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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/24/26 has been entered. Claims 1-20 are pending.
Response to Amendment
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.
Claim(s) 1, 4-13 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over GOULDEN et al. (US 2019/0087646) in view of CHEN et al. (US 2022/0044426).
Claims 1 and 13, GOULDEN teaches a system and method comprising:
a first image capture device and a second image capture device (Fig. 2B, par. 12; the presence information is derived from video data of one or more cameras 118 having a field of view corresponding to the entryway);
output devices (par. 27: the security action includes at least one of: activating a light or adjusting a lighting level of the smart home environment; locking or unlocking a door); and
one or more processors coupled with memory and configured to (par. 29):
detect, using the first and second image capture devices, an entity approaching an area (par. 83: camera facing the entryway);
determine, based on attributes of the entity detected by the image capture device, that the entity corresponds to a profile (par. 230: the server system 164 performs a facial recognition operation (912) based on one or more frames of the motion stream sent to the server by doorbell 106, and determines, based on an outcome of the facial recognition operation, whether the visitor is known to the electronic greeting system);
identify one or more actions corresponding to preferences indicated in the profile (par. 8: if a known visitor approaches the entryway, the system can provide a subset of actions that are appropriate for a known visitor (e.g., a greeting, and/or an option to unlock the door)); and
actuate one or more of the output devices to perform the one or more actions to receive the entity to the area (par. 233: another smart device 204 implements the response (e.g., smart door lock 120 unlocks the door to let the visitor in)).
GOULDEN does not specifically teach the first and the second image capture devices have overlapping field of views and are cooperatively configured to detect an entity using data from both the first and second image capture device within the overlapping field of views.
In the field of endeavor, CHEN teaches a cross-sensor object attribute analysis method which can cover a space in a partially overlapping manner by the images of a plurality of image sensing devices, and use an edge computing architecture to implement at least one AI module to process the images obtained at a same time point to determine the identity of at least one object in the space (par. 32&51).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify GOULDEN’s system by utilizing multi cameras identification setup with overlapped fields of view as taught by CHEN for the purpose of simultaneous monitoring the area and quickly identify an object, a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (CHEN par. 7).
Claim 4, GOULDEN teaches wherein to detect the entity approaching the area, the one or more processors are configured to detect, using the first or second image capture device, at least one of physical characteristics of the entity or behavioral characteristics of the entity (par. 22: context information is based on a facial recognition analysis result, one or more behavior characteristics of the visitor, one or more physical characteristics of the visitor).
Claims 5 and 15, GOULDEN teaches wherein the one or more processors are configured to: detect the entity approaching the area based on images captured of the entity; determine, using image recognition techniques, features of the entity from the images; and determine that the entity corresponds to the profile responsive to determining that the features of the entity meet a threshold (par. 219&230: the server system 164 performs a facial recognition operation (912) based on one or more frames of the motion stream sent to the server by doorbell 106, and determines, based on an outcome of the facial recognition operation, whether the visitor is known to the electronic greeting system).
Claim 6, GOULDEN teaches wherein the one or more processors are configured to: generate a second profile corresponding to a second entity (par. 265: the user adds new face images to the database by registering automatically cropped images of new faces from new or previously unregistered visitors to the smart home environment).
Claims 7 and 16, GOULDEN teaches wherein the output devices comprise one or more of: a light source; a speaker; a display device; an appliance; a climate control system; a lock; an entrance; or a water system (par. 39: locking door).
Claims 8 and 17, GOULDEN teaches wherein to identify the one or more actions, the one or more processors are configured to provide the attributes of the entity and the preferences as inputs to a machine learning model (par. 21: the smart home environment (e.g., a smart doorbell) determines that a particular visitor always knocks at a particular location on the door, in a particular pattern, and with a particular amount of force. In this example, the smart home environment associates such knock attributes with the particular visitor. In another example, a visitor profile for a particular visitor is set (e.g., set manually by a user, or set via machine learning) to associate a particular knock pattern, a particular doorbell ring pattern, or a particular verbal announcement with the particular visitor).
