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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/02/2025 has been entered.
Response to Arguments
Applicant's arguments filed on 04/02/2025 have been fully considered but they are not persuasive.
In re pages 9-10, the applicant argues that “(Emphasis added.) First, Fu fails to teach "security actions identifying, to the person, the one or more characteristics of the person," as recited in claim 1. In contrast, Fu does not teach all of the limitations of the claim. Fu appears to teach identifying a person but does not teach providing a security action "identifying, to the person, the one or more characteristics of the person," as recited in claim 1. As a hypothetical example for sake of clarity, stating "hey you in the Patriots hoody, step away from the car!" may identify to the person a characteristic of the person. Fu teaches that the system might determine an identity of the person and, based on that identity, escalate or de-escalate the level of alert but does not teach "security action identifying, to the person, the one or more characteristics of the person." As such, Fu fails to teach this limitation.
Xu also does not teach "security actions identifying, to the person, the one or more characteristics of the person," as recited in claim 1, nor is Xu relied upon as such. Xu is only relied upon as teaching an Al component. Therefore, Xu fails to remedy the above- identified shortcoming of Xu. Accordingly, Applicant asserts that claim 1 is patentable over Fu and Xu because the references, alone or in combination, fail to teach all of the limitations of the claim.”
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, Fu et al. teaches in col. 6, line 36-65 that “For example, when the unwelcomed visitors learn about the database 218 they may not target the neighborhood 202a-202n. Data in the database 218 may be used to classify types of visitors (e.g., comparisons may be performed between the captured video data and information in the database 218).Multiple levels of alerts may be implemented to distinguish unwelcomed visitors from welcomed visitors (e.g., household members). Since most visitors may be welcomed, identifying strangers and raising the level of alert for immediate attention may be important. The technology to identify and/or classify welcomed visitors may include facial recognition, voice recognition, machine learning of habits (e.g., behaviors) and schedules of household members, and/or user inputs when errors occur. Learned behavior may be used to determine which pre-defined function to perform.”, col. 15 lines 29-43 teaches “For example, the pixels comprising each video frame may be analyzed and/or compared to reference objects and/or templates (or feature maps) to identify, classify and/or recognize objects. For example, video data in the video frames may be compared to templates of known objects to classify portions of captured video frames as a particular type of object. In an example, facial recognition may be implemented to compare an object classified as a face of a person with stored data corresponding to previously stored faces. In some embodiments, the facial recognition may be performed locally by the smart security cameras 100a-100n.”, col. 15 lines 59-col. 16 lines 16 teaches “Characterization of the behavior of the visitor may be performed and/or presented to home/business owners in real time. For example, categorizations of the behavior of visitors may comprise the behavior typical of potential burglars, solicitors, delivery workers, residents, domestic helpers, strangers, friendly visitors with and/or without access to the premises, etc. The number and/or type of behavior categorizations may be varied according to the design criteria of a particular implementation. Different types of behavior by a visitor may have a different impact on the type of visitor classification. For example, a visitor touching the doorknob may result in a small increase to an urgency level. In another example, a visitor detected at two different access points of the premises may result in a large increase to the urgency level (e.g., checking multiple entrances may indicate a burglar is trying to enter the premises). Notifications may be sent and/or other event responses may be initiated based on the urgency level. Heuristics may be used and/or assumptions may be made when monitoring behavior of a visitor. For example, if the visitor stays at the access point (e.g., the front door) for more than 10 seconds an assumption may be made that the visitor has rung the doorbell and/or knocked on the door. The heuristics used and/or the assumptions made may be used to adjust the urgency level.” Fu et al. teaches video data captured by the smart security cameras 100a-100n may be analyzed to determine a behavior and/or identity of a visitor. Based on the video analysis (e.g., facial recognition, object detection, object classification, etc.) and/or behavioral analysis, the smart security cameras 100a-100n may determine whether a visitor is a friendly visitor (e.g., a known, a welcome and/or whitelisted visitor) and/or an unfriendly visitor (e.g., an unknown, an undesired and/or blacklisted visitor). Furthermore, Fu et al. teaches for example, the pixels comprising each video frame may be analyzed and/or compared to reference objects and/or templates (or feature maps) to identify, classify and/or recognize objects. For example, video data in the video frames may be compared to templates of known objects to classify portions of captured video frames as a particular type of object. In an example, facial recognition may be implemented to compare an object classified as a face of a person with stored data corresponding to previously stored faces. In some embodiments, the facial recognition may be performed locally by the smart security cameras 100a-100n. Herein, Fu et al. compares visitor/object with reference information to identify visitor/object as a friendly or an unfriendly visitor, facial recognition may be implemented to compare an object classified as a face of a person with stored data corresponding to previously stored faces. Therefore, based on the visual analysis identifying characteristics of the person to perform security actions prior to predicted event.
