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 .
The Office Action is in response to the application filed on 06/12/2023. Claims 1-20 are pending in
the application. Claims 1, 19 and 20 are independent claims.
Specification
The abstract dated 06/12/2023 has been reviewed. It has 106 words and 9 lines and no legal
phraseology. It is accepted.
Claim Objections
Claims 11 and 15 are objected to because of the following informalities:
Claim 11 recites “transmitting sensor data from the monitoring system of the property …” should read as “transmitting the sensor data from the monitoring system of the property ...”
Claim 15 recites “The method of claim 1, wherein generating the virtual model of the property comprises …” should read as “The method of claim 1, further comprising generating the virtual model of the property, wherein generating the virtual model of the property comprises …"
Appropriate correction is required.
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.
The claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception, an abstract idea, it has not been integrated into practical application, and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated
the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance
published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates,
and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-18 are directed to method and fall within the statutory category of process;
Yes, Claim 19 is directed to non-transitory computer-readable media and falls within the statutory category of article of manufacture;
Yes, Claim 20 is directed to the system and falls within the statutory category of machine.
Step 2A Prong 1:
Claim 1: The limitations of “generating a security rule for the monitoring system using the action data from the computer generated avatar in the virtual model,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of the specification, covers performance of the limitation in the human mind. For example, a person is capable of observing or considering information representing an action, evaluating the action represented by the information, and determining whether the action should be permitted or should result in a security response. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)) – MPEP 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under step 2A Prong I.
Therefore, claims 1, 19 and 20 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claim as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 19 and 20: The judicial exception is not integrated into a practical application.
The additional limitations of “One or more non-transitory computer-readable media storing one or more instructions executable by a computer system to perform operations comprising:” and “A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:,” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception, which does not integrate judicial exception into a practical application (see MPEP §2106.05(f)).
Further, the additional limitations of “presenting a virtual model of a property that includes a monitoring system; obtaining action data from a computer generated avatar in the virtual model … providing, to the monitoring system, the security rule …,” which are merely recitations of insignificant extra-solution activity such as data gathering (i.e., obtaining and providing data) and data outputting (i.e., presenting model data), which does not integrate a judicial exception into practical application (see MPEP § 2106.05(g)).
Further, the additional limitations of “to cause the monitoring system to generate, using the security rule and sensor data from the property received by the monitoring system, a security alert,” which is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The limitation merely uses the monitoring system to receive sensor data, process the sensor data using the generated security rule, and generate a security alert as the resulting output. The claim does not recite any improvement to how the monitoring system recites or processes the sensor data or generates the security alert, but merely use generic computing components as tools to implement the judicial exception and do not result in an improvement to the function of a computer or to any other technology or technical field.
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application, and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquiries set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 19 and 20 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 19 and 20: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); …
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory, …
In particular, the claim recites the additional elements of presenting a virtual model of a property that includes a monitoring system, obtaining action data from a computer generated avatar in the virtual model, and providing the generated security rule to the monitoring system to cause the monitoring system to generate a security alert using the security rule and sensor data received from the property. These additional limitations merely recite generic computing components used in their ordinary capacities to present information, obtain and process data, communicate information, and generate an output. Therefore, these additional elements, when considered individually and in combination, merely apply the judicial exception using generic computer components and do not provide significantly more than the judicial exception.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 19 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-18 are also similarly rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process and/or mathematical concepts, or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-18 are also rejected for incorporating the deficiency of their independent claim 1.
Claim 2 recites “The method of claim 1, wherein generating the security rule using the action data comprises:
determining movement vectors of the avatar moving in the virtual model; and
determining, using the movement vectors, regions represented in the virtual model accessed by the avatar.”
The limitation further specifies evaluating the action data to generate the security rule by determining movement vectors of the avatar and determining, using the movement vectors, regions represented in the virtual model accessed by the avatar. The limitation is merely an extension of mental process. For example, a person capable of observing movement of an avatar, identifying its movement direction, and determining the regions accessed by the avatar based on the observed movement, then determining whether the action should be permitted or should result in a security response. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The method of claim 1, wherein generating the security rule using the action data comprises:
generating a virtual image stream representing images from a viewpoint corresponding to a real camera of the monitoring system of the property; and
generating, using the virtual image stream, the security rule.”
The limitation further specifies generating a virtual image stream representing images from a viewpoint corresponding to a real camera of the monitoring system of the property and generating the security rule using the virtual image stream. The limitation of “generating a virtual image stream …” merely recites, at a high level of generality, computer implementation for generating a virtual image stream representing images, without reciting a particular technological manner by which virtual image stream is generated. See MPEP § 2106.05(f). Accordingly, the limitation does not integrate the judicial exception into practical application and amount to significantly more than the judicial exception. The limitation of “generating … the security rule,” which merely an extension of mental process. For example, a person is capable of observing the images represented by the virtual image stream, identifying an action or condition depicted in the images, and establishing a security rule based on the observed action or condition. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The method of claim 1, comprising:
obtaining monitoring information of the monitoring system of the property; and
generating the virtual model of the property using the monitoring information of the monitoring system of the property.”
The limitation further specifies obtaining monitoring information of the monitoring system of the property and generating the virtual model of the property using the obtained monitoring information. The limitation of “obtaining monitoring information” merely recites insignificant extra-solution activity such as data gathering (i.e., obtaining/collecting data). The limitation of “generating the virtual model” merely recites, at a high level of generality, computer implementation for generating a virtual representation using the obtained information, without reciting a particular technological manner by which the virtual model is generated. See MPEP § 2106.05(f) and (g). Accordingly, these limitations do not integrate the judicial exception into practical application and amount to significantly more than the judicial exception. Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The method of claim 1, comprising:
obtaining user input provided by a user input device representing the action data of the computer generated avatar in the virtual model, wherein the computer generated avatar is controlled using the user input.”
The limitation further specifies obtaining user input from a user input device representing the action data of the computer generated avatar, wherein the computer generated avatar is controlled using the user input. It is merely a recitation of insignificant extra-solution activity such as data gathering (i.e., obtaining user input data), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The method of claim 1, comprising:
obtaining user input requesting that one or more actions represented in the action data trigger one or more security alerts, wherein the one or more security alerts include the security alert.”
