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 .
Status of Application
The following is a Final Office Action. In response to Examiner's communication on 08/12/2025, Applicant on 12/10/2025, amended Claims 1 and 17. Claims 1-20 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicants’ amendments render moot the 35 USC 103 rejections set forth in the previous action. Therefore, new grounds of rejection have been brought forth to address amendments and outlined below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant argues that even if the claims involve an abstract idea, which Applicant disputes, the claims are integrated into a practical application and additionally represent significantly more per Step 2B of the analysis because while some steps may arguably recite an abstract idea, in view of the ordered combination of the steps of applying cost considerations to interfacing with a video analytics system in an inventive manner, the claim as a whole is directed to an improvement to large scale video surveillance systems. Examiner respectfully disagrees.
Pursuant to MPEP 2106, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. MPEP 2106.04.
Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. MPEP 2106.05.
That is, only after determining whether the claims recite limitations that, individually or in combination, that fall within one of the enumerated subject matter groups of abstract ideas in the first prong of Step 2B, under the second prong of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. However, the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, for the reason detailed in the following paragraphs, the limitations referred to by Applicant are part of and directed to the recited abstract idea because they are recitations of mental
processes that can be practically performed mentally and merely use generic computer components as a tool (i.e., “an electronic computing device”) to implement the mental processes.
As set forth in the MPEP, mere automation of a manual or mental process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Merely requiring that the claims use generic computer components, such as an electronic computing device, to implement the recited abstract idea does not make the claims directed to an improvement in technology or otherwise transform the abstract idea into a patent eligible invention.
The steps referred to by Applicant do not recite a significant improvement in technology, but rather, the steps referred to by Applicant are recitation of mental processes that can be practically performed mentally and merely use a generic computer components as a tool (i.e., an “electronic computing device” in Claim 1) to implement the mental process. In fact, aside from the generic component used as a tool to implement the steps, the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but, as noted above, they are recitations of mental processes that recite an abstract idea.
Viewing the limitations in combination per the pen and paper test recited in MPEP 2106.04(a)(2)(iii), a human can mentally access historical data, mentally observe the source of the data from associated logs, mentally calculate an average cost for distinct types of incidents by aid of pen and paper, and mentally perform a judgement as to an optimal path forward.
In combination, these steps do not reflect an improvement in computer technology, but rather a mental process of deciding how to optimize operating costs during the management of a system.
As detailed below with respect to the second prong of Step 2A, the recited abstract idea is not integrated into a practical Application because the additional elements beyond the recited abstract idea merely use generic computer components as a tool to apply the recited abstract idea.
As set forth in the MPEP, mere automation of a manual or mental process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Merely requiring that the claims use generic computer components, such as the generically recited machine learning model, to implement the recited abstract idea does not make the claims directed to an improvement in technology or otherwise transform the abstract idea into a patent eligible invention.
Like in Electric Power Group, the claims are not focused on a specific improvement in computers, but on certain independently abstract ideas that simply use computers as tools. Electric Power Group, LLC v. Alstom S.A,, et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Further, as the heart of the claims lies with performing a historical cost analysis and encompass potentially directing human behavior on its basis, these ideas encompass a Certain Method of Organizing Human Activity, namely a Fundamental Economic Practice and means of Managing Personal Behavior or Relationships or Interactions Between People.
While Examiner agrees with Applicant’s assertion that the claims do not “preempt all ways of estimating or comparing costs”, the breadth of the “video surveillance system” renders it ineligible under the “Apply It” criteria - following the rationale outlined in MPEP 2106.05(f), Mere Instructions to Apply An Exception, broadly enabling such a generic computing system cannot be considered sufficient to integrate the recited abstract ideas or amount to significantly more.
Response to Arguments – 35 USC § 103
Applicant' s arguments with respect to the rejection of Claims 1-20 under 35 USC 103 have been considered but are moot in light of new grounds of rejections necessitated by applicant’s amendments.
Applicant’s first and second assertion, on pages 4-5 of Remarks filed 12/10/2025, that the references of record do not teach claims as currently amended is rendered moot by the new grounds of rejection necessitated by Applicant’s amendments. Examiner respectfully points to the updated rejections below.
Applicant further argues that the cited portion of Schuler does not teach the comparison of average costs. Examiner notes that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. The test for obviousness is not that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As stated in the preceding Non-Final Rejection, we apply the computation of average costs as found in Col 14 Lines 14-21 of Russo, and combine that with the comparison of thresholds that involve cost as found in [0109] of Schuler.
Applicant finally argues that Schuler fails to teach a determination of whether the video analytics system is disabled. The broadest reasonable interpretation of “disabled” is not necessarily a characteristic of the hardware being physically powered on or off, it also encompasses an inactive state where a task has not been assigned to it. The comparison to a threshold is outlined in [0108] of Schuler, with [0107] providing for assignment on its basis.
