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 in response to the Application No. 18/654,120 filed 05/03/2024. Claims 1-20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 3, 9-11, 14, 16 and 18-19 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 1, 9, 14, 18 and 19; recites the limitation, “computing device configured to ……,”.
Claims 3, 11 and 16; recites the limitation, “local device configured to ……,”.
Claims 14, 18 and 19; recites the limitation, “client device configured to ……,”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim(s) 1, 3, 9-11, 14, 16 and 18-19:
“computing device” (Fig. 2, #210; Fig. 7A, #700; Fig. 7B, #730; [0027] “At 104 a computing device (e.g., application server 210) may be provided, wherein the computing device is configured to generate a safety response based on threat event information. For example, in one or more embodiments, server 210 may run an instance of threat management software 222 that is configured to generate a safety response upon receiving threat event information from a camera device (e.g., camera 202)” and [0070] “Generally, system 730 may be any computer system sufficient to implement the features, functions and processes described herein in relation to FIGS. 1-6, including security cameras, CCTVs, handheld computers, mobile phones (e.g., iPhone®), laptop computers, tablet devices (e.g., iPad®), media players, personal digital assistants (PDA), server machines, desktop computers, wearable computers (e.g., Apple Watch®), or the like.” Wherein the computing device has sufficient structure associated with it such that it comprises an application server, mobile phone, laptop, etc.).
“local device” ([0024] “Camera devices in one or more of the embodiments herein may comprise one or more visions processors, GPUs, and/or CPUs (not shown) and associated memory, caches, storages and/or non-transitory computer-readable medium configured to run computer vision software, firmware and/or hardware sufficient to provide the functionality disclosed herein. Relatedly and/or additionally, each camera may be configured to run one or more trained object detection models and/or to be tightly linked with another device (e.g., a local processing or edge device linked with the camera, hereinafter referenced as local device) over one or more networks using one or more communication protocols.” And [0070] “Generally, system 730 may be any computer system sufficient to implement the features, functions and processes described herein in relation to FIGS. 1-6, including security cameras, CCTVs, handheld computers, mobile phones (e.g., iPhone®), laptop computers, tablet devices (e.g., iPad®), media players, personal digital assistants (PDA), server machines, desktop computers, wearable computers (e.g., Apple Watch®), or the like.” Wherein the local device has sufficient structure associated with it such that it comprises edge linked devices such as cameras, cctvs, hand held computer, mobile devices, etc.).
“client device” (Fig. 2, #216; [0021] “In some embodiments, system 200 may include one or more personal computing or client devices 216. In one or more embodiments, client device(s) 216, application server(s) 210, storage 212, database(s) 214 and/or camera device(s) 202 may be communicatively coupled via one or more network(s) 218.” Wherein the client device has sufficient structure associated with it such that it comprises devices such as computer laptops and mobile devices as depicted in Fig. 2).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent claims 1, 9 and 14 as well as their dependent claim(s) 2-8, 10-13 and 15-20 respectively:
Step 1 analysis: Claim 1 is directed to a method which falls within one of the four statutory categories.
Step 2A prong 1 analysis: Claim 1 recites, in part:
“camera is configured to detect a threat event in the safety zone”, “a computing device configured to generate a safety response based on threat event information”, “Detecting, by the camera, a first threat event; Generating, by the camera, a first set of threat event information based on the first threat event; Receiving, by the computing device, the first set of threat event information; Generating, by the computing device, a first safety response based on the first set of threat event information”
The limitations as shown above, as drafted, are processes that, under the broadest reasonable interpretation, cover the performance of the limitations in the mind which fall within the “Mental Process” grouping of abstract ideas. The limitations of:
“detect a threat event in the safety zone”, “generate a safety response based on threat event information”, “detecting... a first threat event”, “generating... a first set of threat event information based on the first threat event”, “receiving... the first threat event information”, “generating... a first safety response based on the first set of threat event information” recite steps that the human mind can perform through an observation and evaluation, such as the human mind can look at an area and through evaluation of objects and individuals in the area, determine if there is a threat event, such as, for example, a person hiding an undisclosed firearm in their jacket or backpack. Accordingly, the claim recites an abstract idea.