The limitation further specifies obtaining user input requesting that one or more actions represented in the action data trigger one or more security alerts. It is merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving information identifying which represented action or actions are to trigger a security alert), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “The method of claim 1, comprising:
providing the security rule to a device of the monitoring system;
obtaining feedback of the security rule from the device; and
updating, in response to obtaining the feedback of the security rule, a security rule database for use by the monitoring system of the property.”
The limitation further specifies providing the security rule to a device of the monitoring system, obtaining feedback of the security rule from the device, and updating, in response to the feedback, a security rule database for use by the monitoring system of the property. It is merely a recitation of insignificant extra-solution activity such as data gathering and data outputting (i.e., providing and obtaining information and updating stored information), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “The method of claim 7, wherein updating the security rule database comprises:
storing the security rule in the security rule database.”
The limitation further specifies storing the security rule in the security rule database. It is merely a recitation of insignificant extra-solution activity such as data gathering (i.e., storing information), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The method of claim 7, comprising:
updating, in response to obtaining the feedback of the security rule, the security rule; and
updating the security rule database by storing the updated security rule in the security rule database.”
The limitation further specifies updating the security rule in response to the obtained feedback and storing the updated security rule in the security rule database. It is merely a recitation of insignificant extra-solution activity such as data gathering (i.e., updating information), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “The method of claim 7, wherein providing the security rule to the device of the monitoring system comprises:
providing data indicating a virtual representation of the avatar performing an action in the virtual model to a device of a user.”
The limitation further specifies providing data indicating a virtual representation of the avatar performing an action in the virtual model to a device of a user. It is merely a recitation of insignificant extra-solution activity such as data output (i.e., transmitting/outputting the resulting information after the virtual representation has been generated), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “The method of claim 1, wherein generating the security alert using the security rule comprises:
transmitting sensor data from the monitoring system of the property to a device of the monitoring system, wherein the sensor data represents an action satisfying one or more thresholds of the security rule.”
The limitation further specifies transmitting sensor data representing an action satisfying one or more thresholds of the security rule to a device of the monitoring system. It is merely a recitation of insignificant extra-solution activity such as data gathering and/or data output (i.e., transmitting the sensor data represents an action satisfying one or more thresholds of the security rule), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claim 12 recites “The method of claim 1, wherein generating the security rule using the action data comprises:
determining a plurality of thresholds including one or more thresholds for movement of an object and one or more thresholds for features of the object.”
The limitation further specifies determining thresholds for movement of an object and for features of the object. The limitation is merely an extension of mental process. For example, a person is capable of establishing criteria for movement of an object and characteristics of the object based on the observed information. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
Claim 13 recites “The method of claim 12, wherein determining the plurality of thresholds comprises:
determining the one or more thresholds for movement including a position threshold indicating whether a person is moving on or off a pathway.”
The limitation further specifies determining a position threshold indicating whether a person is moving on or off a pathway. The limitation is merely an extension of mental process. For example, a person is capable of establishing a position criterion for determining whether an observed person is moving on or off a pathway. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 13 is ineligible under 35 USC 101.
Claim 14 recites “The method of claim 1, wherein generating the security rule comprises:
detecting input indicating an identifier for a specific person;
determining, using the action data from the computer generated avatar in the virtual model, an action for the specific person; and
generating the security rule that (i) includes the identifier for the specific person and (ii) identifies the action that applies to the specific person using the identifier.”
The limitation further specifies detecting an identifier for a specific person from input, determining an action for the specific person based on the action data, and generating a security rule associating the specific person with the applicable action. The limitation is merely an extension of mental process. For example, a person is capable of recognizing an identifier for a particular person from information, determining an action applicable to the person based on observed action information, and establishing a security rule associating the particular person with the action. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 14 is ineligible under 35 USC 101.
Claim 15 recites “The method of claim 1, wherein generating the virtual model of the property comprises:
generating a virtual representation of a home and one or more elements within a threshold distance of the home.”
The limitation merely specifies generating a virtual representation including a home and one or more elements within a threshold distance of the home. The limitation merely recites, at a high level of generality, computer implementation for generating a virtual environment, without reciting a particular technological manner by which the virtual representation of a home and one or more elements are generated. See MPEP § 2106.05(f). Accordingly, the limitation does not integrate the judicial exception into practical application and amount to significantly more than the judicial exception. Therefore, the office finds that the claim 15 is ineligible under 35 USC 101.
Claim 16 recites “The method of claim 1, wherein obtaining the action data from the computer generated avatar in the virtual model comprises:
obtaining actions of the computer generated avatar walking off a path at the property in the virtual model of the property.”
The limitation further specifies obtaining action data representing the computer generated avatar walks off a path at the property in the virtual model. It is merely a recitation of insignificant extra-solution activity such as data gathering (i.e., collecting/obtaining action data have been generated), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 16 is ineligible under 35 USC 101.
Claim 17 recites “The method of claim 1, comprising:
providing data captured by the monitoring system of the property to a device of a user; and
obtaining feedback from the device of the user indicating if the data captured by the monitoring system of the property includes acceptable or not acceptable actions.”
The limitation further specifies providing data captured by the monitoring system to a device of a user and obtaining feedback indicating whether the captured data includes acceptable or unacceptable actions. It is merely a recitation of insignificant extra-solution activity such as data gathering and data output (i.e., transmitting/outputting/collecting captured data and feedback), which does not integrate a judicial exception into practical application and amount to significantly more than the judicial exception. See MPEP § 2106.05(g). Therefore, the office finds that the claim 17 is ineligible under 35 USC 101.
Claim 18 recites “The method of claim 1, comprising:
obtaining audio input as at least part of the action data; and
generating, using the audio input, the security rule for the monitoring system.”