Accordingly, rejections have been updated to address the amendments and maintained below.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to a method and apparatus. Therefore, the claims are directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A method of selectively enabling execution of video analytics on videos captured by cameras, the method comprising: accessing, at an electronic computing device, an incident database identifying incidents resolved by one or more agencies, determining, at the electronic computing device, from a stored electronic record associated with each incident resolved by the one or more agencies, whether the incident were first reported to the one or more agencies by a human source or by a video analytics system that is configured to execute video analytics on videos captured by one or more cameras, and responsively selecting, from the incidents resolved by the one or more agencies, a first set of incidents that were first reported to the one or more agencies by the human source and a second set of incidents that were first reported to the one or more agencies by the video analytics system estimating, at the electronic computing device, a first average cost incurred in resolving the first set of incidents; estimating, at the electronic computing device, a second average cost incurred in resolving the second set of incidents; determining, at the electronic computing device, whether the first average cost is higher than the second average cost by at least a predefined threshold; and determining, at the electronic computing device, that the video analytics system is currently disabled from executing video analytics on videos captured by the one or more cameras and responsively enabling the video analytics system to execute video analytics on videos captured by the one or more cameras to proactively detect and report incidents when the first average cost is higher than the second average cost by at least the predefined threshold.
The examiner submits that the foregoing bolded limitations constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a mental process. Accordingly, the claim recites at least one abstract idea.
Independent Claim 17 recites an abstract idea by analogous reasoning.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method of selectively enabling execution of video analytics on videos captured by cameras, the method comprising: accessing, at an electronic computing device, an incident database identifying incidents resolved by one or more agencies, determining, at the electronic computing device, from a stored electronic record associated with each incident resolved by the one or more agencies, whether the incident were first reported to the one or more agencies by a human source or by a video analytics system that is configured to execute video analytics on videos captured by one or more cameras, and responsively selecting, from the incidents resolved by the one or more agencies, a first set of incidents that were first reported to the one or more agencies by the human source and a second set of incidents that were first reported to the one or more agencies by the video analytics system estimating, at the electronic computing device, a first average cost incurred in resolving the first set of incidents; estimating, at the electronic computing device, a second average cost incurred in resolving the second set of incidents; determining, at the electronic computing device, whether the first average cost is higher than the second average cost by at least a predefined threshold; and determining, at the electronic computing device, that the video analytics system is currently disabled from executing video analytics on videos captured by the one or more cameras and responsively enabling the video analytics system to execute video analytics on videos captured by the one or more cameras to proactively detect and report incidents when the first average cost is higher than the second average cost by at least the predefined threshold.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
As it pertains to Claim 1, the additional elements in the claims include “A method of selectively enabling execution of video analytics on videos captured by cameras”, “database,” “determining…that the video analytics system is currently disabled from executing video analytics on videos captured by the one or more cameras and responsively enabling the video analytics system to execute video analytics on videos captured by the one or more cameras”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claim 17 additionally recites “an electronic computing device”, “a communications interface”, an “electronic processor”. These additional limitations do not integrate the abstract idea into a practical application by analogous reasoning.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to generic computing components that are merely used
as a tool to perform the recited abstract idea and/or do no more than
generally link the use of the recited abstract idea to a particular
technological environment or field of use. Further, looking at the additional
elements as an ordered combination adds nothing that is not already
present when considering the additional elements individually.
Claims 17 is rejected as disclosing substantially similar limitations as Claim 1.
Claims 4, 8,16, 19 recite additional limitations which merely further limit the abstract ideas of Claim 1, and are therefore ineligible.
Additional limitations disclosed are as follows:
Claim 4 recites “disable the video analytics system”.
Claim 8 recites “updating the database…”.
Claim 16 recites “presenting an electronic notification…with a recommendation”.
Claim 19 recites “disable the video analytics system”.
For analogous reasoning as above, Claims 4,8,16,19 do not integrate the abstract ideas recited into a particular application per Step 2A Prong II or amount to significantly more under Step 2B.
Claims 2-3, 5-7, 9-15, 17-18, 20 do not recite any additional elements beyond those recited in the claims from which they depend, and as a result, Claims 18 and 20 do not include any additional elements that either integrate under Step 2A Prong II or amount to significantly more under Step 2B.
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.
Claims 1-5, 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schuler(US 20220207983 A1) in view of Shi(US 20230098165 A1) in further view of Russo(US 11503101 B1).