Step 2A prong 2 analysis: this judicial exception is not integrated into a practical application. In
particular, the claim recites the following additional element(s) –
“a camera”, “a computing device” and “initiating a safety response”
The step of “initiating a safety response is recited to merely constitute post-solution activities after a determination of a threat has been present. That is, the initiation of a safety response is recited at a high level of generality and merely produces a generic output after a determination. The limitation does not impose any limits on how initializing a response is executed or require any particulars that are used to produce such action. Additionally, the limitations performed by a “camera” and “computing device” are also recited at a high level of generality that amounts to no more than instructions to apply the judicial exception using a generic camera and a generic computer.
Step 2B analysis: the claims do not include additional elements that either alone nor in combination are sufficient to amount to significantly more than the judicial exception because the extent that, e.g., “camera” and “computing device” claimed is, generic, well-known, and conventional data gathering elements. As evidence that these are generic, well-known, and conventional data gathering computing elements, Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification need not to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). Furthermore, the claimed “camera” and “computing device”, as described in paragraph [0018], [0027] and [0070] discloses the following:
[0018] “Note that, unless context dictates otherwise, the terms “camera”, camera device,” and “video device” are used interchangeably herein to refer to generally any image or video capture device sufficient to provide the functionality described herein.”
[0027] “At 104 a computing device (e.g., application server 210) may be provided, wherein the computing device is configured to generate a safety response based on threat event information. For example, in one or more embodiments, server 210 may run an instance of threat management software 222 that is configured to generate a safety response upon receiving threat event information from a camera device (e.g., camera 202)” and [0070] “Generally, system 730 may be any computer system sufficient to implement the features, functions and processes described herein in relation to FIGS. 1-6, including security cameras, CCTVs, handheld computers, mobile phones (e.g., iPhone®), laptop computers, tablet devices (e.g., iPad®), media players, personal digital assistants (PDA), server machines, desktop computers, wearable computers (e.g., Apple Watch®), or the like.”
These elements are reasonably interpreted as generic computer and generic computer components which provides no details of anything beyond ubiquitous standard equipment. As such the claimed limitations of “a camera” and “a computing device” is reasonably understood as not providing anything significantly more and the claim(s) as a whole is not patent eligible under 101 analysis. Therefore, claim 1 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Independent claim 9 does not provide elements that overcome the 101 deficiencies as discussed in claim 1 as discussed above. Therefore, claim 9 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Independent claim 14 does not provide elements that overcome the 101 deficiencies as discussed in claim 1 as discussed above. Additionally, the claim recites a “client device” in which neither alone nor in combination are sufficient to amount to significantly more than the judicial exception because the extent that, e.g., a “client device” claimed is, generic, well-known, and conventional data gathering elements. A “client device” element is reasonably interpreted as generic computer and generic computer components which provides no details of anything beyond ubiquitous standard equipment. As such the claimed limitations of “a client device” is reasonably understood as not providing anything significantly more and the claim(s) as a whole is not patent eligible under 101 analysis. Therefore, claim 14 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Dependent claim(s) 2-8 and 10-13:
Claims 2 and 10, recite, in part, “wherein the camera is configured to detect a threat event and a safety context factor in the safety zone using a trained object detection model” which recites an abstract idea. The “detect a threat event and a safety context factor in the safety zone” recites steps that the human mind can perform through an observation and evaluation, such as the human mind can look at an area and through evaluation of objects and individuals in the area determine if there is a threat event, such as, for example, a person hiding an undisclosed weapon in their jacket or backpack and count the number of individuals containing a weapon. Accordingly, the claim recites an abstract idea. The additional element recited in the claim recites a “trained object detection model”. However, such limitation provides nothing more than mere instructions to implement the abstract idea on a generic computer. The “trained object detection model” is used to generally apply the abstract idea without limiting how the detection model functions. The detection model is described at a high level such that it amounts to using a computer with a generic detection model to apply the abstract idea. That is, the limitation as additional element, only recites the outcomes of a trained object detection model without any details about how the outcomes are accomplished and/or how it was trained. The claim as a whole does not integrate the judicial exception into a practical application. Therefore, claims 2 and 10 do not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claims 3 and 11, recite, in part, “wherein the camera comprises a linked local device, and wherein the local device is configured to detect a threat event and a safety context factor in the safety zone using a trained object model.” As explained above for claims 2 and 10, the recited claim recites an abstract idea and the additional element of “local device” provides nothing more than mere instructions to implement the abstract idea on a generic computer. The claim(s) as a whole do not integrate the judicial exception into a practical application. Therefore, claims 3 and 11 do not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claims 4, 5, 6, 12 and 13 do not recite additional elements that amount to significantly more to overcome the abstract idea, as the claims merely state data gathering at a high level of generality, such as obtaining images depicting weapons or provide an abstract idea (e.g., claim 12) in which the human mind can perform through an observation and evaluation as discussed in claims 2 and 10 above. Accordingly, these claims do not include additional elements within them to be indicative of an integration into a practical application or additional elements that amount to significantly more than the judicial exception. Therefore, Claims 4, 5, 6, 12 and 13 are not 35 USC 101 eligible under 101 analysis.