Therefore, in view of the above, the examiner believes that the features of the claims are taught by the applied arts. See also the Office Action sets for the below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 4-7, 9-10, 13, 15-16, 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 5-6, 8, 15-16 of U.S. Patent No. 11,941,114 (herein, “’114”). Although the claims at issue are not identical, they are not patentably distinct from each other because
Regarding claim 1 of instant application
Claim 1 of instant application
Claim 1 of ‘114
A method, comprising:
receiving, by at least one processor, a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input;
detecting, by inputting the received set of inputs into an artificial intelligence model executed by the at least one processor, a person within a zone within a field of view of the video input;
A method, comprising:
receiving a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input; detecting a presence within a field of view of the video input;
determining that the presence is within a zone established within the field of view of the video input based on the received set of inputs and a dimension of the zone;
determining, by the at least one processor, one or more characteristics of the person based at least in part on the received set of inputs;
classifying, by the at least one processor, the one or more characteristics of the person as being correlated with one or more events;
comparing, by the at least one processor, the classification of the one or more characteristics of the person to a threshold;
determining, by inputting the received set of inputs into an artificial intelligence model, that the presence comprises a presence of a person; determining one or more characteristics of the person based at least in part on the received set of inputs;
in response to the classification of the one or more characteristics of the person satisfying the threshold, predicting, by the at least one processor, an event within the zone; and
predicting an event within the zone based at least in part on a correlation between the one or more characteristics and the event; and
performing, by the at least one processor, one or more security actions prior to the predicted event, the one or more security actions identifying, the person, the one or more characteristics of the person and selected to deter the person from performing the predicted event within the zone.
performing one or more security actions prior to the predicted event, the one or more security actions selected to deter the person from performing the predicted event within the zone.
It should be noted that the table above distinguishes the equivalent limitations between the instant application and that of ‘114. In conclusion, claim 1 of the instant application is anticipated by claim 1 of ‘114 in that claim 1 of ‘114 contains all the limitations of claim 1 of the instant application. The instant application claim is broader or equivalent in every aspect than claim 1 of ‘114 and is therefore an obvious variant thereof. Although the conflicting claims are not identical, they are not patently distinct from each other because claim 1 is generic to all that is recited in claim 1 of ‘114. That is, claim 1 of instant application is anticipated by claim 1 of ‘114.
Claim 4 of the instant application corresponds to claim 3 of ‘114 Patent.
Claim 5 of the instant application corresponds to claim 5 of ‘114 Patent.
Claim 6 of the instant application corresponds to claim 6 of ‘114 Patent.
Claim 7 of the instant application corresponds to claim 8 of ‘114 Patent.
Claim 9 of the instant application corresponds to claim 15 of ‘114 Patent.
Regarding claim 10 of instant application
Claim 10 of instant application
Claim 16 of ‘114
An apparatus, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input;
detect, by inputting the received set of inputs into an artificial intelligence model, a person within a zone within a field of view of the video input;
determine one or more characteristics of the person based at least in part on the received set of inputs;
classify the one or more characteristics of the person as being correlated with one or more events;
An apparatus, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input;
detect a presence within a field of view of the video input;
determine that the presence is within a zone established within the field of view of the video input based on the received set of inputs and a dimension of the zone;
determine, by inputting the received set of inputs into an artificial intelligence model, that the presence comprises a presence of a person;
compare the classification of the one or more characteristics of the person to a threshold;
determine one or more characteristics of the person based at least in part on the received set of inputs;
in response to the classification of the one or more characteristics of the person satisfying the threshold, predict an event within the zone; and
predict an event within the zone based at least in part on a correlation between the one or more characteristics and the event; and
perform one or more security actions prior to the predicted event, the one or more security actions identifying, to the person, the one or more characteristics of the person and selected to deter the person from performing the predicted event within the zone.
perform one or more security actions prior to the predicted event, the one or more security actions selected to deter the person from performing the predicted event within the zone.
