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 .
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method comprising:
receiving, from one or more sensors, measurements of evidence of existence of an event of interest;
providing, to a model that operates using a type-2 probability and encodes probabilistic rules, the measurements of the evidence;
providing, by the model and responsive to the measurements of the evidence, an output indicating a likelihood the event of interest exists; and
altering, based on a communication from an operator, an object in a geographical region of the event of interest.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “providing, to a model that operates using a type-2 probability and encodes probabilistic rules, the measurements of the evidence (providing data);
providing, by the model and responsive to the measurements of the evidence, an output indicating a likelihood the event of interest exists (observing an output); and
altering, based on a communication from an operator, an object in a geographical region of the event of interest (incorporating a user input)” are treated by the Examiner as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 9 and 17.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: receiving, from one or more sensors, measurements of evidence of existence of an event of interest;
Claim 9: processing circuitry; a memory; receiving, from one or more sensors, measurements of evidence of existence of an event of interest;
Claim 17: A non-transitory machine readable medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: receiving, from one or more sensors, measurements of evidence of existence of an event of interest.
The additional element of receiving, from one or more sensors, measurements of evidence of existence of an event of interest represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A non-transitory machine readable medium or a memory (generic memory) and processing circuitry (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-8, 10-16, and 18-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-9, 11-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanaujia et al. (US 20150279182 A1), hereinafter “Kanaujia”.
Regarding Claim 1, Kanaujia teaches a method comprising:
receiving, from one or more sensors, measurements of evidence of existence of an event of interest (Kanaujia [0098] At 505, the process 500 tracks one or more targets (e.g., target 30 and/or 35) detected in the environment using multiple sensors (e.g., sensors 15). See Fig. 5 505);
providing, to a model that operates using a type-2 probability and encodes probabilistic rules (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.), the measurements of the evidence (Kanaujia [0099] At 509, the process 500 (e.g., using visual processing module 151) extracts target information and spatial-temporal interaction information of the targets tracked at 505 as probabilistic confidences, as previously described herein. In embodiments, extracting information includes determining the position of the targets, classifying the targets, and extracting attributes of the targets. For example, the process 500 can determine spatial and temporal information of a target in the environment, classify the target a person (e.g., target 30, and determine an attribute of the person is holding a package (e.g., package 31). As previously described herein, the process 500 can reference information in learned models 136 for classifying the target and identifying its attributes. See Fig. 5 509);
providing, by the model and responsive to the measurements of the evidence, an output indicating a likelihood the event of interest exists (Kanaujia [0100] At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein. For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package. See Fig. 5 519).
Kanaujia does not explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest.
However, Kanaujia teaches determining probability of the occurrence of an event that may require follow-up action (Kanaujia [0100] For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia to explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest, because in the case of event such as a determination of a package delivery, an operator or user, would naturally follow receiving that output by investigating or retrieving the left package (See MPEP 2143 I. (E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success).
Regarding Claim 3, Kanaujia (as stated above) does not explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode.
However, Kanaujia teaches the operator and a UI (Kanaujia [0034] The I/O device 133 can include any device that enables an individual to interact with the computing device 130 (e.g., a user interface) and/or any device that enables the computing device 130 to communicate with one or more other computing devices using any type of communications link. The I/O device 133 can be, for example, a handheld device, PDA, smartphone, touchscreen display, handset, keyboard, etc.), an input indicating whether a model is to operate in permissive mode or strict mode (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) to explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode, the rules are determined to be hard or soft when first input or generated, and would therefore interact with the model accordingly.
Regarding Claim 4, Kanaujia (as stated above) does not explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent.
However, Kanaujia teaches hard and soft rules for determining the occurrence of an event (Kanaujia [0016] the Markov logic network defines complex events and object assertions by hard rules that are always true and soft rules that are usually true.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Kanaujia (as stated above) to explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent, as part of defining which rules are considered “hard” or “soft”.
Regarding Claim 5, Kanaujia (as stated above) does not explicitly further teach wherein the output further includes respective probabilities of certain truth, certain falsity, ambiguity, and consistency.
However, Kanauji teaches wherein the output includes respective probabilities (Kanaujia [0100] At 513, the process 500 constructs a Markov logic networks (e.g., Markov logic networks 160 and 425) by grounded formulae based on each of the confidences determined at 509 by instantiating rules from a knowledge base (e.g., knowledge base 138), as previously described herein. At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) to explicitly teach wherein the output further includes respective probabilities of certain truth, certain falsity, ambiguity, and consistency, by specifying ranges for the values of probabilities into named categories to easily explain and format the determined probability (See MPEP (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;).