Claims 1,17
As to Claim 1,
Schuler teaches:
A method of selectively enabling execution of video analytics on videos captured by cameras, the method comprising: accessing, at an electronic computing device, an incident database identifying incidents resolved by one or more agencies;
In [0024], "As depicted, the computing device 102 may have access to at least one memory 114, depicted in the form of a database (though the at least one memory 114 may be in any suitable format), storing records 116 and sensor records 118". In [0043], "The records 116 may comprise any suitable records stored in any number of suitable memories and/or databases that may be used to generate alerts, such as the alert 120. For example, records 116 may comprise any suitable combination of one or more of historical records, public-safety records, Criminal Justice Information Services (CJIS) record, computer-aided dispatch (CAD) records, legal records, records of retired alerts, among other possibilities".
… record associated with each incident resolved by the one or more agencies, whether the incident were first reported to the one or more agencies by a human source
Pertaining to the first set of incidents, we understand the records 116 to encompass such human-reported incidents, such as through humans contacting emergency services, in [0043], "The records 116 may comprise any suitable records stored in any number of suitable memories and/or databases that may be used to generate alerts, such as the alert 120. For example, records 116 may comprise any suitable combination of one or more of historical records, public-safety records, Criminal Justice Information Services (CJIS) record, computer-aided dispatch (CAD) records, legal records, records of retired alerts, among other possibilities".
or by a video analytics system that is configured to execute video analytics on videos captured by one or more cameras,
In [0029], "For example, sensor data from the sensors 106 may comprise any suitable combination of video data and audio data and hence the sensor analytics engine 104 may comprise one or more of a video analytics engine and an audio analytics engine; a video analytics engine may search images and/or video from the sensors 106 for target objects, and similarly, an audio analytics engine may search audio (e.g., which may be incorporated into video) from the sensors 106 for target objects". Note that we construe the video analytics engine to encompass both the sensors and the analytical engine that performs analysis based on the data received from the sensors. As this data is used in the context of public safety, we understand this to disclose the idea of incident reporting; as a non-limiting example, an incident could be a sighting of a missing person. In [0011], " In particular provided herein is a computing device, which may receive or generate an alert to perform a search for a target object; such an alert may include, but is not limited to, a one or more of a public-safety alert, a be-on-the-lookout (BOLO), an all-points bulletin (APB), a silver alert, a gold alert, an amber alert, and the like".
… the incidents resolved by the one or more agencies, a first set of incidents that were first reported to the one or more agencies by the human source and a second set of incidents that were first reported to the one or more agencies by the video analytics system;
Pertaining to the first set of incidents, we understand the records 116 to encompass such human-reported incidents, such as through humans contacting emergency services, in [0043], "The records 116 may comprise any suitable records stored in any number of suitable memories and/or databases that may be used to generate alerts, such as the alert 120. For example, records 116 may comprise any suitable combination of one or more of historical records, public-safety records, Criminal Justice Information Services (CJIS) record, computer-aided dispatch (CAD) records, legal records, records of retired alerts, among other possibilities".
… cost incurred in resolving the first set of incidents … cost incurred in resolving the second set of incidents
In [0108], “For example, criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object, may include one or more of the following criteria, with each criteria assigned a score and/or weight with regards to cost and/or chance of search success and/or importance, and, in some examples, the scores and/or weights may be assigned based whether a search might be performed by the sensor analytics engine 104 or by humans”.
determining, at the electronic computing device, whether the first...is higher than the second average cost by at least a predefined threshold;
In [0109], "The sensors 106 meeting (or not meeting) a threshold condition 224, which may be assigned a relatively low cost core whether, or not the sensors 106 meeting (or not meeting) a threshold condition 224. However, the sensors 106 meeting a threshold condition 224 may be assigned a relatively higher success score relative to the sensors 106 not meeting a threshold condition 224. An importance score may not be assigned to the sensors 106 meeting (or not meeting) a threshold condition 224, regardless of outcome".
and determining, at the electronic computing device, that the video analytics system is currently disabled from executing video analytics on videos captured by the one or more cameras and responsively enabling the video analytics system to execute video analytics on videos captured by the one or more cameras to proactively detect and report incidents when the...is higher than the second...by at least the predefined threshold.
Understanding the enabled/disabled status of the sensor to correspond to whether or not a query is directed to the sensor, in [0108], "For example, criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object, may include one or more of the following criteria, with each criteria assigned a score and/or weight with regards to cost and/or chance of search success and/or importance, and, in some examples, the scores and/or weights may be assigned based whether a search might be performed by the sensor analytics engine 104 or by humans". See [0107] for clarification as to how this score subsequently is used for routing, “Put another way, such criteria (e.g., which may include, but is not limited to, the sensors 106 meeting (or not meeting) a threshold condition 224) may be used to determine whether to assign the alert 120 to the sensor analytics engine 104 to search for the target object using the sensors 106, and/or to provide the alert 120 to one or more of the communication devices 110 to initiate a human-based search for the target object”.