The limitation further specifies obtaining audio input as at least part of the action data and generating the security rule for the monitoring system using the audio input. The limitation of “obtaining audio input …” merely recites insignificant extra-solution activity such as data gathering (i.e., obtaining/collecting data). See MPEP § 2106.05(g). The limitation of “generating, using the audio input, the security rule …,” which merely an extension of mental process. For example, a person is capable of considering information conveyed by audio input and establishing a security rule based on the information. See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 18 is ineligible under 35 USC 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for
the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 11-13, 15-16, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Metzler US20220157137A1.
Claim 1, Metzler teaches A method comprising:
presenting a virtual model of a property that includes a monitoring system ([0218] For example, an embodiment of a surveillance system for an automated detection of anomalies at a facility. The system comprises at least one surveillance sensor adapted to surveillance of at least one or more elements of the property or building and for a generation of real world surveillance data comprising information about the property or building. It also comprises a computing unit configured for an evaluation of the real world surveillance data and for an automatic classification of states, comprising a machine-learned classifier. Therein, the classifier is at least partially trained on training data which is at least partially synthetically generated and derived form a virtual model. [0225] … The virtual model can therein comprise a digital 3D-model of at least a portion or object of the property or building … [0227] … the surveillance sensor can in particular be … embodied with stationary surveillance equipment located at the property or building … [0232] Therein one step is a providing of a virtual model of at least one of the facility elements.);
obtaining action data from a computer generated avatar in the virtual model ([0504] First, a virtual scenario like described above, can easily be created and simulated by a synthetic modeling and optionally also by sampling plausible trajectories from real world observations. Sensor failures, partial obstructions, etc. can therein be incorporated naturally by omitting some parts of the sampled trajectory in the modeling. Besides those normal scenarios, there can also be abnormal scenarios derived from the simulation which comprises anomalies, e.g. trajectories 67 c of potential intruders or the like. For example, such abnormal constellations can be generated by deliberately disabling some natural constraint in the model. Such can comprise things like simulating a camera view of a trajectory 67 c which picks up a person in the second floor, without the person having passed the main door—because he entered the building through a window. The automatic state detection system is thereby able to pre-learn the thereof resulting characteristics in the surveillance data, which make up abnormal trajectories 67 … [0522] … As the person 70 p is modeled to be on its way on the regular pathway to the building 70 r … Examiner note: the synthetically modeled person/potential intruder corresponds to the claimed computer generated avatar, and the simulated trajectory representing movement of that person corresponds to the claimed action data obtained from the avatar in the virtual model);
generating a security rule for the monitoring system using the action data from the computer generated avatar in the virtual model ([0521] As shown in FIG. 43a , the generation of synthetic training data can … be established by combining multiple virtual objects … In this example, there is also a person 70 p. [0522] In FIG. 43b , two examples of a synthetically generated (rendered) training images according to this aspect are shown … As the person 70 p is modeled to be on its way on the regular pathway to the building 70 r, such a rendering is trained to be a normal state. [0523] In the lower image 64 n the synthesis was based on the same objects, but the person 70 p is trying to climb the building and break into the building through a window. Such is trained to be a high class critical security state, requiring immediate action. [0524] … a synthetically pre-trained detector and/or classifier, which is established by deriving a plurality of numerical renderings from a 2D- and/or 3D-model and feeding those renderings as training resource for a supervised learning of the classifier and/or detector … Thereby a generic classifier and/or detector is trained on virtual information. [0235] Specifically, some aspects therein concern a training method for a computation unit of a surveillance system according to this application, which comprises an automatic classifier and/or detector for security issues based on at least one survey sensor. Therein, the training comprises a synthetic generating of virtual training data by virtually deriving the training data from a virtual model, and a training of a classifier and/or detector which is done at least partially based on this training data which is at least partially synthetically generated. The result of the training, e.g. a trained classifier and/or detector or their configuration data, can then be used on real world data from the survey sensor for detecting security alerts. Examiner note: the reference teaches using behavior of a person modeled in a virtual environment to train a security surveillance system. Specifically, a modeled person traveling on a regular pathway is trained as a normal/uncritical state for which no alarm should be raised, whereas the modeled person climbing the building and attempting to enter through a window is trained as a critical security state requiring immediate action ([0521] - [0523]). The resulting trained detector/classifier or its configuration data embodies the learned security rule distinguishing the modeled person’s normal behavior from critical behavior and therefore corresponds to the claimed security rule generated using action data from the computer generated avatar in the virtual model. The reference further teaches that the virtual information trains the classifier/detector ([0524]), and that the resulting trained classifier/detector or configuration data is for the surveillance system and is subsequently used with real-world sensor data to detect security alerts); and
providing, to the monitoring system, the security rule to cause the monitoring system to generate, using the security rule and sensor data from the property received by the monitoring system, a security alert (See [0235] and [0521]-[0524] discussed above; [0233] By deploying the detector and/or classifier to a computation unit for analyzing real world surveillance data from at least one sensor at the building or property, a detecting of a potential presence of an instance of an anomalous state within the real world surveying data can be established … Examiner note: the reference teaches deploying the virtually trained detector/classifier to the computation unit of the surveillance system, wherein the resulting trained detector/classifier or its configuration data embodying the learned security rule corresponds to the security rule provided to the monitoring system. The reference further teaches using the training result or configuration data on real world data obtained from a survey sensor at the building or property for detecting security alerts. Thus, the surveillance system uses the learned security rule embodied in the trained detector/classifier or its configuration data, together with sensor data from the physical property to generate/detect a security alert).
Claim 2, Metzler teaches The method of claim 1, wherein generating the security rule using the action data comprises:
determining movement vectors of the avatar moving in the virtual model ([0504] First, a virtual scenario like described above, can easily be created and simulated by a synthetic modeling and optionally also by sampling plausible trajectories from real world observations. Sensor failures, partial obstructions, etc. can therein be incorporated naturally by omitting some parts of the sampled trajectory in the modeling. Besides those normal scenarios, there can also be abnormal scenarios derived from the simulation which comprises anomalies, e.g. trajectories 67 c of potential intruders … [0516] Artificial training data generation can be implemented … at an abstract level, like by simulating abstracted trajectories within the floor plan which are trained for a detector and/or classifier working on (also at least partial) persons trajectories 67 … Examiner note: the simulated trajectories of persons within the virtual floor plan correspond to the claimed movement vectors, as the trajectories represent movement of the molded persons through the virtual model); and
determining, using the movement vectors, regions represented in the virtual model accessed by the avatar ([0517] Applied to the example of FIG. 41, such a monitoring of an office complex is illustrated, in which exemplary trajectories 67 a,67 b,67 c of people moving between rooms are shown. The person, who has caused the dashed trajectory 67 a entered the building through the main door 68 before going to his/her office. The same person then visited another room along 67 b. [0504] … simulating a camera view of a trajectory 67 c which picks up a person in the second floor, without the person having passed the main door … Examiner note: the reference teaches using the person’s trajectories to identify movement between rooms, including movement from the main entrance to an office and subsequently to another room. Accordingly, the rooms traversed or entered along the trajectories correspond to the claimed regions represent int eh virtual model accessed by the avatar).