It should be noted that the table above distinguishes the equivalent limitations between the instant application and that of ‘114. In conclusion, claim 10 of the instant application is anticipated by claim 16 of ‘114 in that claim 16 of ‘114 contains all the limitations of claim 10 of the instant application. The instant application claim is broader or equivalent in every aspect than claim 16 of ‘114 and is therefore an obvious variant thereof. Although the conflicting claims are not identical, they are not patently distinct from each other because claim 10 is generic to all that is recited in claim 16 of ‘114. That is, claim 10 of instant application is anticipated by claim 16 of ‘114.
Claim 13 of the instant application corresponds to claim 6 of ‘114 Patent.
Claim 15 of the instant application corresponds to claim 15 of ‘114 Patent.
Regarding claim 16 of instant application
Claim 16 of instant application
Claim 20 of ‘114
A non-transitory computer-readable medium storing code comprising instructions executable by a processor to:
receive a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input;
detect, by inputting the received set of inputs into an artificial intelligence model, a person within a zone within a field of view of the video input; determine one or more characteristics of the person based at least in part on the received set of inputs;
classify the one or more characteristics of the person as being correlated with one or more events;
A non-transitory computer-readable medium storing code comprising instructions executable by a processor to:
receive a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input; detect a presence within a field of view of the video input;
determine that the presence is within a zone established within the field of view of the video input based on the received set of inputs and a dimension of the zone;
determine, by inputting the received set of inputs into an artificial intelligence model, that the presence comprises a presence of a person;
compare the classification of the one or more characteristics of the person to a threshold;
determine one or more characteristics of a person proximate the one or more sensors based at least in part on the received set of inputs;
in response to the classification of the one or more characteristics of the person satisfying the threshold, predict an event within the zone; and
predict an event within the zone based at least in part on a correlation between the one or more characteristics and the event; and
perform one or more security actions prior to the predicted event, the one or more security actions identifying, to the person, the one or more characteristics of the person and selected to deter the person from performing the predicted event within the zone.
perform one or more security actions prior to the predicted event, the one or more security actions selected to deter the person from performing the predicted event within the zone.
It should be noted that the table above distinguishes the equivalent limitations between the instant application and that of ‘114. In conclusion, claim 16 of the instant application is anticipated by claim 20 of ‘114 in that claim 20 of ‘114 contains all the limitations of claim 16 of the instant application. The instant application claim is broader or equivalent in every aspect than claim 20 of ‘114 and is therefore an obvious variant thereof. Although the conflicting claims are not identical, they are not patently distinct from each other because claim 16 is generic to all that is recited in claim 20 of ‘114. That is, claim 16 of instant application is anticipated by claim 20 of ‘114.
Claim 20 of the instant application corresponds to claim 15 of ‘114 Patent.
Claims 2-3, 8, 11-12, 14, 17-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 of U.S. Patent No. 11,941,114 (herein, “’114”) in view of US US 2019/0108404 by Xu.
Regarding claim 2 of instant application, claim 1 of ‘114 teaches the claimed as discussed above but fails to teach generating, using an artificial intelligence algorithm, the one or more security actions.
Xu teaches generating, using an artificial intelligence algorithm, the one or more security actions (paragraph 0070 teaches “FIG. 12A shows an exemplary scenario where suspicious moving objects are detected and shared with other homes in the network. In FIG. 12, a home detects a suspicious person and shares the information with other homes in the neighborhood secured by the system. The home indicates a level of confidence with the detection, and neighboring homes in the system can adjust their cameras to look for the identified suspect.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the ability to include artificial intelligence algorithm, as taught by Xu et al. into claim 1 of ‘114, because such incorporation would allow more options to detect person, thus increase user flexibility of the system.
Regarding claim 3 of instant application, claim 1 of ‘114 teaches the claimed as discussed above but fails to teach the method wherein the artificial intelligence algorithm is trained based on historical deterrence of events.