Regarding Claim 6, Kanaujia (as stated above) does not explicitly further teach wherein the output includes a single value that indicates a likelihood of existence and non-existence of the event of interest (Kanaujia [0100] At 513, the process 500 constructs a Markov logic networks (e.g., Markov logic networks 160 and 425) by grounded formulae based on each of the confidences determined at 509 by instantiating rules from a knowledge base (e.g., knowledge base 138), as previously described herein. At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein.).
Regarding Claim 7, ) does not explicitly teach receiving, from the operator and by a user interface (UI), probabilistic rules associating the evidence with existence of the event of interest.
However, Kanaujia teaches the operator and a UI (Kanaujia [0034] The I/O device 133 can include any device that enables an individual to interact with the computing device 130 (e.g., a user interface) and/or any device that enables the computing device 130 to communicate with one or more other computing devices using any type of communications link. The I/O device 133 can be, for example, a handheld device, PDA, smartphone, touchscreen display, handset, keyboard, etc.) and hard and soft rules for determining the occurrence of an event (Kanaujia [0016] the Markov logic network defines complex events and object assertions by hard rules that are always true and soft rules that are usually true.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Kanaujia (as stated above) to explicitly teach receiving, from the operator and by a user interface (UI), probabilistic rules associating the evidence with existence of the event of interest., as part of defining which rules are considered “hard” or “soft” (Kanaujia [0043] For example, a hard rule can be “cars do not fly,” whereas soft rules allow uncertainty and exceptions. Violation of soft rules will make the complex event less probable but not impossible. For example, a soft rule can be, “walking pedestrians on foot do not exceed a velocity of 10 miles per hour.” Thus, the rules can be used to determine that a fast moving object on the ground is a vehicle, rather than a person.).
Regarding Claim 8, Kanaujia (as stated above) further teaches wherein the rules includes rules that positively associate the existence of the event of interest with first evidence and negatively associate the existence of the event of interest with second, different evidence (Kanaujia [0043] For example, a hard rule can be “cars do not fly,” whereas soft rules allow uncertainty and exceptions. Violation of soft rules will make the complex event less probable but not impossible. For example, a soft rule can be, “walking pedestrians on foot do not exceed a velocity of 10 miles per hour.” Thus, the rules can be used to determine that a fast moving object on the ground is a vehicle, rather than a person.).
Regarding Claim 9, Kanauji teaches a system comprising:
processing circuitry (Kanaujia [0037] In embodiments, the computing device 130 includes one or more processors 139);
a memory including instructions that, when executed by the processing circuitry, causes the processing circuitry to perform operations (Kanaujia [0037] In embodiments, the computing device 130 includes one or more processors 139, one or more memory devices 141 (e.g., RAM and ROM), one or more I/O interfaces 143, and one or more network interfaces 144. The memory device 141 can include a local memory (e.g., a random access memory and a cache memory) employed during execution of program instructions.) comprising:
receiving, from one or more sensors, measurements of evidence of existence of an event of interest (Kanaujia [0098] At 505, the process 500 tracks one or more targets (e.g., target 30 and/or 35) detected in the environment using multiple sensors (e.g., sensors 15). See Fig. 5 505);
providing, to a model that operates using a type-2 probability and encodes probabilistic rules (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.), the measurements of the evidence (Kanaujia [0099] At 509, the process 500 (e.g., using visual processing module 151) extracts target information and spatial-temporal interaction information of the targets tracked at 505 as probabilistic confidences, as previously described herein. In embodiments, extracting information includes determining the position of the targets, classifying the targets, and extracting attributes of the targets. For example, the process 500 can determine spatial and temporal information of a target in the environment, classify the target a person (e.g., target 30, and determine an attribute of the person is holding a package (e.g., package 31). As previously described herein, the process 500 can reference information in learned models 136 for classifying the target and identifying its attributes. See Fig. 5 509);
providing, by the model and responsive to the measurements of the evidence, an output indicating a likelihood the event of interest exists (Kanaujia [0100] At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein. For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package. See Fig. 5 519).
Kanaujia does not explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest.
However, Kanaujia teaches determining probability of the occurrence of an event that may require follow-up action (Kanaujia [0100] For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia to explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest, because in the case of event such as a determination of a package delivery, an operator or user, would naturally follow receiving that output by investigating or retrieving the left package (See MPEP 2143 I. (E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success).
Regarding Claim 11, Kanaujia (as stated above) does not explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode.