Schuler does not expressly disclose the remaining limitations.
However, Shi teaches:
determining, at the electronic computing device, from a stored electronic record associated with each incident …, whether the incident were first reported to …source
In [0047], “Flowchart 300 of FIG. 3 begins with step 302. In step 302, information about a first alert is received...As noted above, alert information 110 may comprise alert attributes (or alert metadata) such as an alert identifier (ID), a time associated with the alert, an ID associated with a source of the alert, a create date of the source, an owner of the source, a problem ID, an ID of a source device, an ID of a reporting device, an identifier of an environment or datacenter where a problem occurred, a root cause indication, an incident ID, a parent incident ID, a security impact indicator, a severity level, a customer ID, etc”. In [0048], “In step 304, an alert matching correlation rule may be retrieved based on the information about the first alert… Correlation rules 134 may be based on domain knowledge around the alert source system (e.g., cloud service system) or system 100, or any other suitable knowledge available to users. In some embodiments, a correlation rule may refer to information in data fields of a received alert (e.g., alert information 110) and/or of information related to an existing incident record. For example, the correlation rules may apply to attributes or metadata of received alert information 110 and/or information in existing incident records. Correlation rules may comprise data corresponding to, for example, a source environment type…”
Shi discloses a system directed to managing alerts and monitoring. Schuler discloses a system meant to assign alerts corresponding to incidents to either a sensor and video analytics engine or to human sources. Each reference discloses means for dynamic monitoring and alert management. Extending the alert management of Shi is applicable to Schuler as they are both concerned with the same field of endeavor of monitoring and alert management.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the alert management and analysis of Shi and integrate that into Schuler. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that said alert logging and management would streamline the performance of analytics on the alert data.
Schuler combined with Shi does not expressly disclose the remaining limitations.
However, Russo teaches:
responsively selecting, from the incidents resolved … a first set of incidents … and a second set of incidents…; estimating, at the electronic computing device, a first average cost incurred in resolving the first set of incidents; estimating, at the electronic computing device, a second average cost incurred in resolving the second set of incidents;
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed. Note that we consider the selection of distinct sets to be implicitly performed by the computation of an average cost of relevant tasks; there must be some selection of a set to perform computations on its basis.
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
Russo discloses a system for assigning video analytics tasks for computing devices. Schuler combined with Shi discloses a system meant to assign alerts corresponding to incidents to either a sensor and video analytics engine or to human sources. Each reference discloses means for managing the delegation of analytical tasks. Extending the computations of Russo to Schuler combined with Shi is applicable as they are both concerned with the same task.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler combined with Shi. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the computations performed by Russo would enable users of the system of Schuler combined with Shi to refine their analysis and enhance the granularity of the calculation of criteria that serves as the basis for where to assign tasks.
Claim 17 is rejected as presenting substantially similar limitations as Claim 1.
Claim 17 additionally recites “an electronic computing device”, and “a communications interface”, and an “electronic processor communicatively coupled to the communications interface”, but this is taught in [0064] of Schuler, “As depicted, the computing device 102 comprises: a communication unit 202, a processing unit 204, a Random-Access Memory (RAM) 206, one or more wireless transceivers 208 (which may be optional), one or more wired and/or wireless input/output (I/O) interfaces 210, a combined modulator/demodulator 212, a code Read Only Memory (ROM) 214, a common data and address bus 216, a controller 218, and a static memory 220 storing at least one application 222. Hereafter, the at least one application 222 will be interchangeably referred to as the application 222. Furthermore, while the memories 206, 214 are depicted as having a particular structure and/or configuration, (e.g., separate RAM 206 and ROM 214), memory of the computing device 102 may have any suitable structure and/or configuration”.
Claims 2, 18
As to Claim 2, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein when the first… is higher than the second average cost by at least the predefined threshold,
In [0106], " In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
to enable the video analytics system to execute video analytics on videos captured by the one or more cameras to proactively detect and report incidents.
In keeping with our understanding of the enabled/disabled status of the sensor analytics system as corresponding to whether a task has been assigned to it, in [0105], "Indeed, the controller 218 and/or the computing device 102 may assign any retired alert to the sensor analytics engine 104 to search for the target object using the sensors 106. Furthermore, the alert 120, as generated and/or retrieved at the block 302, may comprise a retired alert, which is evaluated by the controller 218 and/or the computing device 102 for assignment to the sensor analytics engine 104 and/or for revival of another human-based search due, for example, to new evidence being found in a court case and/or a new sighting of the target object in the geographic area 108, or another geographic area". In this case, the retired alert that was previously reported by the sensor analytics engine has been rerouted to the analytics engine, with the average cost of Russo substituted into the threshold criteria.