Xu teaches the method wherein the artificial intelligence algorithm is trained based on historical deterrence of events (paragraph 0082 teaches “The camera security system receives images of people and recognizes faces based on the setup of FIGS. 1-2 at step 32. The camera security system then monitors for a trigger at step 34. The trigger can comprise any appropriate trigger as described herein. If, at step 36, a trigger is not detected, the process proceeds to step 44 to monitor for a trigger. If, at step 36, a trigger is detected, a message is provided at step 38. The camera security system monitors for an indication of a notification, as described herein, at step 40. If, at step 42, it is determined that no indication of a notification has been received by the camera security system, the process proceeds to step 40. If, at step 42, it is determined that indication of a notification has been received, the camera security system renders an indication of the notification (e.g., displays message that a member is in danger, makes sound, displays image of an individual in danger, vibrates, etc.) at step 44.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the ability to include the artificial intelligence algorithm is trained based on historical deterrence of events, as taught by Xu et al. into claim 1 of ‘114, because such incorporation would allow more options to detect person, thus increase user flexibility of the system.
Regarding claim 8 of instant application, claim 1 of ‘114 teaches the claimed as discussed above but fails to teach the method further comprising: identifying the person using an artificial intelligence algorithm, the one or more security actions selected based at least in part on the identified person.
Xu teaches the method further comprising: identifying the person using an artificial intelligence algorithm, the one or more security actions selected based at least in part on the identified person (in addition to discussion above, Xu, paragraph 0082 teaches “The camera security system receives images of people and recognizes faces based on the setup of FIGS. 1-2 at step 32. The camera security system then monitors for a trigger at step 34. The trigger can comprise any appropriate trigger as described herein. If, at step 36, a trigger is not detected, the process proceeds to step 44 to monitor for a trigger. If, at step 36, a trigger is detected, a message is provided at step 38. The camera security system monitors for an indication of a notification, as described herein, at step 40. If, at step 42, it is determined that no indication of a notification has been received by the camera security system, the process proceeds to step 40. If, at step 42, it is determined that indication of a notification has been received, the camera security system renders an indication of the notification (e.g., displays message that a member is in danger, makes sound, displays image of an individual in danger, vibrates, etc.) at step 44.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the ability to include identifying the person using an artificial intelligence algorithm, the one or more security actions selected based at least in part on the identified person, as taught by Xu et al. into claim 1 of ‘114, because such incorporation would allow more options to detect person, thus increase user flexibility of the system.
Claim 11 of instant application is rejected for the same reason as discussed in the claim 2 above with reference to claim 1 of ‘114 Patent.
Claim 12 of instant application is rejected for the same reason as discussed in the claim 3 above with reference to claim 1 of ‘114 Patent.
Claim 14 of instant application is rejected for the same reason as discussed in the claim 28 above with reference to claim 1 of ‘114 Patent.
Claim 17 of instant application is rejected for the same reason as discussed in the claim 2 above with reference to claim 1 of ‘114 Patent.
Claim 18 of instant application is rejected for the same reason as discussed in the claim 3 above with reference to claim 1 of ‘114 Patent.
Claim 19 of instant application is rejected for the same reason as discussed in the claim 8 above with reference to claim 1 of ‘114 Patent.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10,986,717 by Fu et al. in view of US 2019/0108404 by Xu.