Claim 7 recites, in part, “wherein the first set of threat event information comprises an image depicting the first threat event, wherein the first safety response comprises a safety alert comprising the image, and wherein initiating the first safety response comprises sending the safety alert to a local law enforcement computing system or a local private security computing system” which provides nothing more than mere data gathering at a high level of generality, and thus are insignificant extra-solution activity and does not integrate the judicial exception into a practical application. That is, the claim recites post-solution activity after determining a first threat event and a first safety response. Therefore, claim 7 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim 8 recites, in part, “wherein generating a first safety response further comprises prompting at least one insight model to provide an insight on the first set of threat event information, and wherein the first safety response comprises a safety alert comprising the insight”. The claim includes an “insight model” which recites steps that the human mind can perform through an observation and evaluation, such as the human mind can look at an area and through evaluation of objects and individuals in the area determine if there is a threat event, such as, for example, a person hiding an undisclosed weapon in their jacket or backpack and count the number of individuals containing a weapon. The inclusion of using an “insight model” provides nothing more than mere instructions to implement the abstract idea on a generic computer as it is recited at a high level of generality. Accordingly, the claim does not include additional elements within them to be indicative of an integration into a practical application or additional elements that amount to significantly more than the judicial exception. Therefore, claim 8 is not 35 USC 101 eligible under 101 analysis.
Dependent claim(s) 15-20:
Claims 15 and 16 disclose identical limitations as previously discussed in claims 3 and 11. The claim(s) as a whole do not integrate the judicial exception into a practical application. Therefore, claims 15 and 16 do not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim 17 recites, in part, “wherein the first safety metric comprises a current threat index for the safety zone, an estimate of the number of concealed weapons currently present in the safety zone, or an insight generated by an insight model” which provides nothing more than mere data gathering at a high level of generality, and thus are insignificant extra-solution activity and does not integrate the judicial exception into a practical application. That is, the claim recites post-solution activity after determining a safety context factor. Therefore, claim 17 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim 18 recites, in part, “wherein the first safety metric comprises a natural language insight generated by a large language model, wherein the client device is further configured to receive user input” and “generating, by the computing device, a second safety metric comprising a second natural language insight generated by the large language model, wherein the generating comprises prompting the large language model with the first user input” which provides nothing more than mere instructions to implement the abstract idea on a generic computer. That is, the “natural langue insight generated by a large language model” step is recited at a high level of generality and merely produces an insight as a generic output after a determination. The limitation does not impose any limits on how the insight is produced or require any particular components that are used to produce the audible alert. As such the claimed limitations of “large language model” is reasonably understood as not providing anything significantly more and the claim(s) as a whole is not patent eligible under 101. Therefore, claim 18 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim 19 does not provide elements that overcome the deficiencies of the independent claim as the claim recites an abstract idea of “detecting a second safety context factor” steps that the human mind can perform through an observation and evaluation (as described above) and includes additional elements such as “computing device” and “client device” that provides nothing more than mere instructions to implement the abstract idea on a generic computer. The claim as a whole does not integrate the judicial exception into a practical application. Therefore, claim 189 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim 20 recites similar limitations as previously discussed in claim 17. The claim as a whole does not integrate the judicial exception into a practical application. Therefore, claim 20 does not comply with the requirements of 35 U.S.C. 101 under 101 analysis.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 9-11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schoeman (US 20220148397 A1).