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
the method further comprising: determining that the video analytics system is currently enabled to execute video analytics on videos captured by the one or more cameras and responsively continuing
In Col 13 Lines 8-18, "As an example, the edge computing devices 120 may have certain limitations such as power limits due to the type of power source (e.g., battery, power over ethernet) used by the edge computing devices 120 or limitations based on thermal or cooling capacity. When the device capabilities indicates that the edge computing devices 120 will not have enough power or cooling capacity to execute the video analytics task determined at block 320 in addition to other video analytics tasks that the edge computing devices 120 may be expected to concurrently perform at the same time". We understand the ability to see what tasks computing devices are assigned at a given time to analogize to a determination of whether or not the video analytics system is enabled in Russo.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 18 is rejected as presenting substantially similar limitations as Claim 2.
Claim 3
As to Claim 3, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein when the first…is not higher than the second...by at least the predefined threshold
In [0106], " In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
and responsively disabling the video analytics system from executing video analytics on videos captured by the one or more cameras.
In keeping with our understanding of the disabled/enabled status as whether an alert is assigned to the video analytics system of Schuler in [0106], " In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
the method further comprising: determining that the video analytics system is currently enabled to execute video analytics on videos captured by the one or more cameras
In Col 13 Lines 8-18, "As an example, the edge computing devices 120 may have certain limitations such as power limits due to the type of power source (e.g., battery, power over ethernet) used by the edge computing devices 120 or limitations based on thermal or cooling capacity. When the device capabilities indicates that the edge computing devices 120 will not have enough power or cooling capacity to execute the video analytics task determined at block 320 in addition to other video analytics tasks that the edge computing devices 120 may be expected to concurrently perform at the same time".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 4, 19
As to Claim 4, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein when the first …is not higher than the second..by at least the predefined threshold,
In [0106], " In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
the method further comprising: determining that the video analytics system is currently disabled to execute video analytics on videos captured by the one or more cameras and responsively continuing to disable the video analytics system from executing video analytics on videos captured by the one or more cameras.
In keeping with our understanding of the enabled/disabled status of the sensor analytics system as corresponding to whether a task has been assigned to it, in [0105], "Indeed, the controller 218 and/or the computing device 102 may assign any retired alert to the sensor analytics engine 104 to search for the target object using the sensors 106. Furthermore, the alert 120, as generated and/or retrieved at the block 302, may comprise a retired alert, which is evaluated by the controller 218 and/or the computing device 102 for assignment to the sensor analytics engine 104 and/or for revival of another human-based search due, for example, to new evidence being found in a court case and/or a new sighting of the target object in the geographic area 108, or another geographic area". In this case, the retired alert that was previously reported by a human based search has been rerouted to a human-based search, with the average cost of Russo substituted into the threshold criteria.
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 19 is rejected as presenting substantially similar limitations as Claim 4.
Claim 5
As to Claim 5, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, where estimating the first ... as a function of one or more of: a human resource cost for time spent in manually searching videos captured from the cameras for each incident in the first set of incidents after the incident has been reported by the human source
In [0106], "In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria". We understand this calculation of cost to encompass time spent reviewing video footage from relevant camera sources if the incident has been reported by sensors; as what is claimed as the derivation of cost as a function of this variable, the incorporation of this factor in the broader cost derivation discloses this limitation.
and a human resource cost for time spent in resolving each incident in the first set of incidents reported by the human source
In [0078], " For example, in such a learning mode, historical records (e.g., of the records 116) may be provided to indicate decisions as to whether or not sensors indicated in sensor records (e.g., such as the sensor records 118) meet a threshold condition 224. Such historical records may further indicate decisions as to whether or not given alerts were assigned to the sensor analytical engine 104 or provided to the one or more communication devices 110, for example to indicate whether or not a given alert met criteria for minimizing one or more of human resources and cost in searching for a target object in a given alert, and maximizing a chance of success in finding the target object".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
and a cloud and/or edge computing cost associated with executing video analytics at the video analytics system for each incident in the first set of incidents after the incident has been first reported by the human source;
In Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 7
As to Claim 7, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Russo teaches:
The method of claim 1, wherein estimating the second. Average cost as a function of one or more of: a cloud and/or edge computing cost associated with executing video analytics at the video analytics system for each incident in the second set of incidents prior to the incident being first reported by the video analytics system;
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
Russo does not expressly disclose the remaining limitations.
However, Schuler teaches:
a human resource cost for time spent in validating each incident in the second set of incidents reported by the video analytics system; and a human resource cost for time spent in resolving each incident in the second set of incidents reported by the video analytics system.
