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 communication is responsive to the correspondence filled on 07/27/2026.
Claims 1-7 are presented for examination.
Response to Arguments
Applicant's arguments filed 07/27/2026 with respect to claims 1-7 have been considered but are moot in view of the new ground(s) of rejection.
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 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khadloya (U.S. Pub. No. 20190258866 A1), in view of Mirza (U.S. Pub. No. 20210125352 A1).
Regarding to claim 1:
1. Khadloya teach a position detection system comprising: (Khadloya [0030] location information about the first human being. [0060] In an example, filters and deformable part-based models or algorithms can be used together to model a human appearance more accurately and in a more robust manner. Various examples of the filters can include HoG or HoG-like filters. Models can be trained by a latent SVM formulation where latent variables usually specify an object of interest (e.g., a human in this case), such as including relative geometric positions of parts of a human.) a plurality of cameras each configured to capture an image corresponding to a respective field of view area; (Khadloya [0034] Aspect 25 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 21 through 24 to optionally include or use a second camera configured to receive a series of images of a second environment, wherein each of the images is a different frame acquired at a different time, and wherein the image processor circuit is configured to determine whether the first human being is present in or absent from the second environment based on information from the second camera. [0035] Aspect 26 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 21 through 25 to optionally include or use a second camera configured to receive a series of images of a second environment, wherein each of the images is a different frame acquired at a different time, and a second processor circuit configured to receive information from the memory circuit about the first human being and receive information about one or more other human beings detected in images from the second camera, and generate a dashboard for presenting to a user information about the first human being together with information about the one or more other human beings. [0062] FIG. 1 the cameras 102b can have respective fixed fields of view or can be movable. In an example, at least one of the cameras 102b includes a camera with a 180 degree view sensor and the camera is mounted on a ceiling or wall. Images acquired by such a camera can be de-warped such as prior to further processing. Other 180 degree view or more limited field of view sensors can similarly be used.) a plurality of processors (Khadloya [0014] Aspect 5 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 1 through 4 to optionally include or use the first processor circuit being a processor circuit that is housed together with the camera. Plurality f camera has plurality processor) each configured to detect a position of an object in field images generated by a respective one of the plurality of cameras (Khadloya [0034] Aspect 25 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 21 through 24 to optionally include or use a second camera configured to receive a series of images of a second environment, wherein each of the images is a different frame acquired at a different time, and wherein the image processor circuit is configured to determine whether the first human being is present in or absent from the second environment based on information from the second camera. [0035] Aspect 26 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 21 through 25 to optionally include or use a second camera configured to receive a series of images of a second environment, wherein each of the images is a different frame acquired at a different time, and a second processor circuit configured to receive information from the memory circuit about the first human being and receive information about one or more other human beings detected in images from the second camera, and generate a dashboard for presenting to a user information about the first human being together with information about the one or more other human beings. [0062] FIG. 1 the cameras 102b can have respective fixed fields of view or can be movable. In an example, at least one of the cameras 102b includes a camera with a 180 degree view sensor and the camera is mounted on a ceiling or wall. Images acquired by such a camera can be de-warped such as prior to further processing. Other 180 degree view or more limited field of view sensors can similarly be used.) and configured to generate position information of the detected object in the corresponding field of view area; (Khadloya [0013] Aspect 4 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 1 through 3 to optionally include selecting a classification model for use by the first neural network, wherein the classification model is optimized for image analysis at an angle or field of view corresponding to the angle or field of view of the camera.)