Regarding claim 1, Schoeman discloses a method, comprising: Providing a camera proximate a safety zone, wherein the camera is configured to detect a threat event in the safety zone (“The one or more threat detection devices 144 may include or correspond to surveillance devices or sensors. For example, the one or more threat detection devices 144 may include cameras, microphones, etc.” Schoeman, [0054]); Providing a computing device configured to generate a safety response based on threat event information (“In some implementations, once event or alert criteria is met, an alarm and/or notifications are initiated and sent over an end-to-end encrypted, remote Virtual Private Network (VPN) or another type of secure connection to Law Enforcement and Law Enforcement is connected to on-site video cameras... Additionally, image data and text alert criteria can be sent to Law Enforcement units and designated contacts.” Schoeman, [0011]-[0012]); Detecting, by the camera, a first threat event (“The one or more threat detection devices 144 may include or correspond to surveillance devices or sensors. For example, the one or more threat detection devices 144 may include cameras, microphones, etc.” Schoeman, [0054]; “the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]); Generating, by the camera, a first set of threat event information based on the first threat event (“Once the match or threat is confirmed, the on-site system 602 can take action, either by receiving or generating commands locally or receiving commands via a secure connection over a network or cellular network, as described further with reference to FIG. 7. As illustrated in the example of FIG. 6, exemplary actions may include generating alerts and/or notifications. The alert message may include preset information identifying a type of an event, a threat level of the event, or both” Schoeman, [0142]); Receiving, by the computing device, the first set of threat event information (“The notification message may include information on the threat, a type of threat, a location of the threat, a response plan for the threat, a status of countermeasures and building security devices, etc., or a combination thereof. The notification or alert message can also be sent to mobile devices of one or more associates of the on-site system in addition to or in the alternative of sending such messages to off-site entities 504, 506” Schoeman, [0142]); Generating, by the computing device, a first safety response based on the first set of threat event information (“sending an alert to mobile devices of one or more associates of the on-site system 602 responsive to identifying the threat and the location of the threat, activating an alarm responsive to identifying the threat and the location of the threat, or both.” Schoeman, [0143]; “The alert message may include preset information identifying a type of an event, a threat level of the event, or both” Schoeman, [0142]); and Initiating the first safety response (“sending an alert to mobile devices of one or more associates of the on-site system 602 responsive to identifying the threat and the location of the threat, activating an alarm responsive to identifying the threat and the location of the threat, or both.” Schoeman, [0143]).
Regarding claim 2, Schoeman discloses the method of claim 1 wherein the camera is configured to detect a threat event in the safety zone using a trained object detection model (“Machine vision data 326 includes a machine vision program configured to recognize/classify faces and objects, thus performing facial recognition and object detection... To illustrate, the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]).
Regarding claim 3, Schoeman discloses the method of claim 1, wherein the camera comprises a linked local device (“As illustrated in FIG. 3, data link wires 308 couple the components of system 300 together. The data link wires 308 may be wired individually or maybe combined, such as routed in a local junction box before relay to on-site computer 302. In other implementations, one or more components of the system 300 are wirelessly coupled together.” Schoeman, [0092]), and wherein the local device is configured to detect a threat event in the safety zone using a trained object detection model (“On-site computer 302 may include or correspond to a central computer or central controller (e.g., 142) that is configured to control devices of system 300, such as first and second devices 304, 306” Schoeman, [0093]; “the on-site computer 302 may process video feed or images from both the first device 304 and the second device 306 simultaneously” wherein the on-site computer comprises the “machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]).
Regarding claim 4, Schoeman discloses the method of claim 1, wherein the threat event comprises a person holding a brandished weapon (“security threats can be identified and possibly neutralized before the threat does harm. To illustrate, a firearm may be detected on a person when the person brandishes the firearm (e.g., removes the firearm from a concealed area or bag) and before the person uses the firearm” Schoeman, [0007]).
Regarding claim 5, Schoeman discloses the method of claim 4, wherein the brandished weapon is a gun, a knife, or a club (“a firearm may be detected on a person when the person brandishes the firearm” Schoeman, [0007]).
Regarding claim 6, Schoeman discloses the method of claim 1, wherein the first safety response comprises a safety alert (“Additionally, image data and text alert criteria can be sent to Law Enforcement units and designated contacts.” Schoeman, [0012]).