Claim 3, Metzler teaches The method of claim 1, wherein generating the security rule using the action data comprises:
generating a virtual image stream representing images from a viewpoint corresponding to a real camera of the monitoring system of the property ([0496] … In a 3d modeling software, the environment of the warehouse can be created with the desired monitored object comprised as a virtual model of the door … This can include for example … point of view and frustum of the sensors which are used to capture the warehouse scene. On this basis, physically correct materials can be applied in creating photo-realistic render images. [0491] … there are many parameters which can be varied, such as the point of view 62 … Thereby, a whole series of virtual or synthetic training data can be synthesized … Examiner note: the reference teaches generating photo realistic rendered images from a virtual model of the monitored environment, wherein the virtual rendering is configured according to the point of view and frustum of the sensors used to capture the warehouse scene ([0496]). The reference further teaches generating a whole series of the virtual or synthetic images while varying the point of view ([0491]). Thus, the series of rendered virtual images corresponds to the claimed virtual image stream representing images from a viewpoint corresponding to a real camera of the monitoring system of the property); and
generating, using the virtual image stream, the security rule ([0491] … A computation system can generate thousands and more of such training data items which are according to this aspect used to train the security system. For example, to automatically evaluate the real world camera images … which has been trained on the virtual training data … [0235] … the training comprises a synthetic generating of virtual training data by virtually deriving the training data from a virtual model, and a training of a classifier and/or detector … based on this training data … The result of the training, e.g. a trained classifier and/or detector or their configuration data, can then be used on real world data from the survey sensor for detecting security alerts. Examiner note: the reference teaches using the virtually generated image data to train the security system ([0491]), resulting in a trained detector/classifier or its configuration data that is subsequently used to evaluate real-world surveillance data for detecting security alerts ([0235]). The trained detector/classifier or its configuration data embodying the learned security condition corresponds to the claimed security rule generated using the virtual image steam).
Claim 4, Metzler teaches The method of claim 1, comprising:
obtaining monitoring information of the monitoring system of the property ([0287] The location or position of each surveillance sensors 4 resp. the location of the building element 5 the sensor 4 surveys is known by the central computing unit 2 wherefore in case of a mobile surveillance sensor 43 the surveillance sensor 4 transmits its location data to the central computing unit 2 by the communication means 5.); and
generating the virtual model of the property using the monitoring information of the monitoring system of the property ([0496] … In a 3d modeling software, the environment of the warehouse can be created with the desired monitored object comprised as a virtual model of the door … This can include for example a visual appearance due to material properties (i.e. metal surfaces, wood or plastic), lighting configurations, point of view and frustum of the sensors which are used to capture the warehouse scene. Examiner note: the reference teaches that the central computing unit knows the location or position of each surveillance sensor and the location of the building element surveyed by the sensor ([00287]). The reference further teaches creating the corresponding warehouse environment in 3D modeling software using parameters including the point of view and frustum of the sensors used to capture the warehouse scene ([0496]). Thus, the surveillance sensor information defining where and what portion of the property is monitored is used to establish the sensor viewpoint and viewing region represented in the created 3D environment).
Claim 11, Metzler teaches The method of claim 1, wherein generating the security alert using the security rule comprises:
transmitting sensor data from the monitoring system of the property to a device of the monitoring system, wherein the sensor data represents an action satisfying one or more thresholds of the security rule ([0218] The system comprises at least one surveillance sensor adapted to surveillance of at least one or more elements of the property or building and for a generation of real world surveillance data comprising information about the property or building. It also comprises a computing unit configured for an evaluation of the real world surveillance data and for an automatic classification of states … [0419] … a state detection algorithm, i.e. a common event detector associated to all of the surveillance sensors of the monitoring site, may be stored on a local computing unit 302 and be configured to process surveying data associated to the monitoring site. [0420] Incoming states 301 are then classified by a local state filter 303, e.g. wherein the state filter 303 provides an initial assignment 304 of the state 301 into three classes: “critical state” 305, e.g. automatically raising an alert 306, “uncritical state” 307, e.g. raising no automatic action 308, and “uncertain state” 309 … [0422] By way of example, the state filter 303 may be based on a normality-anomaly classification model in an n-dimensional state-space wherein a state is represented by an n-dimensional state-vector, in particular wherein a respective class is represented by a section of the n-dimensional state-space. Examiner note: the reference teaches surveillance sensors generating real-world surveillance data concerning the monitored property and providing the surveillance data for processing by a local computing unit. The surveillance data represents detected events or states, which are evaluated by a state filter according to defined classification regions in an n-dimensional state space. Then the reprehended state falls within the critical state classification, an alert is automatically raised. Thus, the classification boundary defining a critical state corresponds to the claimed threshold to the security rule, and the sensor/surveillance data representing a state satisfying that threshold results in generation of the security alert).