Regarding claim 1, Fu et al. discloses a method, comprising:
receiving, by at least one processor, a set of inputs from one or more sensors of a security system, the set of inputs comprising a video input (fig. 3 (100), col. 5 lines 64-67 teaches “The areas of interest 204a-204n may be doors, windows, garages, other entrances, and/or vantage points. Generally, the smart cameras 100a-100n may be mounted at the areas of interest 204a-204n.”);
detecting, by inputting the received set of inputs, a person within a zone within a field of view of the video input (in addition to discussion above, col. 16 lines 27-57 teaches “In one example, a visitor may be detected approaching a front door, and a sequence of motions may indicate a behavior of placing something in a mailbox. A notification may be sent to the homeowner 252 that an item has been delivered, but not enough information may be available for the processor 152 to recognize and/or identify the event (e.g., the delivery could be the daily mail, a solicitor dropping off an advertisement, a parcel delivery, a newspaper, etc.).);
determining, by the at least one processor, one or more characteristics of the person based at least in part on the received set of inputs (col. 29 lines 65-col.30 lines 16 teaches “The functions performed by the diagrams of FIGS. 1-10 may be implemented using one or more of a conventional general purpose processor, digital computer, microprocessor……”, col. 15 lines 21-33 teaches “In some embodiments, video data captured by the smart security cameras 100a-100n may be analyzed to determine a behavior and/or identity of a visitor. Based on the video analysis (e.g., facial recognition, object detection, object classification, etc.) and/or behavioral analysis, the smart security cameras 100a-100n may determine whether a visitor is a friendly visitor (e.g., a known, a welcome and/or whitelisted visitor) and/or an unfriendly visitor (e.g., an unknown, an undesired and/or blacklisted visitor).”, col. 22 lines 4-24);
classifying, by the at least one processor, the one or more characteristics of the person as being correlated with one or more events (in addition to discussion above, col. 6, line 36-65 teaches “For example, when the unwelcomed visitors learn about the database 218 they may not target the neighborhood 202a-202n. Data in the database 218 may be used to classify types of visitors (e.g., comparisons may be performed between the captured video data and information in the database 218).Multiple levels of alerts may be implemented to distinguish unwelcomed visitors from welcomed visitors (e.g., household members). Since most visitors may be welcomed, identifying strangers and raising the level of alert for immediate attention may be important. The technology to identify and/or classify welcomed visitors may include facial recognition, voice recognition, machine learning of habits (e.g., behaviors) and schedules of household members, and/or user inputs when errors occur. Learned behavior may be used to determine which pre-defined function to perform.”);
comparing, by the at least one processor, the classification of the one or more characteristics of the person to a threshold (in addition to discussion above, col. 15 lines 29-43 teaches “For example, the pixels comprising each video frame may be analyzed and/or compared to reference objects and/or templates (or feature maps) to identify, classify and/or recognize objects. For example, video data in the video frames may be compared to templates of known objects to classify portions of captured video frames as a particular type of object. In an example, facial recognition may be implemented to compare an object classified as a face of a person with stored data corresponding to previously stored faces. In some embodiments, the facial recognition may be performed locally by the smart security cameras 100a-100n.”, col. 15 lines 59-col. 16 lines 16 teaches “Characterization of the behavior of the visitor may be performed and/or presented to home/business owners in real time. For example, categorizations of the behavior of visitors may comprise the behavior typical of potential burglars, solicitors, delivery workers, residents, domestic helpers, strangers, friendly visitors with and/or without access to the premises, etc. The number and/or type of behavior categorizations may be varied according to the design criteria of a particular implementation. Different types of behavior by a visitor may have a different impact on the type of visitor classification. For example, a visitor touching the doorknob may result in a small increase to an urgency level. In another example, a visitor detected at two different access points of the premises may result in a large increase to the urgency level (e.g., checking multiple entrances may indicate a burglar is trying to enter the premises). Notifications may be sent and/or other event responses may be initiated based on the urgency level. Heuristics may be used and/or assumptions may be made when monitoring behavior of a visitor. For example, if the visitor stays at the access point (e.g., the front door) for more than 10 seconds an assumption may be made that the visitor has rung the doorbell and/or knocked on the door. The heuristics used and/or the assumptions made may be used to adjust the urgency level.”);
in response to the classification of the one or more characteristics of the person satisfying the threshold, predicting, by the at least one processor, an event within the zone (as discussed above); and
performing, by the at least one processor, one or more security actions prior to the predicted event, the one or more security actions identifying, the person, the one or more characteristics of the person and selected to deter the person from performing the predicted event within the zone (in addition to discussion above, col. 17 lines 41-56 teaches “The user 252 may set up an option to automatically receive visitors at the door. In one example, the smart security light 100 may be configured to deter a potential burglar who is looking for an easy target home with no one inside the house 202. When the smart security light 100 classifies the visitor as a potential burglar the speaker 174 may announce “Hi Mary and Paul are busy. Please speak and leave a message with your phone number, you are being recorded by a monitoring service. Bye bye.” (e.g., play the pre-recorded security response). If the visitor is still detected after a pre-determined amount of time (e.g., 40 seconds), the additional security response may be activated (e.g., activating a siren for 20 seconds). If the visitor is still there after the siren, an automatic alarm signal may be sent (e.g., the notification security response) to a designated person to engage with the visitor (e.g., the authorities 214)” Fu et al. compares visitor/object with reference information to identify visitor/object as a friendly or an unfriendly visitor, thus meets claimed invention).