Regarding claim 7, Schoeman discloses the method of claim 1, wherein the first set of threat event information comprises an image depicting the first threat event, wherein the first safety response comprises a safety alert comprising the image, and wherein initiating the first safety response comprises sending the safety alert to a local law enforcement computing system or a local private security computing system (“In some implementations, once event or alert criteria is met, an alarm and/or notifications are initiated and sent over an end-to-end encrypted, remote Virtual Private Network (VPN) or another type of secure connection to Law Enforcement and Law Enforcement is connected to on-site video cameras... Additionally, image data and text alert criteria can be sent to Law Enforcement units and designated contacts.” Schoeman, [0011]-[0012]).
Regarding claim 9, Schoeman discloses a method, comprising: Providing a camera proximate a safety zone, wherein the camera is configured to detect a threat event (“The one or more threat detection devices 144 may include or correspond to surveillance devices or sensors. For example, the one or more threat detection devices 144 may include cameras, microphones, etc.” Schoeman, [0054]) and a safety context factor in the safety zone (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]); Providing a computing device configured to generate a safety response based on threat event information (“In some implementations, once event or alert criteria is met, an alarm and/or notifications are initiated and sent over an end-to-end encrypted, remote Virtual Private Network (VPN) or another type of secure connection to Law Enforcement and Law Enforcement is connected to on-site video cameras... Additionally, image data and text alert criteria can be sent to Law Enforcement units and designated contacts.” Schoeman, [0011]-[0012]) and safety context factor information (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]);
Detecting, by the camera, a first threat event (“The one or more threat detection devices 144 may include or correspond to surveillance devices or sensors. For example, the one or more threat detection devices 144 may include cameras, microphones, etc.” Schoeman, [0054]; “the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]) and a first safety context factor (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]); Generating, by the camera, a first set of threat event information based on the first threat event (“Once the match or threat is confirmed, the on-site system 602 can take action, either by receiving or generating commands locally or receiving commands via a secure connection over a network or cellular network, as described further with reference to FIG. 7. As illustrated in the example of FIG. 6, exemplary actions may include generating alerts and/or notifications. The alert message may include preset information identifying a type of an event, a threat level of the event, or both” Schoeman, [0142]) and a first set of safety context factor information based on the first safety context factor (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]);
Receiving, by the computing device, the first set of threat event information (“The notification message may include information on the threat, a type of threat, a location of the threat, a response plan for the threat, a status of countermeasures and building security devices, etc., or a combination thereof. The notification or alert message can also be sent to mobile devices of one or more associates of the on-site system in addition to or in the alternative of sending such messages to off-site entities 504, 506” Schoeman, [0142]) and the first set of safety context factor information (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]; [0142]); Generating, by the computing device, a first safety response based on the first set of threat event information (“sending an alert to mobile devices of one or more associates of the on-site system 602 responsive to identifying the threat and the location of the threat, activating an alarm responsive to identifying the threat and the location of the threat, or both.” Schoeman, [0143]; “The alert message may include preset information identifying a type of an event, a threat level of the event, or both” Schoeman, [0142]) and the first set of safety context factor information (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]; [0142]); and Initiating the first safety response (“sending an alert to mobile devices of one or more associates of the on-site system 602 responsive to identifying the threat and the location of the threat, activating an alarm responsive to identifying the threat and the location of the threat, or both.” Schoeman, [0143]).
Regarding claim 10, Schoeman discloses the method of claim 9, wherein the camera is configured to detect a threat event and a safety context factor in the safety zone using a trained object detection model (“Machine vision data 326 includes a machine vision program configured to recognize/classify faces and objects, thus performing facial recognition and object detection... To illustrate, the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]; “The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]).
Regarding claim 11, Schoeman discloses the method of claim 9, wherein the camera comprises a linked local device (“As illustrated in FIG. 3, data link wires 308 couple the components of system 300 together. The data link wires 308 may be wired individually or maybe combined, such as routed in a local junction box before relay to on-site computer 302. In other implementations, one or more components of the system 300 are wirelessly coupled together.” Schoeman, [0092]), and wherein the local device is configured to detect a threat event and a safety context factor in the safety zone using a trained object model (“On-site computer 302 may include or correspond to a central computer or central controller (e.g., 142) that is configured to control devices of system 300, such as first and second devices 304, 306” Schoeman, [0093]; “the on-site computer 302 may process video feed or images from both the first device 304 and the second device 306 simultaneously” wherein the on-site computer comprises the “machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]; (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]).