However, Kanaujia teaches the operator and a UI (Kanaujia [0034] The I/O device 133 can include any device that enables an individual to interact with the computing device 130 (e.g., a user interface) and/or any device that enables the computing device 130 to communicate with one or more other computing devices using any type of communications link. The I/O device 133 can be, for example, a handheld device, PDA, smartphone, touchscreen display, handset, keyboard, etc.), an input indicating whether a model is to operate in permissive mode or strict mode (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) to explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode, the rules are determined to be hard or soft when first input or generated, and would therefore interact with the model accordingly.
Regarding Claim 12, Kanaujia (as stated above) does not explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent.
However, Kanaujia teaches hard and soft rules for determining the occurrence of an event (Kanaujia [0016] the Markov logic network defines complex events and object assertions by hard rules that are always true and soft rules that are usually true.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Kanaujia (as stated above) to explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent, as part of defining which rules are considered “hard” or “soft”.
Regarding Claim 13, Kanaujia (as stated above) does not explicitly further teach wherein the output further includes respective probabilities of certain truth, certain falsity, ambiguity, and consistency.
However, Kanauji teaches wherein the output includes respective probabilities (Kanaujia [0100] At 513, the process 500 constructs a Markov logic networks (e.g., Markov logic networks 160 and 425) by grounded formulae based on each of the confidences determined at 509 by instantiating rules from a knowledge base (e.g., knowledge base 138), as previously described herein. At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) to explicitly teach wherein the output further includes respective probabilities of certain truth, certain falsity, ambiguity, and consistency, by specifying ranges for the values of probabilities into named categories to easily explain and format the determined probability (See MPEP (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;).
Regarding Claim 14, Kanaujia (as stated above) does not explicitly further teach wherein the output includes a single value that indicates a likelihood of existence and non-existence of the event of interest (Kanaujia [0100] At 513, the process 500 constructs a Markov logic networks (e.g., Markov logic networks 160 and 425) by grounded formulae based on each of the confidences determined at 509 by instantiating rules from a knowledge base (e.g., knowledge base 138), as previously described herein. At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein.).
Regarding Claim 15, ) does not explicitly teach receiving, from the operator and by a user interface (UI), probabilistic rules associating the evidence with existence of the event of interest.
However, Kanaujia teaches the operator and a UI (Kanaujia [0034] The I/O device 133 can include any device that enables an individual to interact with the computing device 130 (e.g., a user interface) and/or any device that enables the computing device 130 to communicate with one or more other computing devices using any type of communications link. The I/O device 133 can be, for example, a handheld device, PDA, smartphone, touchscreen display, handset, keyboard, etc.) and hard and soft rules for determining the occurrence of an event (Kanaujia [0016] the Markov logic network defines complex events and object assertions by hard rules that are always true and soft rules that are usually true.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Kanaujia (as stated above) to explicitly teach receiving, from the operator and by a user interface (UI), probabilistic rules associating the evidence with existence of the event of interest., as part of defining which rules are considered “hard” or “soft” (Kanaujia [0043] For example, a hard rule can be “cars do not fly,” whereas soft rules allow uncertainty and exceptions. Violation of soft rules will make the complex event less probable but not impossible. For example, a soft rule can be, “walking pedestrians on foot do not exceed a velocity of 10 miles per hour.” Thus, the rules can be used to determine that a fast moving object on the ground is a vehicle, rather than a person.).
Regarding Claim 16, Kanaujia (as stated above) further teaches wherein the rules includes rules that positively associate the existence of the event of interest with first evidence and negatively associate the existence of the event of interest with second, different evidence (Kanaujia [0043] For example, a hard rule can be “cars do not fly,” whereas soft rules allow uncertainty and exceptions. Violation of soft rules will make the complex event less probable but not impossible. For example, a soft rule can be, “walking pedestrians on foot do not exceed a velocity of 10 miles per hour.” Thus, the rules can be used to determine that a fast moving object on the ground is a vehicle, rather than a person.).