Fu et al. fails to disclose detecting, by inputting the received set of inputs into an artificial intelligence model executed by the at least one processor, a person within a zone within a field of view of the video input.
Xu discloses detecting, by inputting the received set of inputs into an artificial intelligence model executed by the at least one processor, a person within a zone within a field of view of the video input (fig. 11, paragraph 0042 teaches “Another sensors that can be used with the camera and PIR can include a force/wave sensor, a microphone, a moisture sensor, or a combination thereof. The force/wave sensor can be at least one of a motion detector, an accelerometer, an acoustic sensor, a tilt sensor, a pressure sensor, a temperature sensor, or the like. The motion detector is configured to detect motion occurring outside of the communications camera security system, for example via disturbance of a standing wave, via electromagnetic and/or acoustic energy, or the like. The accelerator is capable of sensing acceleration, motion, and/or movement of the communications camera security system.”, paragraph 0069 teaches “FIG. 11 shows in more details of the system 100. The system includes an artificial intelligence server 130 that applies deep learning to sensors 132 and communicates with users such as human 134. The AI server 130 handles growing network intelligence as more homes add camera with facial recognition in accordance with the preferred embodiments. For example, FIG. 12 illustrates an environment where one home detects a suspicious person, and such suspicious warnings are broadcasted to the neighborhood. Thus, users with or without security cameras can be protected.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the ability to include artificial intelligence model, as taught by Xu et al. into the system of Fu et al., because such incorporation would allow more options to detect person, thus increase user flexibility of the system.
Regarding claim 2, the method further comprising generating, using an artificial intelligence algorithm, the one or more security actions (in addition to discussion above, Xu, paragraph 0070 teaches “FIG. 12A shows an exemplary scenario where suspicious moving objects are detected and shared with other homes in the network. In FIG. 12, a home detects a suspicious person and shares the information with other homes in the neighborhood secured by the system. The home indicates a level of confidence with the detection, and neighboring homes in the system can adjust their cameras to look for the identified suspect.”).
The motivation for combining references has been discussed in independent claim above.
Regarding claim 3, the method wherein the artificial intelligence algorithm is trained based on historical deterrence of events (in addition to discussion above, Xu, paragraph 0082 teaches “The camera security system receives images of people and recognizes faces based on the setup of FIGS. 1-2 at step 32. The camera security system then monitors for a trigger at step 34. The trigger can comprise any appropriate trigger as described herein. If, at step 36, a trigger is not detected, the process proceeds to step 44 to monitor for a trigger. If, at step 36, a trigger is detected, a message is provided at step 38. The camera security system monitors for an indication of a notification, as described herein, at step 40. If, at step 42, it is determined that no indication of a notification has been received by the camera security system, the process proceeds to step 40. If, at step 42, it is determined that indication of a notification has been received, the camera security system renders an indication of the notification (e.g., displays message that a member is in danger, makes sound, displays image of an individual in danger, vibrates, etc.) at step 44.”).
The motivation for combining references has been discussed in independent claim above.
Regarding claim 4, the method wherein the classification corresponds to a likelihood that the person is to perpetrate the predicted event (in addition to discussion above, Fu et al., col. 15 lines 59-col. 16 lines 16 teaches “Characterization of the behavior of the visitor may be performed and/or presented to home/business owners in real time. For example, categorizations of the behavior of visitors may comprise the behavior typical of potential burglars, solicitors, delivery workers, residents, domestic helpers, strangers, friendly visitors with and/or without access to the premises, etc. The number and/or type of behavior categorizations may be varied according to the design criteria of a particular implementation. Different types of behavior by a visitor may have a different impact on the type of visitor classification. For example, a visitor touching the doorknob may result in a small increase to an urgency level. In another example, a visitor detected at two different access points of the premises may result in a large increase to the urgency level (e.g., checking multiple entrances may indicate a burglar is trying to enter the premises). Notifications may be sent and/or other event responses may be initiated based on the urgency level. Heuristics may be used and/or assumptions may be made when monitoring behavior of a visitor. For example, if the visitor stays at the access point (e.g., the front door) for more than 10 seconds an assumption may be made that the visitor has rung the doorbell and/or knocked on the door. The heuristics used and/or the assumptions made may be used to adjust the urgency level.”, col. 17 lines 41-56).
