Detailed Action
1. Claims 23-41 are pending in this Application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
3. Claims 23-28, 30, 32-37,39 and 41are rejected under 35 U.S.C. 103(a) as being unpatentable over Hartman et al., (hereafter Hartman ), US-20220012968, pub., 01/13/2022 ,in view of Gafni et al., (hereafter Gafni), US 11373352 B1, filed on 03/04/2021
As to claim 23, Hartman teaches A method for determining intent to open a door, the method being performed by an intent determiner (Abstract, An access control system is described for unlocking a door as an individual approaches the door based on the individual's intention to open the door), the method comprising:
obtaining an image of a physical space near the door (see Fig. 1, camera 32; Fig. 2: step 102, [0048], claim 11, A method for controlling access to an area or compartment comprising: acquiring a sequence of images of a scene in a vicinity of an ingress to the area or compartment;);
determining a position and orientation of a person in the image by providing the image to an image machine learning model, wherein the image machine learning model is configured to determine position and orientation of a person in the image (claims 15-16, the method further comprising the step of computing body motion, head orientation, and subject distance from the ingress based on said body features, wherein the step of determining a level of intent is performed with a trained neural network for extracting an intent vector, and the level of intent is based on said body features, said computed body motion, head
orientation, subject distance from the ingress, and intent vector.),
adding a data item, comprising an indicator of the position and an indicator of the orientation, to a data structure ([0074], the body features are sent to memory 340, and a central processor 350 or another processer is operable to compute various characteristics relating to the individual based on the body features including, head
orientation, distance to the subject, and body motion. );
determining based on the data structure, whether there is intent of the person to open the door ([0074], [0114], When an enrolled face for a particular door is found to be real with the intent to open the door, the computer system will send a signal, for example, over GPIO (general purpose input and output) to the control device 1000 to unlock the door so that the individual may open the door. ); and
repeating the method until an exit condition is true (see Fig. 2, see e.g. block 132, see also ([0074]: intent classifier).
It is noted that Hartman does not specifically teach “wherein the image machine learning model is configured to determine a stick figure of the person based on the image”
On the other hand Gafni teaches wherein the image machine learning model is configured to determine a stick figure of the person based on the image(claim 3, , wherein the keypoint pose comprises keypoints of the first person in the first pose represented by a stick figure, wherein the keypoint pose is generated by processing the first image using a pre-trained machine-learning model, and wherein the stick figure in the keypoint pose further comprises facial and hand landmarks)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate method of applying stick figures for keypoint pose estimation taught by Gafni into Hartman.
The suggestion/motivation for doing so would have been using stick figures in image processing offers major computational and analytical advantages over processing full silhouettes or raw pixel data.
As to claim 24, Hartman teaches wherein in the step of adding the data item, the data item comprises coordinates of anatomical features (Fig.4, [0109], [0072], FIG. 4 details an intention detection system in accordance with an embodiment of the invention. Initially, several image frames from camera 310 are obtained and placed in buffer 320. Data from a depth sensor may also be obtained and used or fed into the intention classifier. The depth/angle information of the face arising from the reflected light is also used to compute a more unique signature of a real face vs. a personation attack),
It is noted that Hartman does not specifically teach “anatomical features represented by the stick figure”
On the other hand Gafni teaches anatomical features represented by the stick figure (col. 1 lines 65 - col.2 line 15, The stick figure in the keypoint pose may further comprise facial and hand landmarks. The dense pose may comprise two channels mapping sampling points of the first person's body to UV coordinates and a body index channel, ).
As to claim 25, Gafni teaches in the step of adding the data item, the data item comprises an orientation of at least one anatomical feature represented by the stick figure(Fig.1 units 100, 120 and 150). Regarding the motivation statement, the motivation statement applied into claim 23 above equally apply in claim 25
As to claim 26, Hartman teaches adding the data item comprises adding the data item while preserving its position in a sequence in relation to any preceding data items in the data structure (Fig.4 [0072], FIG. 4 details an intention detection system in accordance with an embodiment of the invention. Initially, several image frames from camera 310 are obtained and placed in buffer 320. Data from a depth sensor may also be obtained and used or fed into the intention classifier, discussed herein).
As to claim 27, Hartman teaches the exit condition is true when the intent determiner determines that there is intent of the person to open the door, and wherein the method further comprises sending a signal to proceed with a door opening process (Fig.2 step 180, claims 5-6,, [0069],. at least one processor is operable to compute a level of authenticity of the subject, and prohibit activating the device based on the computed level of authenticity. If the level of authenticity is deemed adequate at step 132, the method proceeds to step 180 to open/unlock. This step activates the access control device 247 based on, collectively, the level of intent, the level of confidence, and the presentation attack screening.)
As to claim 28, Hartman teaches the exit condition is true when the person is no longer determined to be in the image(Fig.2 steps 150 and 160, [0054]-[0055], Step 160 states to determine whether a tracking ID exists for the candidate. If a tracking ID does not exist (e.g., the candidate is new), the process simply proceeds to step 154. The state of the system remains at ‘scan’, and the live stream of images (step 102) is interrogated for a person. If, at step 160, a tracking ID exists for the candidate (e.g., the candidate was being tracked but has fallen or leaned over to pick up a belonging), then the method proceeds to step 150. In embodiments, step 150 stops the current tracking ID, and resets the timer)
As to claim 30, Hartman teaches determining the position and orientation comprises determining a direction of the person in the image, indicating a direction of a torso of the person, and wherein the direction of the person in the image is the indicator of the orientation ([0016], [0019], generate a body feature vector for the human torso based on the sequence of images; compute body pose orientation, face pose orientation, and face size based on the body feature vector; and using an intention classifier trained to accept the body feature vector, body pose orientation, face pose orientation, and face size, determine the level of intent based on output from the intention classifier for the subject)
As to claim 32, Hartman teaches An intent determiner for determining intent to open a door, the intent determiner comprising: a processor; and a memory storing instructions that, when executed by the processor(claims 1 and 19, [0108]);regarding remaining limitations of claim 32, all the remaining limitations are rejected the same as claims 23.