and configured to transmit the specified information on the object to a host system. (Khadloya [0015] Aspect 6 can include or use, or can optionally be combined with the subject matter of Aspect 5, to optionally include communicating, to a server located remotely from the camera, information about the indication that a human being is present in or absent from the environment. [0070] In an example, a processor circuit can be provided outside of a sensor. In this case, image or video information from the camera can be transmitted to the processor circuit for analysis, such as to determine whether a human being is present in a scene. In an example, such a processor circuit can be a home security panel or controller such as can be located remotely from the sensor such as in a different home or building. [0071] In an example, the edge device-based human presence identification can be configured to provide various information. For example, a result of a human presence detection algorithm can be that (i) a single human is detected to be present in a scene; (ii) multiple human beings are detected to be present in a scene, or (iii) specific or known human being(s) are determined to be present in a scene, and information about the known human beings such as names or other characteristics can be determined. [0072] In an example, the body detector 108 is configured to process one or more received images (or frames of video data) and executes various techniques for detecting a presence of a human body. In an example, the body detector 108 first processes each of multiple received images to identify one or more regions that are likely to include a human figure or that include candidate humans. Next, the body detector 108 can identify a root of a body in the one or more regions such as using root filters. Next, the body detector 108 can be used to identify one or more body parts of a detected body based on, e.g., pair-wise constraints. The body parts can be detected using one or more body part detectors as discussed elsewhere herein. The body detector 108 can calculate scores for the various detected body parts and calculate an overall score based on one or more scores associated with the body parts. The overall score can correspond to a confidence that a human or individual is identified in the scene, as opposed to another non-human object. While performing human detection, the body detector 108 can be configured to consider occlusion, illumination or other conditions. [0073] In an example, the network 110 can be any wired network, wireless network, a combination of wired or wireless networks. In an example, the network 110 includes a LAN or wireless LAN, the Internet, a point-to-point connection, or other network connection and combinations thereof. The network 110 can be any other type of network that transmits or receives data, such as from personal devices, telephones, video/image capturing devices, video/image servers, or any other electronic devices.)
Khadloya [0134] In an example, a heat map can be generated using information from the body detector 108. A heat map can include a pictorial representation of an occupancy that is typically color-coded to show areas of greater or lesser occupancy over time. For example, a heat map can be used together with, or generated from, dwell time or wait time information. In an example, a heat map can include information about how many individuals are present in each of one or more regions over time, and can use different colors to show a count of individuals and an amount of time spent by each individual (or by a group of individuals) in the one or more regions. Khadloya do not explicitly teach and a computer configured to specify information regarding the position of the object in an area where the respective field of view areas of the plurality of cameras are integrated based on the position information generated by the plurality of processors.
However Mirza teach and a computer configured to specify information regarding the position of the object in an area where the respective field of view areas (Mirza [0070] In order to describe the physical location of people and objects within the space 102, a global plane 104 is defined for the space 102. The global plane 104 is a user-defined coordinate system that is used by the tracking system 100 to identify the locations of objects within a physical domain (i.e. the space 102). Referring to FIG. 1 as an example, a global plane 104 is defined such that an x-axis and a y-axis are parallel with a floor of the space 102. In this example, the z-axis of the global plane 104 is perpendicular to the floor of the space 102. A location in the space 102 is defined as a reference location 101 or origin for the global plane 104. In FIG. 1, the global plane 104 is defined such that reference location 101 corresponds with a corner of the store. In other examples, the reference location 101 may be located at any other suitable location within the space 102. [0071] In this configuration, physical locations within the space 102 can be described using (x,y) coordinates in the global plane 104. As an example, the global plane 104 may be defined such that one unit in the global plane 104 corresponds with one meter in the space 102. In other words, an x-value of one in the global plane 104 corresponds with an offset of one meter from the reference location 101 in the space 102. In this example, a person that is standing in the corner of the space 102 at the reference location 101 will have an (x,y) coordinate with a value of (0,0) in the global plane 104. If person moves two meters in the positive x-axis direction and two meters in the positive y-axis direction, then their new (x,y) coordinate will have a value of (2,2). In other examples, the global plane 104 may be expressed using inches, feet, or any other suitable measurement units) of the plurality of cameras are integrated based on the position information generated by the plurality of processors. (Mirza [0063] This means that information from each camera needs to be processed independently to identify and track people and objects within the field of view of a particular camera. The information from each camera then needs to be combined and processed as a collective in order to track people and objects within the physical space.)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Khadloya, further incorporating Mirza in video/camera technology. This involves Mirza’s known global plane coordinate integration technique to Khadloya’s per camera human detection architecture as a known technique applied to a similar system. One would be motivated to do so, to incorporate a computer configured to specify information regarding the position of the object in an area where the respective field of view areas of the plurality of cameras are integrated based on the position information generated by the plurality of processors. This functionality will improve efficiency with predictable results.