Claim 12, Metzler teaches The method of claim 1, wherein generating the security rule using the action data comprises:
determining a plurality of thresholds including one or more thresholds for movement of an object and one or more thresholds for features of the object ([0288] The states can be structured hierarchically, e.g. a person detected in a corridor can stand still, walk, run, crawl, etc, describing for example instead of an even “Person detected” “Running person detected”, “Walking person detected”, etc. [0309] … For example, the time interval between “door 51 c opened” and “door 51 a opened” is determined and it is evaluated to what degree this time interval of a detected pattern deviates from previously measured time intervals or not and/or might be a criterion for criticality in itself, e.g. when the time interval depasses a certain time limit. [0517] The person who caused the dash-dotted trajectory 67 c was not observed entering the building through the main entrance 68. This person could be a potential intruder that made his/her way in through a window 69. A classifier trained as described above is configured to identify such a state and to automatically raise a therefore specific alarm or action. [0518] Such can e.g. comprise a person detection, in which the real world or simulated person images are automatically altered in such a way as to at least partially occlude the face or the body of a person, as it might be the case with an intruder wearing a mask. [0447] The machine learning system can therein provide the basic framework for learning such, actual functions, thresholds, etc. are substantially machine learned and not purely hand-coded by a human programmer. Examiner note: the reference teaches determining learned thresholds for classifying security relevant states, The states include movement related characteristics, such as whether a person stands, walks, runs, crawls, or follows an anomalous trajectory, and feature related characteristics, such as appearance of a person including whether the person’s face or body is occluded. The reference further teaches that the functions and thresholds used for the classification are machine learned. Accordingly, the learned criteria for distinguishing the disclosed movement states and object/person features correspond to the claimed thresholds for movement of an object and threshold for features of the object).
Claim 13, Metzler teaches The method of claim 12, wherein determining the plurality of thresholds comprises:
determining the one or more thresholds for movement including a position threshold indicating whether a person is moving on or off a pathway ([0517] The person who caused the dash-dotted trajectory 67 c was not observed entering the building through the main entrance 68. This person could be a potential intruder that made his/her way in through a window 69. A classifier trained as described above is configured to identify such a state and to automatically raise a therefore specific alarm or action. Examiner note: the reference teaches evaluating the position and trajectory of a virtual person relative to an expected route into the monitored property, distinguishing a person entering through the main entrance from a person following an anomalous trajectory associated with entry through a window. The spatial boundary between the expected route and the anomalous route corresponds to the claimed position threshold indicating whether a person is moving on or off a pathway).
Claim 15, Metzler teaches The method of claim 1, wherein generating the virtual model of the property comprises:
generating a virtual representation of a home and one or more elements within a threshold distance of the home ([0521] As shown in FIG. 43a , the generation of synthetic training data can in one of the possible embodiments be established by combining multiple virtual objects, preferably in a plurality of different combinations, with the virtual objects themselves varied … The figure also shows disturbances 70 f, for example grass growing in front of the building. In this example, there is also a person 70 p. [0522] In FIG. 43b , two examples of a synthetically generated (rendered) training images according to this aspect are shown. The upper image 64 n shows a normal example … There is a foreground 70 f, a background of the building 70 r to be surveyed and sky as well as a person 70 p. As the person 70 p is modeled to be on its way on the regular pathway to the building 70 r, such a rendering is trained to be a normal state. [0523] In the lower image 64 n the synthesis was based on the same objects, but the person 70 p is trying to climb the building and break into the building through a window. Examiner note: the reference teaches generating a virtual/rendered representation comprising a building together with surrounding elements, including foreground/grass, a person, and a pathway leading to the building. The person is further virtually represented at different positions relative to the building, including on the pathway to the building and immediately adjacent to the building while attempting to enter through a window. Under the Broadest reasonable interpretation, these virtually represented surrounding elements positioned proximate to the building correspond to the claimed one or more elements within a threshold distance of the home).
Claim 16, Metzler teaches The method of claim 1, wherein obtaining the action data from the computer generated avatar in the virtual model comprises:
obtaining actions of the computer generated avatar walking off a path at the property in the virtual model of the property (([0521] As shown in FIG. 43a , the generation of synthetic training data can in one of the possible embodiments be established by combining multiple virtual objects, preferably in a plurality of different combinations … For example, there can be one or more backgrounds 70 r, either as a 3D-model or as a 2D image, in particular a picture of the specific site on which the security system will be installed … The figure also shows disturbances 70 f, for example grass growing in front of the building. In this example, there is also a person 70 p. [0522] … The upper image 64 n shows a normal example which is tagged as normal or uncritical and for which no alarm should be raised. There is a foreground 70 f, a background of the building 70 r to be surveyed and sky as well as a person 70 p. As the person 70 p is modeled to be on its way on the regular pathway to the building 70 r, such a rendering is trained to be a normal state. [0523] In the lower image 64 n the synthesis was based on the same objects, but the person 70 p is trying to climb the building and break into the building through a window. Such is trained to be a high class critical security state, requiring immediate action. Examiner note: the reference teaches synthetically generating a virtual person within a virtual representation of the monitored property. The reference distinguishes a normal virtual action in which the person is modeled as traveling on the regular pathway to the building from an abnormal virtual action in which the same type of virtual person departs from the normal pathway behavior and instead moves to the building and attempts to enter through a window. Under the broadest reasonable interpretation, the latter simulated action corresponds to the claimed computer generated avatar walking off a path at the property in the virtual model).
The elements of claims 19 and 20 are substantially the same as those of claim 1. Therefore, the elements of claims 19 and 20 are rejected due to the same reasons as outlined above for claim 1. The additional limitations of Claims 19 and 20: One or more non-transitory computer-readable media storing one or more instructions executable by a computer system to perform operations comprising, and A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations (See Metzler [0159] and [0285]).
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.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to claim 1
above, and further in view of Chavez US20130050199A1.