Claims 9 and 18, GOULDEN teaches wherein the one or more processors are further configured to: detect, using the first and second image capture devices cooperatively within the overlapping field of views, a second entity approaching the area; determine that the second entity does not correspond to one of a plurality of profiles comprising the profile; identify one or more second actions based on the second entity to deter the entity from perpetrating an act; and actuate one or more of the output devices to perform the one or more second actions (par. 66: On the other hand, if an unknown visitor approaches the entryway, the system can provide a different subset of actions that are appropriate for an unknown visitor (e.g., a warning, an option to lock the door, and/or an option to call the authorities), see further claim 1 rejection).
Claim 10, GOULDEN teaches wherein the actions comprise turning on a light system to guide the entity within the area (par. 71: at least a subset of the quick actions are action-oriented, such as increasing a security level of the smart home environment, locking or unlocking a door, turning on or off a light, calling the authorities, alerting a security company or other person associated with the smart home (e.g., a neighbor), capturing a snapshot or video clip of the visitor (e.g., and sending it to the authorities, or storing it on a user-accessible server system), and/or turning on or off an alarm).
Claims 11 and 19, GOULDEN teaches wherein the one or more processors are further configured to determine the one or more preferences indicated in the profile based on historic actions of the entity within the area (par. 142: the inferred user preferences are based on historical user activity and/or historical activity of other users).
Claims 12 and 20, GOULDEN teaches wherein the preferences are established via a client device (par. 43: receiving a subsequent selection of an identified subsequent action from the user of the client device), and wherein to actuate one or more of the output devices to perform the one or more actions to receive the entity, the one or more processors are configured to actuate a sequence of the actions according to the preferences established via the client device (par. 43: the smart home may then send a new set of quick actions to the smart home user, including (a) an action to unlock the door, (b) an action to alert Susan of the visitor, (c) an action to request that the visitor wait for someone to answer the door, and (d) an action notifying the visitor that Susan is unavailable and the study session must be canceled … par. 70-71: a different scenarios where the user may preprogram one or more of the quick actions or assistant responses to include at least a subset of the quick actions are action-oriented, such as increasing a security level of the smart home environment, locking or unlocking a door, turning on or off a light, calling the authorities, alerting a security company or other person associated with the smart home (e.g., a neighbor), capturing a snapshot or video clip of the visitor (e.g., and sending it to the authorities, or storing it on a user-accessible server system), and/or turning on or off an alarm).
Claim(s) 2, 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over GOULDEN in view of CHEN et al., and further in view of Richardson et al. (US 20180348718).
Claims 2 and 14, the combination does not teach wherein to detect the entity approaching the area, the one or more processors are configured to receive, from a client device associated with the entity, an indication that the entity has entered a perimeter of a geographic area.
In the field of endeavor, Richardson teaches a triggered notifications and actions system. He goes on to teach receiving boundary-crossing (geo-fence) signals from the plurality of mobile devices, where a boundary-crossing signal specifies when one of the mobile devices crosses a boundary (e.g., the geo-fence) around a perimeter setup to be near a coordinator 210 (par. 91).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the combination’s system to include boundary detection of user as taught by Richardson in order to automatically set home security and automation system to certain mode as programmed for user convenience, a finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable (Richardson par. 93).
Claim 3, the combination teaches wherein the indication includes an identification of the profile (Richardson par. 7: a plurality of users, registering a device profile for a resident device (e.g., smart thermostat) to be controlled based on a location of the plurality of users).
Response to Arguments
Applicant’s arguments have been considered but are moot in view of new ground(s) of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to An T Nguyen whose telephone number is (571)270-5167. The examiner can normally be reached Monday - Friday 9-5 ET.
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/AN T NGUYEN/Primary Examiner, Art Unit 2686
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