Regarding claim 5, the method wherein the one or more characteristics of the person includes a frequency that the person is located within a distance of an object, and wherein determining that the frequency satisfies a frequency threshold, wherein the threshold comprises the frequency threshold (in addition to discussion above, Fu et al., col. 25 lines 1-5, col. 27 lines 21-30 teaches “In some embodiments, the smart security light 100 may be configured to deter package theft. For example, the “loiterer” detection described above may be modified to include “package detection”. For example, the smart security light 100 may monitor the package deposit 230. Once packages are detected at the door (e.g., a signal is provided by the wireless device 232), the status of the smart security light 100 may be set to a “package alarm” state to immediately sound an alarm/siren (e.g., using the speaker 174) when a person is detected near the packages.”, col. 14 lines 31-37, col. 17 lines 41-56).
Regarding claim 6, the method wherein receiving the set of inputs comprises: receiving, by the at least one processor, data from other devices within a geographic area in which the security system is located, wherein predicting the event is based at least in part on the received data (in addition to discussion above, Fu et al., col. 6 lines 36-48 teaches “If the network of trusted neighbors 202a-202n has the same system, they may exchange images, video, and/or other information of unwelcomed visitors. The website and/or web interface 216 may have the database 218 to manage the images, video, and/or other information. Unwelcome visitors stored in the database 218 may be shared with other neighbors and/or the authorities 214 using the web interface 216. For example, when the unwelcomed visitors learn about the database 218 they may not target the neighborhood 202a-202n. Data in the database 218 may be used to classify types of visitors (e.g., comparisons may be performed between the captured video data and information in the database 218).”).
Regarding claim 7, the method wherein performing the one or more security actions comprises:
identifying a setting of the security system (in addition to discussion above, Fu et al., col. 16 lines 1-16 teaches “Different types of behavior by a visitor may have a different impact on the type of visitor classification. For example, a visitor touching the doorknob may result in a small increase to an urgency level. In another example, a visitor detected at two different access points of the premises may result in a large increase to the urgency level (e.g., checking multiple entrances may indicate a burglar is trying to enter the premises). Notifications may be sent and/or other event responses may be initiated based on the urgency level. Heuristics may be used and/or assumptions may be made when monitoring behavior of a visitor. For example, if the visitor stays at the access point (e.g., the front door) for more than 10 seconds an assumption may be made that the visitor has rung the doorbell and/or knocked on the door. The heuristics used and/or the assumptions made may be used to adjust the urgency level.”);
emitting a sound based at least in part on the identified setting (in addition to discussion above, Fu et al., col. 17 lines 41-56 teaches “The user 252 may set up an option to automatically receive visitors at the door. In one example, the smart security light 100 may be configured to deter a potential burglar who is looking for an easy target home with no one inside the house 202. When the smart security light 100 classifies the visitor as a potential burglar the speaker 174 may announce “Hi Mary and Paul are busy. Please speak and leave a message with your phone number, you are being recorded by a monitoring service. Bye bye.” (e.g., play the pre-recorded security response). If the visitor is still detected after a pre-determined amount of time (e.g., 40 seconds), the additional security response may be activated (e.g., activating a siren for 20 seconds). If the visitor is still there after the siren, an automatic alarm signal may be sent (e.g., the notification security response) to a designated person to engage with the visitor (e.g., the authorities 214)”);
adjusting the sound based at least in part on the identified setting (in addition to discussion above, Fu et al., col. 17 lines 41-56 teaches “The user 252 may set up an option to automatically receive visitors at the door. In one example, the smart security light 100 may be configured to deter a potential burglar who is looking for an easy target home with no one inside the house 202. When the smart security light 100 classifies the visitor as a potential burglar the speaker 174 may announce “Hi Mary and Paul are busy. Please speak and leave a message with your phone number, you are being recorded by a monitoring service. Bye bye.” (e.g., play the pre-recorded security response). If the visitor is still detected after a pre-determined amount of time (e.g., 40 seconds), the additional security response may be activated (e.g., activating a siren for 20 seconds). If the visitor is still there after the siren, an automatic alarm signal may be sent (e.g., the notification security response) to a designated person to engage with the visitor (e.g., the authorities 214)”); and
emitting, at a second time prior to the predicted event, the adjusted sound (as discussed above).