Claim 5, Metzler fails to teach, but Chavez teaches The method of claim 1, comprising:
obtaining user input provided by a user input device representing the action data of the computer generated avatar in the virtual model, wherein the computer generated avatar is controlled using the user input ([0066] The user's avatar, and possibly other avatars, is/are able to move within the virtual reality environment in response to commands received from the user, such as key strokes, mouse cursor movements, user gestures or bodily movements, and the like. [0068] For example, a user can see a representation of a portion of the computer-generated virtual reality environment on a display and input commands via his or her user input device, such as a mouse or keyboard. The user interface module 212 receives the command(s) and other input(s) from the user and passes the user input to the virtual reality environment rendering module 208. The virtual reality environment rendering module 208 causes the user's avatar or other object under the control of the user to execute the desired action in the virtual reality environment. Examiner note: the reference teaches obtaining user input provided by a user input device representing action data of a computer generated avatar in a virtual model because the user interface module receives commands and other inputs from a user input device, such as mouse or keyboard, and passes the user input to the virtual reality environment rendering module, which causes the user’s avatar to execute the desired action in the virtual reality environment. The reference further teaches that the avatar moves in response to user commands, including keystrokes, mouse movements, user gestures, or bodily movements).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Chavez, and apply user commands received through a user input device to cause an avatar to execute a desired action in a virtual reality environment, in order to facilitate generating different desired avatar behaviors and corresponding virtual scenarios for training the monitoring system, thereby improving the system’s ability to recognize different behaviors encountered during monitoring.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to claim 1
above, and further in view of Cobb US20130242093A1.
Claim 6, Metzler teaches one or more actions represented in the action data (See Metzler, [0504] and [0521]-[0523]). However, Metzler fails to teach, but Cobb teaches The method of claim 1, comprising:
obtaining user input requesting that one or more actions trigger one or more security alerts, wherein the one or more security alerts include the security alert ([0022] In one embodiment, a user may create an alert directive to override the normal alert publication process. An alert directive allows a user to provide feedback to the machine learning engine to either always or never create an alert for a certain behavioral event. [0023] To create an alert directive, a user selects an event occurrence or an alert previously generated by the system to use as a template … After selecting an alert, the user defines alert directive matching criteria. In one embodiment, the criteria may include whether the behavior should always or never result in an alert … Once the user has defined the matching criteria, the user interface creates an alert directive in the alert database with references pointing back to the original alert used to create it. Thereafter, the user interface sends information about the alert directive to the machine learning engine. [0027] … When the machine learning engine processes information of subsequent events that matches an alert directive's match criteria and tolerances, the machine learning engine bypasses the normal publication methods of the behavioral recognition system and immediately publishes an alert or discards the event (given the matching criteria) … Examiner note: the reference teaches obtaining user input by which a user selects a behavioral event an defines an alert directive specifying whether the selected behavior should always result in an alert. Then a subsequent occurrence of that behavior matches the user defined alert directive, the system immediately publishes an alert.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Cobb, and allow a user to select an action represented by the simulated action data and specify that occurrence of the selected action triggers a security alert, in order to enable the surveillance system to identify user designated behaviors as security relevant events and provide an alert when the behavior occurs, thereby reducing unwanted alerts while ensuring the user identified security critical behaviors result in an alert.
Claim(s) 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to
claim 1 above, and further in view of Chapman US20140059641A1.
Claim 7, Metzler further teaches The method of claim 1, comprising:
providing the security rule to a device of the monitoring system ([0520] The resulting detector and/or classifier can then be loaded by the surveillance system, which is thereby configured to detect and/or classify such security events based on real world pictures taken by an automatic building or property surveillance system—which had been specifically trained on the virtually generated visual (or thermal) appearance of such events. [0233] By deploying the detector and/or classifier to a computation unit for analyzing real world surveillance data from at least one sensor at the building or property, a detecting of a potential presence of an instance of an anomalous state within the real world surveying data can be established …);
([0154] In addition, a local state filter may be dynamically updated from the updated common classification model, i.e. representing a global state filter. For example, a local operator familiar to local pre-requisites may decide which differences between the global event filter and the local state filter are taken over into the local event filter. Alternatively, this update can be performed automatically, e.g. based on rules, in particular based on consistency checks. [0156] In another embodiment, the security monitoring system is configured to update each state filter based on at least part of the common update information, namely that for each event filter the corresponding first class and the corresponding second class are established based on the common update information, … [0148] Furthermore, a monitoring system locally installed on a particular facility may be part of an extended network of many local monitoring systems running on a plurality of different monitoring sites, each local security monitoring system providing update information to a global event detection algorithm and/or a global event classification model.).
However, Metzler fails to teach obtaining feedback of the security rule from the device; and
updating, in response to obtaining the feedback of the security rule, a security rule database.
Chapman teaches obtaining feedback of the security rule from the device ([0031] Logger 180 creates security logs of all authorized and unauthorized activity such as violations of pre-existing rules or the sending of an authorized email … The created logs are stored in log(s) 190 and a copy of the security data and/or logs is sent to computer 110 and stored in storage device 130. Rule feedback program 120 uses the copy of the security data and/or logs, stored in storage device 130, during the automatic generation of feedback for a proposed security rule.); and
updating, in response to obtaining the feedback of the security rule, a security rule database ([0033] The statistical analysis may provide motivation for the authoring of a new security rule or for the modification an existing rule … a user, who happens to be a manager, may use the report presented by rule feedback program 120 to help determine if existing IDPS security rules require an update, for example, because they are impractical (over-inclusive) or ineffective (under-inclusive) based on the number of matches in the log records. After determining that the existing rules are in need of an update, the manager sends out a work order to the rule author to update the security rule 140. [0041] … rule feedback program 120 finalizes the new rule and saves it to security rule 140. The new security rule may be accessed by computer 160, enabled and begin to function as a security rule for IDPS 170. [0030] In this embodiment, storage device 130 is the storage location for security rules as they are written, after they have been finalized, and after the rule has been enabled for deployment on IDPS 170. Computer 160 has access to security rule 140 through computer 110. After a new security rule has been finalized, computer 110 pushes the security rule to computer 160 for storage at and usage during actual log processing by IDPS 170. Examiner note: the reference teaches obtaining security data/logs generated from operation of security rules at computer 160, sending the security data/logs back to computer 110, and using the returned data to generate feedback concerning a security rule. The reference further teaches determining from the feedback that an existing security rule requires updating, updating the security rule, and saving the resulting rule for subsequent access and use).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Chapman, and apply obtaining feedback regarding operation of a security rule and update the security rule based on the feedback, in order to improve the effectiveness of the security rule by identifying and correcting rules that are over inclusive or under inclusive based on actual monitored activity.