In [0078], " For example, in such a learning mode, historical records (e.g., of the records 116) may be provided to indicate decisions as to whether or not sensors indicated in sensor records (e.g., such as the sensor records 118) meet a threshold condition 224. Such historical records may further indicate decisions as to whether or not given alerts were assigned to the sensor analytical engine 104 or provided to the one or more communication devices 110, for example to indicate whether or not a given alert met criteria for minimizing one or more of human resources and cost in searching for a target object in a given alert, and maximizing a chance of success in finding the target object".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 8
As to Claim 8, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, further comprising: responsive to enabling the video analytics system, receiving a report indicating an occurrence of a new incident from the video analytics system; updating the database with information relating to the new incident;
In [0105], "Indeed, the controller 218 and/or the computing device 102 may assign any retired alert to the sensor analytics engine 104 to search for the target object using the sensors 106. Furthermore, the alert 120, as generated and/or retrieved at the block 302, may comprise a retired alert, which is evaluated by the controller 218 and/or the computing device 102 for assignment to the sensor analytics engine 104 and/or for revival of another human-based search due, for example, to new evidence being found in a court case and/or a new sighting of the target object in the geographic area 108, or another geographic area". We understand such teaching to encompass instances of the target object being sighted through the video analytics system.
and indicating, in the database, that the new incident was reported by the video analytics system.
In keeping with understanding the claimed video analytics system to analogize to both the sensors and the sensor analytics engine of Schuler in [0047], "Hence, the records 116 may be used by the computing device 102 to generate any suitable alert, such as the alert 120, and furthermore, information to populate such alerts may be retrieved from the records 116 including, but not limited to, textual descriptions of the target objects, images thereof, and the like. Such information may be used by the computing device 102 to generate classifiers, and the like, usable by the sensor analytics engine 104 to search for a target object in the sensor data from the sensors 106, which may be incorporated into the alerts, such as the alert 120, for example to perform image and/or audio searches in images and/or audio acquired by cameras of the sensors 106". The generation of classifiers and sensor data would denote that an alert is implicitly originating from the sensors, and therefore the video analytics system.
Claim 9
As to Claim 9, Schuler combined with Shi and Russo teaches all the limitations of Claim 8 as discussed above.
Schuler teaches:
The method of claim 8, further comprising: estimating a cost incurred in resolving the new incident reported by the video analytics system; updating the second...incurred in the resolving the second set of incidents including the new incident reported by the video analytics system; and determining whether the first...is higher than the updated second...by at least the predefined threshold.
In [0107], "In particular, the controller 218 and/or the computing device 102 may rely on machine learning algorithms, and the like, and/or numerical weighting schemes, and the like, to determine whether or not a given alert meets criteria for minimizing one or more of human resources and cost in searching for a target object in a given alert, and maximizing a chance of success in finding a target object. Put another way, such criteria (e.g., which may include, but is not limited to, the sensors 106 meeting (or not meeting) a threshold condition 224) may be used to determine whether to assign the alert 120 to the sensor analytics engine 104 to search for the target object using the sensors 106, and/or to provide the alert 120 to one or more of the communication devices 110 to initiate a human-based search for the target object". This implicitly discloses the estimation of such a cost, as Schuler seeks to compare to a threshold that involves the cost on the determination of where to direct the alert.
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 10
As to Claim 10, Schuler combined with Shi and Russo teaches all the limitations of Claim 9 as discussed above.
Schuler teaches:
The method of claim 9, further comprising: continuing to enable the video analytics system to execute video analytics on videos captured by the one or more cameras to proactively detect and report incidents when the first ... is higher than the updated second ... by at least the predefined threshold.
In [0105], "Indeed, the controller 218 and/or the computing device 102 may assign any retired alert to the sensor analytics engine 104 to search for the target object using the sensors 106. Furthermore, the alert 120, as generated and/or retrieved at the block 302, may comprise a retired alert, which is evaluated by the controller 218 and/or the computing device 102 for assignment to the sensor analytics engine 104 and/or for revival of another human-based search due, for example, to new evidence being found in a court case and/or a new sighting of the target object in the geographic area 108, or another geographic area". In [0106], "In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 11
As to Claim 11, Schuler combined with Shi and Russo teaches all the limitations of Claim 9 as discussed above.
Schuler teaches:
The method of claim 9, further comprising: disabling the video analytics system from executing video analytics on videos captured by the one or more cameras when the first ... is not higher than the updated second ... by at least the predefined threshold.