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) 8 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schoeman in view of Barzilay (WO 2025191566 A1).
Regarding claim 8, Schoeman discloses all of the subject matter as described above except for specifically teaching wherein generating a first safety response further comprises prompting at least one insight model to provide an insight on the first set of threat event information, and wherein the first safety response comprises a safety alert comprising the insight. However, Barzilay in the same field of endeavor teaches wherein generating a first safety response further comprises prompting at least one insight model to provide an insight on the first set of threat event information, and wherein the first safety response comprises a safety alert comprising the insight (“A real-time, Al, threat detection and classification system is provided with a threat analysis system that applies deep learning algorithms to generate event-based output of real-time threat alerts” Barzilay, [0004]; “The threat analysis system 50 produces video analysis results, indicating the possible existence and estimated location of threats. Threats that may be detected by the threat analysis system, may include weapons (e.g., firearms, knives, etc.), as well as physical actions suggesting threats or violence (e.g., hitting, threatening, aiming and/or firing a weapon, aggressive stance/posture). Profiling of possible assailants may also be performed (age, gender, etc.), as well as identification of individuals. In some examples, clothing may also be a parameter triggering threat events, such as presence of army personnel, police officers, paramedics, etc., or outfits indicative of terrorist affiliation” Barzilay, [0017]).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Schoeman and Barzilay before the effective filing date of the claimed invention. The motivation for this combination of references would have been to generate event-based analysis of real-time threats detected in video recordings (Barzilay, [0004]). This motivation for the combination of Schoeman and Barzilay is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 12, Schoeman and Barzilay disclose the method of claim 9, wherein the threat event comprises a person holding a brandished weapon (“security threats can be identified and possibly neutralized before the threat does harm. To illustrate, a firearm may be detected on a person when the person brandishes the firearm (e.g., removes the firearm from a concealed area or bag) and before the person uses the firearm” Schoeman, [0007]) and the safety context factor comprises the number of people in the safety zone at the time of the threat event or concealed weapon indicators (“The threat analysis system 50 produces video analysis results, indicating the possible existence and estimated location of threats. Threats that may be detected by the threat analysis system, may include weapons (e.g., firearms, knives, etc.), as well as physical actions suggesting threats or violence (e.g., hitting, threatening, aiming and/or firing a weapon, aggressive stance/posture). Profiling of possible assailants may also be performed (age, gender, etc.), as well as identification of individuals” Barzilay, [0017]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Regarding claim 13, Schoeman and Barzilay disclose the method of claim 12, wherein the brandished weapon is a gun, a knife, or a club (“a firearm may be detected on a person when the person brandishes the firearm” Schoeman, [0007]).
Regarding claim 14, Schoeman and Barzilay disclose a method comprising: Providing a camera proximate a safety zone (“The one or more threat detection devices 144 may include or correspond to surveillance devices or sensors. For example, the one or more threat detection devices 144 may include cameras, microphones, etc.” Schoeman, [0054]) configured to detect a safety context factor in the safety zone (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]); Providing a computing device configured to generate a safety metric based on safety context factor update information (“The analysis may include computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert... The scoring method may also be configured to detect multiple threats and to weight the combinations to form a threat score (e.g., three suspicious people and two possible weapons gives an aggregate score that is the sum of the individual threat scores, or may be calculated by an alternative function to give a higher threat score)” Barzilay, [0021]); Providing a client device configured to receive and display a safety metric (“The “monitoring” client or web application of the control room interface 60 provides an interface for the operators that typically shows both a map of a given area under surveillance, as well as live video streams, streamed from the data center interface, or directly from the video upload interfaces. The interface may include real-time prioritization of video streams, real-time prioritization of alerts, and display of alert locations, together with corresponding video streams” Barzilay, [0024] wherein the safety metric is the “computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert. Alerts (also referred to herein as “alert events”) may be categorized into several levels of priority, depending on the threat score and the type of threat detected” Barzilay, [0020]); Detecting, by the camera, a first safety context factor (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]); Generating, by the camera, a first set of safety context factor update information based on the first safety context factor (“issuing the alert includes providing a message indicating a description of a potential threat, containing one or more of event details, timestamps, a source and type of the alert, and additional images, videos, GIFs” Barzilay, [0098]); Receiving, by the computing device, the first set of safety context factor update information (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]; [0142]); Generating, by the computing device (Schoeman, [0085]; [0142]), a first safety metric (“computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert. Alerts (also referred to herein as “alert events”) may be categorized into several levels of priority, depending on the threat score and the type of threat detected” Barzilay, [0020]); Receiving, by the client device (“mobile devices” Schoeman, [0086]), the first safety metric (Barzilay, [0020]); and Displaying, by the client device, the first safety metric (wherein the safety metric is the “computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert. Alerts (also referred to herein as “alert events”) may be categorized into several levels of priority, depending on the threat score and the type of threat detected” Barzilay. [0020]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Regarding claim 15, Schoeman and Barzilay disclose the method of claim 14, wherein the camera is configured to detect a safety context factor in the safety zone using a trained object detection model (“Machine vision data 326 includes a machine vision program configured to recognize/classify faces and objects, thus performing facial recognition and object detection... To illustrate, the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]; “The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]).