Claim 8, Metzler fails to teach, but Chapman teaches The method of claim 7, wherein updating the security rule database comprises:
storing the security rule in the security rule database ([0030] In this embodiment, storage device 130 is the storage location for security rules as they are written, after they have been finalized, and after the rule has been enabled for deployment on IDPS 170. Computer 160 has access to security rule 140 through computer 110. After a new security rule has been finalized, computer 110 pushes the security rule to computer 160 for storage at and usage during actual log processing by IDPS 170. [0024] In this embodiment, rule editor program 150, is a rule editing/authoring program. Rule editor program 150 includes a number of rule templates for the creation of IDPS security rules as well as a copy of previously created rules which have been or are currently deployed on IDPS 170.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Chapman, and apply maintaining finalized and deployed security rules in a storage location accessible by the security system, in order to facilitate subsequent access, deployment, and use of the security rules during monitoring operations.
Claim 9, Metzler fails to teach, but Chapman teaches The method of claim 7, comprising:
updating, in response to obtaining the feedback of the security rule, the security rule ([0033] … a user, who happens to be a manager, may use the report presented by rule feedback program 120 to help determine if existing IDPS security rules require an update, for example, because they are impractical (over-inclusive) or ineffective (under-inclusive) based on the number of matches in the log records. After determining that the existing rules are in need of an update, the manager sends out a work order to the rule author to update the security rule 140.); and
updating the security rule database by storing the updated security rule in the security rule database ([0030] In this embodiment, storage device 130 is the storage location for security rules as they are written, after they have been finalized, and after the rule has been enabled for deployment on IDPS 170. [0041] In this embodiment, in step 270, rule feedback program 120 finalizes the new rule and saves it to security rule 140. The new security rule may be accessed by computer 160, enabled and begin to function as a security rule for IDPS 170.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Chapman, and apply revising an existing security rule based on feedback indicating that the rule is over inclusive or under inclusive and maintaining the resulting rule in the security rule storage, in order to improve the effectiveness of the security rules by correcting rules determined to be impractical or ineffective based on monitored activity.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler and Chapman as
applied to claim 7 above, and further in view of Chavez US20130050199A1.
Claim 10, Metzler and Chapman fail to teach, but Chavez teaches The method of claim 7, wherein providing the security rule to the device of the monitoring system comprises:
providing data indicating a virtual representation of the avatar performing an action in the virtual model to a device of a user ([0063] … An avatar's view, which can be presented to a corresponding user, can itself depend on the location and/or orientation of the avatar within the virtual reality environment. For example, the avatar's view may depend on the direction in which the avatar is facing and the selected viewing option, such as whether the user has opted to have the view appear as if the user were looking through the eyes of the avatar or whether the user has opted to pan back from the avatar to see a three-dimensional view of where the avatar is located and what the avatar is doing in the three-dimensional computer-generated virtual reality environment. [0067] The user interface module 212 receives command and request inputs from the supervisor communication device 104 and/or communication device(s) (not shown) associated with a set of monitored entities 116 a-n and provides information, such as a visual rendering of the virtual reality environment (received from the virtual reality environment rendering module 208) and contact center, predictive dialer, and/or call center information or component thereof information (received from the information manager 204), to the supervisor communication device 104 and/or a communication device (not shown) associated with a selected monitored entity 116 a-n. [0068] For example, a user can see a representation of a portion of the computer-generated virtual reality environment on a display and input commands via his or her user input device, such as a mouse or keyboard. The user interface module 212 receives the command(s) and other input(s) from the user and passes the user input to the virtual reality environment rendering module 208. The virtual reality environment rendering module 208 causes the user's avatar or other object under the control of the user to execute the desired action in the virtual reality environment. In this way, the user may control a portion of the virtual reality environment, such as his or her avatar or other objects in contact with the avatar, to change the virtual reality environment.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler and Chapman to incorporate the teachings of Chavez, and apply a visual rendering showing the avatar and tis activity within the computer generated virtual environment through a user’s communication device, in order to allow the user to visually observe the avatar’s activity and more effectively monitor the represented activity within the virtual environment.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to claim
1 above, and further in view of Shuster US20130047098A1.
Claim 14, Metzler further teaches The method of claim 1, wherein generating the security rule comprises:
determining, using the action data from the computer generated avatar in the virtual model, an action
generating the security rule that ([0491] The computation system can therein not only generate static scenes but also sequences or scenarios of security related courses of events to be detected and/or classified. [0522] As the person 70 p is modeled to be on its way on the regular pathway to the building 70 r, … [0523] … the person 70 p is trying to climb the building and break into the building through a window. Such is trained to be a high class critical security state, requiring immediate action.).
However, Metzler fails to teach detecting input indicating an identifier for a specific person; determining an action for the specific person; the security rule that (i) includes the identifier for the specific person and (ii) identifies the action that applies to the specific person using the identifier.
Shuster teaches detecting input indicating an identifier for a specific person ([0218] At block 1301, the service provider identifies an entity attempting to take an action on a securable object. The attempt may be identified in response to a request submitted by a user to have that user's avatar or other entity take the desired action. [0209] … An entity may represent a user or other individual capable of taking actions within the virtual world. Examples of entities may include user account 1107, persona object 1108, and avatar object 1109. Examiner note: the user request constitutes input indicating the particular entity/avatar associated with the user, and the service provider identifies that entity in response to the request. As [0209] explains, the identified entity represents a user or other individual in the virtual world. Thus, the request provides input indicating an identifier for the specific person represented by the identified virtual world entire); determining an action for the specific person ([0218] the service provider identifies an entity attempting to take an action on a securable object. [0214] a permission record is applicable to an action when the entity attempting the action matches entity pattern 1203, the action being attempted matches action pattern 1204, and the securable object to be acted upon matches securable pattern 1205. Examiner note: the reference teaches determining both which identified entity is attempting the action and which action is being attempted, and matches the identified entity and its attempted action against corresponding entity and action patterns. Accordingly, the action is determined for the particular identified person/entity, rather than determining an action without regard to the person performing it); the security rule that (i) includes the identifier for the specific person and (ii) identifies the action that applies to the specific person using the identifier ([0214] Permission record 1201 may further include entity pattern 1203, action pattern 1204, and/or securable pattern 1205 … [0226] … The first permission record 1402 indicates that an entity named “Bob” is not permitted to take the action of hitting securable objects that are trees. Examiner note: the permission record corresponds to the claimed security rule because it defines whether a particular action is permitted for a particular identified entity. Specifically, permission record 1402 includes the particular entity identifier “Bob” and identifies the action “hitting” that applies to Bob. Thus, the rule associates the identified action with the specific person through the person’s identifier, rather than merely specifying action independently of the person).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Shuster, and apply a virtual world entity representing a user with an action attempted by that entity and using permission records having corresponding entity and action pattern, in order to enable security rules generated from the virtual actions to distinguish which action are applicable to particular identified users, thereby providing person specific control over actions within the virtual environment.