Regarding to claim 2:
2. Khadloya teach the position detection system according to claim 1, wherein each of the plurality of processors is configured to learn a first image of the object that is training data, (Khadloya [0059] In an example, human detection using machines to analyze images includes model training and detection. Training can be an offline step where machine learning algorithms (such as a convolutional neural network, or CNN) are trained on a training data set to learn human and non-human forms from various images. Detection can use one or more machine learning models to classify human and non-human regions in an image or frame. In an example, the detection is performed using a pre-processing step of identifying potential regions for presence of a human and a post-processing step of validating the identified potential regions. In the detection step, part-based detectors can be used in the identified region(s) by a root filter such as to localize, or provide information about a location of, each human part. [0091] In an example, the storage device 212 can include a training database including pre-loaded human images for comparison to a received image (e.g., image information received using one or more of the cameras 102b) during a detection process. The training database can include images of humans in different positions and can include images of humans having different sizes, shapes, genders, ages, hairstyles, clothing, and so on. In an example, the images can be positive image clips for positive identification of objects as human bodies and can include negative image clips for positive identification of objects as non-human bodies.)
and recognize the object in the field image that is input data acquired from the respective one of the plurality of cameras to specify the position of the recognized object. (Please see the rejection of claim 1)
Regarding to claim 3 and 5:
3. Khadloya teach the position detection system according to claim 1, wherein each of the plurality of processors is an image processor. (Khadloya [0030] Aspect 21 can include a camera configured to receive a series of images of the environment, wherein each of the images is a different frame acquired at a different time and an image processor circuit. The image processor circuit can be configured to identify a difference between a portion of at least first and second frames acquired by the camera, the difference indicating movement by one or more objects in the environment monitored by the camera, and select a third frame from among the multiple frames for full-frame analysis, and apply the third frame as an input to a first neural network and in response receive a first indication that the third frame includes an image of at least a portion of a first human being. [0037] Aspect 28 can include a first camera configured to receive a series of images of the environment, wherein each of the images corresponds to a different frame acquired at a different time, and an image processor circuit. In an example, the image processor circuit is configured to identify a difference between a portion of at least first and second frames acquired by the first camera, the difference indicating movement by an object in the environment monitored by the first camera, to select a third frame from among the multiple frames for full-frame analysis, to apply the third frame as an input to a first neural network and in response determine a first indication of a likelihood that the third frame includes at least a portion of an image of a first human being, and to provide an indication that a human being is present in or absent from the environment based on the identified difference and on the determined first indication of the likelihood that the third frame includes at least a portion of an image of the first human being.)
Regarding to claim 4 and 6-7:
4. Khadloya teach the position detection system according to claim 1, wherein one of the plurality of cameras and a respective the of the plurality of processors are disposed in a single unit. (Khadloya [0014] Aspect 5 can include or use, or can optionally be combined with the subject matter of one or any combination of Aspects 1 through 4 to optionally include or use the first processor circuit being a processor circuit that is housed together with the camera.)
Conclusion
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 extension fee 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 NASIM N NIRJHAR whose telephone number is (571)272-3792. The examiner can normally be reached on Monday - Friday, 8 am to 5 pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William F Kraig can be reached on (571) 272-8660. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NASIM N NIRJHAR/Primary Examiner, Art Unit 2896
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