In [0105], "Indeed, the controller 218 and/or the computing device 102 may assign any retired alert to the sensor analytics engine 104 to search for the target object using the sensors 106. Furthermore, the alert 120, as generated and/or retrieved at the block 302, may comprise a retired alert, which is evaluated by the controller 218 and/or the computing device 102 for assignment to the sensor analytics engine 104 and/or for revival of another human-based search due, for example, to new evidence being found in a court case and/or a new sighting of the target object in the geographic area 108, or another geographic area". In [0106], "In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 12
As to Claim 12, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, further comprising: adjusting the predefined threshold as a function of one or more of: criticality of the first set of incidents and second set of incidents,
In [0112], "Hence, it is understood that the cost of a human-based search will generally be higher than a sensor-based search. However, cost for performing a human-based search may be justified based on a time constraint and/or an incident type and/or an incident priority, as described hereafter". In [0114], "Incident type and/or incident priority associated with an alert. For example, some incident types, such as a search for a murder suspect, may be more urgent and/or may be of a higher priority than other incident types, such as a search for a stolen bicycle. Hence, an incident type and/or an incident priority may be used to justify, or not, cost for a human-based search".
number of incidents in the first set of incidents and the second set of incidents, one or more regions in which the first set of incidents and the second set of incidents occurred.
In [0110], " A size of the geographic area 108, where the larger the geographic area, the more users needed to search it, along with a higher cost. Hence, a cost score associated with a size of the geographic area 108, associated with a human-based search, may increase or decrease with size of the geographic area 108, and a success score may decrease or increase with size of the geographic area 108 (e.g., the large the geographic area 108, the more expensive is a human-based search, and the less a chance of success)".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
date and time of occurrence of the first set of incidents and the second set of incidents,
We take the notion of recording the timestamp at which information is collected to disclose this limitation, in Col 15 Line 65 - Col 16 Line 6, "In one embodiment, the notification includes information related to one or more of: a resource identifier identifying the one or more cloud computing devices 130 to which the video analytics task is assigned, a particular time period in the future (e.g., 4.30 PM evening on the same day) during which video data is to be captured and transmitted by the video camera 140 to the one or more cloud computing devices 130 for executing the video analytics task".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 13, 20
As to Claim 13, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein accessing further comprises: retrieving the first set of incidents and the second set of incidents from the database, such that, the first set of incidents and second set of incidents are of the same or similar type.
We can retrieve incidents in similar geographical areas in [0052], "In another example, the threshold condition may indicate a minimum number and/or density of sensors 106, in the geographic area 108, associated with given locations and/or scene types, above which a sensor-based search for the target object may be successful, for example a minimum number of sensors 106 having street locations and/or main street locations and/or associated with street views and/or main street views, and the like". We can also analyze incidents with a similar target type in [0054], "Such threshold conditions may be determined heuristically and/or may be determined using the historical records (of the records 116) indicating whether past sensor-based searches and/or human-based searches were successful in the geographic area 108, for example for a given target object type indicated by the alert 120".
Claim 20 is rejected as disclosing substantially similar limitations as Claim 13.
Claim 14
As to Claim 14, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein accessing further comprises: retrieving the first set of incidents and the second set of incidents from the database, such that, the first set of incidents and second set of incidents occurred in a jurisdiction controlled by the same agency.
We can retrieve incidents in similar geographical areas in [0052], "In another example, the threshold condition may indicate a minimum number and/or density of sensors 106, in the geographic area 108, associated with given locations and/or scene types, above which a sensor-based search for the target object may be successful, for example a minimum number of sensors 106 having street locations and/or main street locations and/or associated with street views and/or main street views, and the like". In [0039], "The geographic area 108 may be any suitable geographic area and may be one of a plurality of geographic areas into which a larger geographic region may be divided, for example by neighborhood, district, precinct (e.g., police precinct), ward, county, and the like".
Claim 15
As to Claim 15, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein accessing further comprises: retrieving the first set of incidents and the second set of incidents from the database, such that, the first set of incidents and second set of incidents were reported to have occurred at the same location
We can retrieve incidents in similar geographical areas in [0052], "In another example, the threshold condition may indicate a minimum number and/or density of sensors 106, in the geographic area 108, associated with given locations and/or scene types, above which a sensor-based search for the target object may be successful, for example a minimum number of sensors 106 having street locations and/or main street locations and/or associated with street views and/or main street views, and the like".
and/or during the same timeframe.