Regarding Claim 17, Kanaujia teaches a non-transitory machine readable medium including instructions that, when executed by a machine, cause the machine to perform operations (Kanaujia [0037] The processor 139 executes computer program instructions (e.g., an operating system and/or application programs), which can be stored in the memory device 141 and/or storage system 135.) comprising:
receiving, from one or more sensors, measurements of evidence of existence of an event of interest (Kanaujia [0098] At 505, the process 500 tracks one or more targets (e.g., target 30 and/or 35) detected in the environment using multiple sensors (e.g., sensors 15). See Fig. 5 505);
providing, to a model that operates using a type-2 probability and encodes probabilistic rules (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.), the measurements of the evidence (Kanaujia [0099] At 509, the process 500 (e.g., using visual processing module 151) extracts target information and spatial-temporal interaction information of the targets tracked at 505 as probabilistic confidences, as previously described herein. In embodiments, extracting information includes determining the position of the targets, classifying the targets, and extracting attributes of the targets. For example, the process 500 can determine spatial and temporal information of a target in the environment, classify the target a person (e.g., target 30, and determine an attribute of the person is holding a package (e.g., package 31). As previously described herein, the process 500 can reference information in learned models 136 for classifying the target and identifying its attributes. See Fig. 5 509);
providing, by the model and responsive to the measurements of the evidence, an output indicating a likelihood the event of interest exists (Kanaujia [0100] At 519, the process 500 (e.g., using scene analysis module 135) determines probability of occurrence of a complex event based on the Markov logic network constructed at 513 for individual sensor, as previously described herein. For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package. See Fig. 5 519).
Kanaujia does not explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest.
However, Kanaujia teaches determining probability of the occurrence of an event that may require follow-up action (Kanaujia [0100] For example, an event of a person leaving the package in the building can be determined based on a combination of events, including the person entering the building with a package and the person exiting the building without the package.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia to explicitly teach altering, based on a communication from an operator, an object in a geographical region of the event of interest, because in the case of event such as a determination of a package delivery, an operator or user, would naturally follow receiving that output by investigating or retrieving the left package (See MPEP 2143 I. (E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success).
Regarding Claim 19, Kanaujia (as stated above) does not explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode.
However, Kanaujia teaches the operator and a UI (Kanaujia [0034] The I/O device 133 can include any device that enables an individual to interact with the computing device 130 (e.g., a user interface) and/or any device that enables the computing device 130 to communicate with one or more other computing devices using any type of communications link. The I/O device 133 can be, for example, a handheld device, PDA, smartphone, touchscreen display, handset, keyboard, etc.), an input indicating whether a model is to operate in permissive mode or strict mode (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) to explicitly teach receiving, from the operator and by a UI, an input indicating whether a model is to operate in permissive mode or strict mode, the rules are determined to be hard or soft when first input or generated, and would therefore interact with the model accordingly.
Regarding Claim 20, Kanaujia (as stated above) does not explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent.
However, Kanaujia teaches hard and soft rules for determining the occurrence of an event (Kanaujia [0016] the Markov logic network defines complex events and object assertions by hard rules that are always true and soft rules that are usually true.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Kanaujia (as stated above) to explicitly teach wherein the permissive mode operates under an assumption that the geographic region of the event of interest is consistent and the strict mode operates under an assumption that the rules are consistent, as part of defining which rules are considered “hard” or “soft”.
Claim(s) 2, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanaujia (US as stated above), in view of Mota et al. (US 20190370731 A1) hereinafter “Mota”.
Regarding Claim 2, Kanaujia (as stated above) further teaches wherein the one or more sensors includes an imaging device or a weather sensor (Kanaujia [0031] The environment 10 includes a network 13 of surveillance sensors 15-1, 15-2, 15-3, 15-4 (i.e., sensors 15) around a building 20. The sensors 15 can be calibrated or non-calibrated sensors. Additionally, the sensors 15 can have overlapping or non-overlapping fields of view. Also see [0033] In accordance with aspects of the present disclosure, sensors 15 are any apparatus for obtaining information about events occurring in a view. Examples include: color and monochrome cameras, video cameras, static cameras, pan-tilt-zoom cameras, omni-cameras, closed-circuit television (CCTV) cameras, charge-coupled device (CCD) sensors, analog and digital cameras, PC cameras, web cameras, tripwire event detectors, loitering event detectors, and infra-red-imaging devices. If not more specifically described herein, a “camera” refers to any sensing device.).
Kanaujia (as stated above) teaches that the probabilistic rules are derived from a knowledge base (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events. The hard rules are assertions that should be strictly satisfied for an associated complex event to be identified. Also see [0044] The rules in the knowledge base 138 can be used to construct the Markov logic network 160.), but does not explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest.
Mota teaches a knowledge base provided by the operator, the operator being a subject matter expert (SME) of the event of interest (Mota [0044] In addition to the user input 302, the method may also include receiving or determining economic news 312, which may include, for example, data related to the product segment selected or input by a user and which news database should be used to crawl or search for information, which may be gathered from a knowledgebase. The knowledge base may include a database maintained by subject matter experts.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) in view of Kota to explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest, because it is known to have a knowledge base maintained by an expert to ensure that the knowledge stored in that database is verified to be up to date and accurate.