Regarding claim 8, the method further comprising: identifying the person using an artificial intelligence algorithm, the one or more security actions selected based at least in part on the identified person (in addition to discussion above, Xu, paragraph 0082 teaches “The camera security system receives images of people and recognizes faces based on the setup of FIGS. 1-2 at step 32. The camera security system then monitors for a trigger at step 34. The trigger can comprise any appropriate trigger as described herein. If, at step 36, a trigger is not detected, the process proceeds to step 44 to monitor for a trigger. If, at step 36, a trigger is detected, a message is provided at step 38. The camera security system monitors for an indication of a notification, as described herein, at step 40. If, at step 42, it is determined that no indication of a notification has been received by the camera security system, the process proceeds to step 40. If, at step 42, it is determined that indication of a notification has been received, the camera security system renders an indication of the notification (e.g., displays message that a member is in danger, makes sound, displays image of an individual in danger, vibrates, etc.) at step 44.”).
The motivation for combining references has been discussed in independent claim above.
Regarding claim 9, the method further comprising: performing a first action of the one or more security actions (in addition to discussion above, Fu et al., col. 15 lines 59-col. 16 lines 16, col. 17 lines 41-col. 18 lines 13 teaches identified classification); updating the classification of the characteristics of the person; comparing the updated classification of the characteristics of the person to the threshold (in addition to discussion above, Fu et al., col. 15 lines 59-col. 16 lines 16, col. 17 lines 41-col. 18 lines 13 teaches wait few seconds after sending message and classified urgent level); in response to the updated classification of the characteristics of the person satisfying the threshold, updating the prediction of the event within the zone; and; performing a second action of the one or more security actions based at least in part on the updated prediction of the event within the zone (in addition to discussion above, Fu et al., col. 16 lines 66-col. 17 lines 12 teaches “The user 252 may select from several choices of pre-recorded messages for each type of visitor. The deterrence effect may be one or more of “nothing”, a comment in a “nice tone” and “several minutes of loud sirens” (e.g., an alarm). One example of a deterrence message for a potential burglar may be: “Hi Mary and Paul are busy. Please speak and leave a message with your phone number, you are being recorded by a monitoring service. Bye bye”. After the security response is performed, the smart security light 100 may monitor a reaction (e.g., a behavior) of the visitor to the security response. In an example, if the visitor is still in the area of interest after 40 seconds an additional security response may be performed (e.g., sound siren for 20 seconds).”, col. 17 lines 41-col. 18 lines 13).
Claim 10 is rejected for the same reason as discussed in the corresponding claim 1 above (in addition to discussion above, Fu et al., fig. 2 shows memory).
Claim 11 is rejected for the same reason as discussed in the corresponding claim 2 above.
Claim 12 is rejected for the same reason as discussed in the corresponding claim 3 above.
Claim 13 is rejected for the same reason as discussed in the corresponding claim 6 above.
Claim 14 is rejected for the same reason as discussed in the corresponding claim 8 above.
Claim 15 is rejected for the same reason as discussed in the corresponding claim 9 above.
Claim 16 is rejected for the same reason as discussed in the corresponding claim 1 above.
Claim 17 is rejected for the same reason as discussed in the corresponding claim 2 above.
Claim 18 is rejected for the same reason as discussed in the corresponding claim 3 above.
Claim 19 is rejected for the same reason as discussed in the corresponding claim 8 above.
Claim 20 is rejected for the same reason as discussed in the corresponding claim 9 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIGAR CHOWDHURY whose telephone number is (571)272-8890. The examiner can normally be reached Monday-Friday 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached on 571-272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NIGAR CHOWDHURY/Primary Examiner, Art Unit 2484
Prosecution Insights