Regarding claim 16, Schoeman and Barzilay disclose the method of claim 14, wherein the camera comprises a linked local device (“As illustrated in FIG. 3, data link wires 308 couple the components of system 300 together. The data link wires 308 may be wired individually or maybe combined, such as routed in a local junction box before relay to on-site computer 302. In other implementations, one or more components of the system 300 are wirelessly coupled together.” Schoeman, [0092]), and wherein the local device is configured to detect a safety context factor in the safety zone using a trained object model (“On-site computer 302 may include or correspond to a central computer or central controller (e.g., 142) that is configured to control devices of system 300, such as first and second devices 304, 306” Schoeman, [0093]; “the on-site computer 302 may process video feed or images from both the first device 304 and the second device 306 simultaneously” wherein the on-site computer comprises the “machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack” Schoeman, [0098]; (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]).
Regarding claim 17, Schoeman and Barzilay disclose the method of claim 14, wherein the first safety metric comprises a current threat index for the safety zone, an estimate of the number of concealed weapons currently present in the safety zone, or an insight generated by an insight model (“The analysis may include computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert... The scoring method may also be configured to detect multiple threats and to weight the combinations to form a threat score (e.g., three suspicious people and two possible weapons gives an aggregate score that is the sum of the individual threat scores, or may be calculated by an alternative function to give a higher threat score)” Barzilay, [0021]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Regarding claim 18, Schoeman and Barzilay disclose the method of claim 14, wherein the first safety metric (Barzilay, [0021]) comprises a natural language insight generated by a large language model (“The Al agents 206 may each include one or more of various types and architectures of Al models such as:... Large Language Models (LLMs)” Barzilay, [0034]), wherein the client device is further configured to receive user input (A reverse proxy may be used to manage API requests and video stream routing” Barzilay, [0090]; i.e., the client device is that of a web application service that may receive user input/request), and further comprising: Receiving, by the client device (“mobile devices (e.g., 262-266) associated with on-site staff or a category of on-site staff (e.g., trusted staff, managers, teachers, etc.) are configured to send and receive message” Schoeman, [0086]), a first user input requesting additional insight (“The LLM request may include details on the available edge and cloud devices, with constant updates at each call to provide the most up-to-date information.” Barzilay, [0046]; i.e., a user may request a more updated analyzed insight with respect to the threat(s));
Receiving, by the computing device, the first user input requesting additional insight (“The LLM request may include details on the available edge and cloud devices, with constant updates at each call to provide the most up-to-date information.” Barzilay, [0046]; i.e., a user may request a more updated analyzed insight with respect to the threat(s)); Generating, by the computing device, a second safety metric comprising a second natural language insight generated by the large language model, wherein the generating comprises prompting the large language model with the first user input (“processing each video stream by the multiple Al agents to generate one or more indications of potential threats, wherein each indication is associated with one or more cameras and an estimated location of each potential threat. The method includes processing the one or more indications by large language model (LLM) configured to determine an inference plan for processing the multiple respective video streams by an alternate set of Al agents, including alternate early inference options. The method includes applying the inference plan to process the multiple respective video streams to generate a revised indication of a potential threat. The method includes responsively issuing an alert including the revised indication” Barzilay, [0096]; “The LLM request may include details on the available edge and cloud devices, with constant updates at each call to provide the most up-to-date information.” Barzilay, [0046]); Receiving, by the client device (Schoeman, [0086]), the second safety metric (“a large language model (LLM) configured to determine an inference plan for processing the multiple respective video streams by an alternate set of Al agents, including alternate early inference options. The method includes applying the inference plan to process the multiple