Regarding Claim 10, Kanaujia (as stated above) further teaches wherein the one or more sensors includes an imaging device or a weather sensor (Kanaujia [0031] The environment 10 includes a network 13 of surveillance sensors 15-1, 15-2, 15-3, 15-4 (i.e., sensors 15) around a building 20. The sensors 15 can be calibrated or non-calibrated sensors. Additionally, the sensors 15 can have overlapping or non-overlapping fields of view. Also see [0033] In accordance with aspects of the present disclosure, sensors 15 are any apparatus for obtaining information about events occurring in a view. Examples include: color and monochrome cameras, video cameras, static cameras, pan-tilt-zoom cameras, omni-cameras, closed-circuit television (CCTV) cameras, charge-coupled device (CCD) sensors, analog and digital cameras, PC cameras, web cameras, tripwire event detectors, loitering event detectors, and infra-red-imaging devices. If not more specifically described herein, a “camera” refers to any sensing device.).
Kanaujia (as stated above) teaches that the probabilistic rules are derived from a knowledge base (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events. The hard rules are assertions that should be strictly satisfied for an associated complex event to be identified. Also see [0044] The rules in the knowledge base 138 can be used to construct the Markov logic network 160.), but does not explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest.
Mota teaches a knowledge base provided by the operator, the operator being a subject matter expert (SME) of the event of interest (Mota [0044] In addition to the user input 302, the method may also include receiving or determining economic news 312, which may include, for example, data related to the product segment selected or input by a user and which news database should be used to crawl or search for information, which may be gathered from a knowledgebase. The knowledge base may include a database maintained by subject matter experts.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) in view of Kota to explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest, because it is known to have a knowledge base maintained by an expert to ensure that the knowledge stored in that database is verified to be up to date and accurate.
Regarding Claim 18, Kanaujia (as stated above) further teaches wherein the one or more sensors includes an imaging device or a weather sensor (Kanaujia [0031] The environment 10 includes a network 13 of surveillance sensors 15-1, 15-2, 15-3, 15-4 (i.e., sensors 15) around a building 20. The sensors 15 can be calibrated or non-calibrated sensors. Additionally, the sensors 15 can have overlapping or non-overlapping fields of view. Also see [0033] In accordance with aspects of the present disclosure, sensors 15 are any apparatus for obtaining information about events occurring in a view. Examples include: color and monochrome cameras, video cameras, static cameras, pan-tilt-zoom cameras, omni-cameras, closed-circuit television (CCTV) cameras, charge-coupled device (CCD) sensors, analog and digital cameras, PC cameras, web cameras, tripwire event detectors, loitering event detectors, and infra-red-imaging devices. If not more specifically described herein, a “camera” refers to any sensing device.).
Kanaujia (as stated above) teaches that the probabilistic rules are derived from a knowledge base (Kanaujia [0043] In accordance with aspects of the present disclosure, the knowledge base 138 includes hard and soft rules for modeling spatial and temporal interactions between various entities and the temporal structure of various complex events. The hard rules are assertions that should be strictly satisfied for an associated complex event to be identified. Also see [0044] The rules in the knowledge base 138 can be used to construct the Markov logic network 160.), but does not explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest.
Mota teaches a knowledge base provided by the operator, the operator being a subject matter expert (SME) of the event of interest (Mota [0044] In addition to the user input 302, the method may also include receiving or determining economic news 312, which may include, for example, data related to the product segment selected or input by a user and which news database should be used to crawl or search for information, which may be gathered from a knowledgebase. The knowledge base may include a database maintained by subject matter experts.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kanaujia (as stated above) in view of Kota to explicitly teach the probabilistic rules are provided by the operator, the operator being a subject matter expert (SME) of the event of interest, because it is known to have a knowledge base maintained by an expert to ensure that the knowledge stored in that database is verified to be up to date and accurate.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Solmaz et al. (US 20220161818 A1) discloses a Method And System For Supporting Autonomous Driving Of An Autonomous Vehicle.
Ray et al. (US 20130104236 A1) discloses a Pervasive, Domain And Situational-Aware, Adaptive, Automated, And Coordinated Analysis And Control Of Enterprise-Wide Computers, Networks, And Applications For Mitigation Of Business And Operational Risks And Enhancement Of Cyber Security.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863