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to claim
1 above, and further in view of Madden US20190354875A1.
Claim 17, Metzler fails to teach, but Madden teaches The method of claim 1, comprising:
providing data captured by the monitoring system of the property to a device of a user ([0042] For example, the user device 106 a may be configured to display a user interface (e.g., a web page) provided by the home monitoring system 104 that enables a user to perceive images captured by the home surveillance device 106 b (e.g., a camera) and/or reports related to the home monitoring system 104 (e.g., notifications generated by notification generator 114).); and
obtaining feedback from the device of the user indicating if the data captured by the monitoring system of the property includes acceptable or not acceptable actions ([0052] … A rule may be defined, for example, based on observations of established relationships in the graph-based knowledge base. In one example, a rule can be “Jane and Jane's phone exit the house together,” where Jane and her phone are observed exiting the house together ten times over the course of ten days … The sub-graph can then be used to generate a rule, where a violation of the rule (e.g., Jane leaves the house without Jane's phone) will trigger a notification. [0053] In some implementations, a user can define a rule … A user can generate a rule where the home monitoring system 104 generates a notification if “Bob” is ever detected in “Jane's car.” [0054] Based in part on activity data 116, the rule evaluator 112 can determine that a rule 118 is being executed (212) … One or more notifications responsive to the rule 118 is generated (214) and presented to the user (216). [0055] The home monitoring system 104 can receive user feedback responsive to the notification from a user (218), and updates the one or more rules 118 based in part on the user feedback (220). For example, a user can choose to dismiss a notification based on a rule, which may cause the home monitoring system to interpret the rule as unimportant to the user. Examiner note: the reference teaches monitored activity data can indicate an action or activity that satisfies or violates a monitoring rule, causing a notification to be presented to the user, and that the user can provide feedback responsive to that notification, including dismissing the notification as unimportant. Under BRI, this teaches obtaining user feedback indicating whether an action represented by the monitored activity is considered acceptable or not acceptable).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Madden, and apply the home monitoring functionality of presenting captured surveillance information to a user and receiving the users’ s feedback regarding monitored activities and associated notifications, in order to allow the monitoring system to account for the user’s assessment of monitored activity and distinguish activity that warrants attention from activity that the user considers unimportant, thereby reducing unnecessary notifications and improving the relevance of the monitoring system’s responses.
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler as applied to claim
1 above, and further in view of Heppner US10721280B1 and Warren US20150324706A1.
Claim 18, Metzler fails to teach, but Heppner teaches The method of claim 1, comprising:
obtaining audio input as at least part of the action data (Col.20, lines 2-12, “The modeling and rendering server 112b may generate an avatar for a first user at block 212, and the first user's avatar may be associated with and linked to the first device 102 and the activities including speech, movement, and interaction with the environment and other avatars. At block 214, the modeling and rendering server 112b may generate an avatar for a second user, and the second user's avatar may be associated with and linked to the second device 104 and the activities including speech, movement, and interaction with the environment and other avatars.” Col.2 lines 47-55, “receives, from at least one of the first wearable electronic communication device and the second wearable electronic communication device, an at least one action input, wherein the at least one action input comprises a motion input or a speech input; and updates, based on the at least one action input, at least one of the first avatar or the second avatar with at least one of an action associated with the motion input or an action and an audio indication associated with the speech input.” Col.6, lines 46-51, “Users coupled to the wearable devices may have their speech and movement mimicked by their avatar in the virtual environment and may observe the avatars' associated with other users in real time, including leg movement, arm movement, torso movement, and facial movement/expressions.” Examiner note: the reference teaches generating an avatar in a virtual environment and associating the avatar with activities including speech, movement and interaction with the environment and other avatars. The reference also identifies speech input as “action input” and teaches updating the corresponding avatar based on the action input. The reference further teaches that the user’s speech is mimicked by the avatar in the virtual environment. Thus, the speech input constitutes at least part of the action input associated with the avatar’s activity in the virtual environment.); and
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler to incorporate the teachings of Heppner, and obtain speech input as action input associated with the avatar, in order to enable virtual security simulation to account for speech related activity in addition to physical movement when representing the action of a person, thereby enabling the surveillance system to account for a broader and more complete representation of person behavior when detecting security relevant activity.
However, Metzler and Heppner fail to teach generating, using the audio input, the security rule for the monitoring system.
Warren teaches generating, using the audio input, the security rule for the monitoring system ([0059] Rule generation module 510 may receive the rules instructions from the user and generate at least one rule for the home automation system in response to the received instructions … the user may provide audible instructions related to setting a rule (e.g., “lock the front door every time Bill arms the system”), … [0067] At block 705, method 700 includes receiving a spoken command having a plurality of rule setting terms. Block 710 includes establishing at least one operation rule for the home automation system based on the spoken command. At block 710, the method 700 includes storing the at least one operation rule for later use by the automation and security system. Examine note: the reference teaches receiving audible/spoken input containing rule setting information and establishing an operation rule based on the spoken input, wherein the generated rule is subsequently used by the automation and security system).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Metzler and Heppner to incorporate the teachings of Warren, and apply audible instructions containing rule setting information to the speech input associated with the avatar to generate a security rule for the monitoring system, in order to allow the system to define security related monitoring conditions based on the audio input, thereby simplifying rule creation and enabling user defined monitoring conditions without requiring manual entry through a conventional interface.
Conclusion
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/YI. HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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