respective video streams to generate a revised indication of a potential threat. The method includes responsively issuing an alert including the revised indication” Barzilay, [0096]; “The LLM request may include details on the available edge and cloud devices, with constant updates at each call to provide the most up-to-date information.” Barzilay, [0046]; and Displaying, by the client device (Schoeman, [0086]), the second safety metric (Barzilay, [0096]; [0046]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Regarding claim 19, Schoeman and Barzilay disclose the method of claim 14, further comprising: Detecting, by the camera, a second safety context factor (“the machine vision program is configured to process received images through an AI model 352, trained on library data to identify weapons, i.e., a firearm in a person's hand or an outline of a firearm in a person's coat or backpack. Accordingly, identification of the threat occurs before harm or damage” Schoeman, [0098]);
Generating, by the camera, a second set of safety context factor update information based on the second safety context factor (“issuing the alert includes providing a message indicating a description of a potential threat, containing one or more of event details, timestamps, a source and type of the alert, and additional images, videos, GIFs” Barzilay, [0098]); Receiving, by the computing device, the second set of safety context factor update information (“The on-site system 212 may be configured to push different messages and data to different users based on an access level of the user or class or users, a type of threat, a threat level, an identity of the threat, or a combination thereof. As illustrative examples, the on-site system 212 may push image data, map data, threat data, route data, instructions, alerts, notifications, etc. to devices associated with the users” Schoeman, [0085]);
Generating, by the computing device (“on-site system” Schoeman, [0085]; [0142]), a second safety metric (“computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert. Alerts (also referred to herein as “alert events”) may be categorized into several levels of priority, depending on the threat score and the type of threat detected” Barzilay, [0020]); Receiving, by the client device (“mobile devices (e.g., 262-266) associated with on-site staff or a category of on-site staff (e.g., trusted staff, managers, teachers, etc.) are configured to send and receive message” Schoeman, [0086]), the second safety metric (Barzilay, [0020]); and Displaying, by the client device (“mobile devices” Schoeman, [0086]), the second safety metric (Barzilay, [0020]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Regarding claim 20, Schoeman and Barzilay disclose the method of claim 19, wherein the second safety metric comprises a current threat index for the safety zone, an estimate of the number of concealed weapons currently present in the safety zone, or a natural language insight generated by a large language model (“computation of a real-time “global threat score” for a set of cameras or per individual camera, for example, as a number between 0 and 1. Different artificial intelligence (Al) agents may run to process each video stream, each producing a threat score and a connected alert. Alerts (also referred to herein as “alert events”) may be categorized into several levels of priority, depending on the threat score and the type of threat detected” Barzilay, [0020]; “a large language model (LLM) configured to determine an inference plan for processing the multiple respective video streams by an alternate set of Al agents, including alternate early inference options. The method includes applying the inference plan to process the multiple respective video streams to generate a revised indication of a potential threat. The method includes responsively issuing an alert including the revised indication” Barzilay, [0096]).
Therefore, combining Schoeman and Barzilay would meet the claim limitations for the same reasons as previously discussed in claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210063120 A1 discloses a security system which senses and detects physical threat to persons in a protected area.
US 20180165934 A1 discloses an automated object and activity tracking in a live video feed by identifying objects and activities present and generates natural language text and machine learning that describes the video feed.
US 20180314897 A1 discloses a surveillance system that flags potential threats automatically and alerts security personnel.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMANUEL SILVA-AVINA whose telephone number is (571)270-0729. The examiner can normally be reached Monday - Friday 11 AM - 8 PM EST.
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, Chineyere Wills-Burns can be reached at (571) 272-9752. 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.
/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673