Claim 33 is rejected the same as claim 24 except claim 33 is directed to a system claim . Thus, argument analogous to that presented above for claim 24 is also applicable to claim 33.
Claim 34 is rejected the same as claim 25 except claim 34 is directed to a system claim. Thus, argument analogous to that presented above for claim 25 is also applicable to claim 34.
Claim 35 is rejected the same as claim 26 except claim 35 is directed to a system claim. Thus, argument analogous to that presented above for claim 26 is also applicable to claim 35.
Claim 36 is rejected the same as claim 27 except claim 36 is directed to a system claim. Thus, argument analogous to that presented above for claim 27 is also applicable to claim 36.
Claim 37 is rejected the same as claim 28 except claim 37 is directed to a system claim. Thus, argument analogous to that presented above for claim 28 is also applicable to claim 37.
Claim 39 is rejected the same as claim 30 except claim 39 is directed to a system claim. Thus, argument analogous to that presented above for claim 30 is also applicable to claim 39.
As to claim 41, Hartman teaches A non-transitory computer readable medium storing a computer program for determining intent to open a door, the computer program comprising computer program code which, when executed on an intent determiner, causes the intent determine (claims 1 and 19, [0108]);regarding the remaining limitations of claim 41, all the remaining limitations are rejected the same as claims 23.
4. Claims 29 and 38 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hartman , US-20220012968, in view of Gafni, US 11373352 B, further in view of Barralon Pierre (hereafter Barralon), US-20160128642-A1, pub.05/12/2016
As to claim 29, Hartman teaches determining the position and orientation (claims 15-16, this limitation discussed in claim 23 above).
However, it is noted that modified Hartman does not specifically teach “determining a center point of the person in the image, and wherein the center point is the indicator of the position”
On the other hand Barralon teaches determining a center point of the person in the image, and wherein the center point is the indicator of the position([017], Preferably, the stage of estimating the position and/or orientation of the body of the person is done as follows: extracting a point of interest from the at least one image; estimating the relative position
of the sensor with respect to the visual pattern and the orientation of the sensor; and estimating the center of mass of the person from the relative position and orientation of the sensor).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a well-known method of calculating a center point of the person in the image taught by Barralon into modified Hartman.
The suggestion/motivation for doing so would have been allows user of modified Hartman to simplifies detection and tracking a of a person by reducing complex human shapes into single, mathematically simple coordinates .
Claim 38 is rejected the same as claim 29 except claim 38 is directed to a system claim . Thus, argument analogous to that presented above for claim 29 is also applicable to claim 38.
5. Claims 31and 40 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hartman, US-20220012968, in view of Gafni, US 11373352 B1, further in view of Geva et al., (hereafter Geva), US 20200349723 A1, pub. 11/05/2020
As to claim 31, Hartman teaches adding the data item ([0074], this limitation discussed in claim 23 above).
However, it is noted that modified Hartman does not specifically teach “adding an indicator of time to the data structure, in association with the data item”
On the other hand Geva teaches adding an indicator of time to the data structure, in association with the data item (claim 8, the method of claim 7, wherein the method further comprises synchronizing the two or more image frames to the sensor data, and wherein a time indicator of the sensor data corresponds to a time indicator of the two or more
image frames and/or the frame window).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the time indicator associated to two or more image frames taught by Geva into modified Hartman.
The suggestion/motivation for doing so would have been allows user of modified Hartman to identify specific moments, which is vital for forensic analysis such as security footage review.
Claim 40 is rejected the same as claim 31 except claim 40 is directed to a system claim. Thus, argument analogous to that presented above for claim 31 is also applicable to claim 40.
Prior art not used in rejections but pertinent to the claims or disclosure.
“SYSTEMS AND METHODS FOR PERSONALIZED INTENT PREDICTION.” US 20210358250A1, pub.11/18/2021, to VENETIANER. et al., disclosed:
Systems for personalized intent prediction may perform a process including receiving image data depicting the behavior of a person on approach toward a secure location, generating intent data including data representing a current trajectory of the person on the current approach, comparing the intent data with a personal statistical model for the person that includes data representing trajectories associated with historical approaches by the person toward the secure location and respective result data indicating whether the historical approach resulted in the person entering the secure location, determining, dependent on the comparing, a personalized intent score representing a likelihood that the person intends to enter the secure location on the current approach, and pre-emptively enabling, or refraining from pre-emptively enabling, entry to the secure location prior to the person reaching the secure location based on whether the personalized intent score meets or exceeds a predetermined minimum confidence threshold for enabling entry (see Abstract).
Contact Information
Any inquiry concerning this communication or earlier communication from the examiner should be directed to Mekonen Bekele whose telephone number is (469) 295-9077.The examiner can normally be reached on Monday-Friday from 9:00AM to 6:50 PM Eastern Time.
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/MEKONEN T BEKELE/Primary Examiner, Art Unit 2699
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