filtering to the first avatar motion image and the second avatar motion image.
Holzer teaches skeleton detection and tracking of people or other objects in real-time during capturing images [0025]. Holzer teaches using skeleton detection to aid in pose detection [0053]. Holzer teaches a temporal filtering method may be applied to remove spurious detections [0053].
It would have been obvious before the effective filing date of the claimed invention to apply the temporal filtering of Holzer with the avatar motion images of Bendale in view of Lee because it can remove spurious detections as taught by Holzer [0053].
RE claim 18, claim 18 recites similar limitations as claim 9 but in process form. Therefore, the same rationale used for claim 9 is applied.
Allowable Subject Matter
Claims 5-7 and 14-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Bendale teaches the computer-based vision algorithms and functions (1410) may include algorithms or functions that may be suitable for automatically extracting information from images (e.g., video image) [0077]. The computer-based vision algorithms and functions (1410) may include image recognition algorithms (1434) and machine vision algorithms (1436). Image recognition algorithms (1434) may include algorithms that may be suitable for automatically identifying and/or classifying objects, places, people, etc., in one or more image frames or other displayed data [0077]. The machine vision algorithms (1436) may include algorithms that may be suitable for allowing computers to “see”, or, to rely on image sensors cameras with specialized optics to acquire images for processing, analyzing, and/or measuring various data characteristics for decision making purposes [0077]. Although Bendale does not specifically teach the algorithms (1434, 1436) as identifying motion, one may interpret the teachings as the image recognition algorithm (1434) capable of identifying an object and further the machine vision algorithm(1436) as identifying motion.
Lee teaches obtaining model information related to motion [abstract]. A 3D avatar can be created that provides expression corresponding to the user’s facial expressions using the plurality of images received from a camera module [0065]. Camera module (180) may capture moving images [0044]. The camera (210) may capture at least part of the user’s body, such as the face [0060-0061]. The camera may capture a plurality images, such as a first image and a second image, where the images include a plurality of feature points [0061]. The processor (220) may determine different and a plurality of feature points [0063]. Motion is determined based on the degree of variation of the plurality of feature points between the images [0069-0070].
However, the cited prior art does not disclose or render obvious the combination of elements recited in the claims as whole. Specifically, the cited prior art fails to disclose or render obvious the limitations: identifying a motion type of the object based on feature information and further identifying a pre-defined avatar motion information corresponding to the motion.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE L SAMS:
direct telephone number:
(571) 272-7661
email:
michelle.sams@uspto.gov
personal fax number:
(571)273-7661
The examiner is currently part time and can be reached Mon.-Fri. 5:30am-9:30am.
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/MICHELLE L SAMS/
Primary Examiner, Art Unit 2611
17 July 2026
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