We take the notion of recording the timestamp at which information is collected to disclose this limitation, in Col 15 Line 65 - Col 16 Line 6, "In one embodiment, the notification includes information related to one or more of: a resource identifier identifying the one or more cloud computing devices 130 to which the video analytics task is assigned, a particular time period in the future (e.g., 4.30 PM evening on the same day) during which video data is to be captured and transmitted by the video camera 140 to the one or more cloud computing devices 130 for executing the video analytics task".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 16
As to Claim 16, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Schuler teaches:
The method of claim 1, wherein prior to enabling the video analytics system, the method further comprising: presenting an electronic notification to the one or more agencies with a recommendation
In [0102], "In some examples, a user 112 of a communication device 110 that is to assign alerts to other users 112, and the like, to perform such a human-based search, may accept or reject the alert 120, for example based on a heuristic decision as to whether, or not, there are sufficient human-based resources to perform such a human-based search and/or whether or not the target object is important enough to merit such as human-based search (e.g., people may merit such a human-based search whereas bicycles may not)".
and receiving a response indicating permission to enable the video analytics system to execute video analytics on videos captured by the one or more cameras.
In [0103], "When rejected (e.g., as determined at the computing device 102 via a rejection notification from a communication device 110), the alert 120 may be assigned to the sensor analytics engine 104 to search for the target object regardless of the sensors 106 meeting, or not meeting, a threshold condition 224".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
that the video analytics system be enabled for executing video analytics on video captured by the one or more cameras;
In Col 6 Lines 20-29, "As an example, the video camera 140 may transmit video data captured by the video camera 140 to edge computing devices 120 via a local area network to enable the edge computing device 120 to execute an assigned video analytics task. As another example, the video camera 140 may transmit video data captured by the video camera 140 to cloud computing devices 130 via a wide area network to enable the cloud computing devices 130 to execute an assigned video analytics task".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the performance of computations as taught in Russo and apply that to the system as taught in Schuler. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 6 are rejected under 35 U.S.C. 103 as being unpatentable over Schuler(US 20220207983 A1) in view of Shi(US 20230098165 A1) in further view of Russo(US 11503101 B1) in further view of Zhang(US 20210027485 A1).
Claim 6
As to Claim 6, Schuler combined with Shi and Russo teaches all the limitations of Claim 1 as discussed above.
Zhang teaches:
The method of claim 1, further comprising: deducting average revenues respectively collected in relation with resolving the first and second set of incidents from
In [0143], "The machine learning training can then incorporate into a model relationships between outcomes of interest and the state of the area shown in monitoring images, whether or not the images are labeled with specific conditions shown in the area. For example, with the data sets indicating monitoring data and outcomes, the system can automatically train a model to determine what the ideal or optimal state is to achieve certain outcomes. Similarly, the system can identify states or conditions of the monitored area that decrease desired outcomes and should be avoided. The system can be configured to train a model to optimize for any of various metrics, such as costs, revenue, employee labor percentage (e.g., cost of labor as a percentage of revenue), and so on. The system can use an optimization function (e.g., a cost function or objective function) to train the model to recognize how the state of the monitored area affects certain criteria or factors of interest. This can allow the system to learn which states or conditions of the monitored area results in the best outcomes, e.g., the highest customer satisfaction, highest revenue, lowest cost of labor to revenue, etc".
Zhang does not expressly disclose the remaining limitations.
However, Schuler teaches:
the ... and second ... prior to determining whether the first … is higher than the second … by at least a predefined threshold.
In [0106], " In some examples, the decision, by the controller 218 and/or the computing device 102, to assign the alert 120 to the sensor analytics engine 104 (e.g. at the block 310) or initiate the human-based search (e.g. at the block 312), may be further based on one or more criteria for minimizing one or more of human resources and cost in searching for the target object and maximizing a chance of success in finding the target object. The sensors 106 meeting (or not meeting) a threshold condition 224 comprise one of such criteria".
Schuler does not expressly disclose the remaining limitations.
However, Russo teaches:
average cost
We can make estimates for incidents with distinct characteristics, in Col 14 Lines 14-21, "The electronic computing device 110 then estimates a cloud computing cost to be incurred for completing execution of the current video analytics task (determined at block 320) at the cloud computing devices 130 using the costs (e.g., by averaging the costs) historically incurred for performing a similar type of video analytics task on video data historically captured corresponding to the same or similar scene 150-2". Here understand the origin of the incident reporting, or our first and second average, to be a discerning characteristic by which distinct cost averages could be computed.
Russo combined with Schuler discloses means for managing the delegation of analytical tasks, including those from video sources. Zhang discloses means for status monitoring from video footage with machine learning. Extending the revenue calculation of Zhang to the system of Russo combined with Schuler is applicable as they are both concerned with the task of performing video analytics.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to integrate the revenue calculations of Zhang and apply that to the system as taught in Schuler combined with Shi and Russo. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the revenue-included calculation of Zhang would enable users of the system of Schuler combined with Shi and Russo to have a more nuanced picture of cost when calculating the threshold criteria.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THEODORE XIE/Examiner, Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623