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 instant application having application No. 18/736,034 of CURIEL for “SECURITY SYSTEMS AND METHODS FOR DETECTING HAZARDS USING SMART SENSORS” filed June 06, 2024 has been examined.
Drawings
Drawings Figures 1-7 submitted on June 06, 2024 are in compliance with the provisions of 37 CFR 1.121(d).
Information Disclosure Statements
The information disclosure statements (IDSs) submitted June 07, 2024 and January 17, 2025 are being considered by the examiner.
Claim Rejections - 35 USC § 102/103
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 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.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4, 7-9, 11, 14-15, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the Applicant Submitted Prior Art of LSNYDER et al. (U.S. Publication No. 2024/0311925 A1) hereinafter “Snyder”.
As to claim 1, Snyder discloses a sensor (detection sensor 104 Figure 1 [i.e. a sensor], shown in Figure 1 and described in Paragraph 0015), for detecting hazards (hazards detected [i.e. detecting hazards], described in Paragraphs 0015-0016), the sensor comprising: at least one memory (one or more memory components 306 [i.e. at least one memory], shown in Figure 3 and described in Paragraphs 0015-0016 and 0042-0043) with instructions stored thereon; and at least one processor (one or more processors 304 [i.e. at least one processor], shown in Figure 3 and described in Paragraphs 0015-0016) in communication with the at least one memory, wherein the instructions (machine readable instructions [i.e. instructions], described in Paragraphs 0015 and 0042-0043), when executed by the at least one processor (described in Paragraphs 0015 and 0042-0043), cause the at least one processor to: generate sensor profile data associated with a location proximate to the sensor based upon sensor data generated by the sensor (sensing to detect whether a user is in a near proximity to the monitored item by the risk detection sensor 104 [i.e. generate sensor profile data associated with a location proximate to the sensor based upon sensor data generated by the sensor], described in Paragraph 0019); apply the sensor profile data to a sensor model profile associated with the location and stored in the at least one memory (historical loss data associated with a plurality of users, historical loss data associated with the one or more monitored items, risk data associated with the one or more monitored items [i.e. apply the sensor profile data to a sensor model profile associated with the location and stored in the at least one memory], described in Paragraph 0020); wherein the sensor model profile includes a plurality of parameter levels for the location proximate to the sensor generated by a machine learning model; identify a discrepancy between the sensor profile data and the sensor model profile; determine a potential hazard at the location based upon the discrepancy between the sensor profile data and the sensor model profile; and generate an alert based upon the potential hazard at the location (determine one or more risk factors with respect to a monitored item based on one or more sensors, determine, via a risk model, a risk score indicative of a risk of a danger event based on the one or more risk factors, detect whether the risk score indicative of the risk of the danger event is above a threshold risk score, and generate a risk event detection when the risk score indicative of the risk of the danger event is above the threshold risk score. The machine readable instructions may further cause the intelligent risk detection system to perform at least the following when executed by the one or more processors: transmit a risk detection alert based on generation of the risk event detection, activate a risk mitigation activation sensor based on the risk detection alert, and determine, via the risk model and based on the one or more risk factors, whether an updated risk score indicative of the risk of the danger event is below the threshold risk score such that the risk of the danger event is determined to be mitigated [i.e. wherein the sensor model profile includes a plurality of parameter levels for the location proximate to the sensor generated by a machine learning model; identify a discrepancy between the sensor profile data and the sensor model profile; determine a potential hazard at the location based upon the discrepancy between the sensor profile data and the sensor model profile; and generate an alert based upon the potential hazard at the location], described in Paragraph 0056).
As to claim 2, Snyder’s disclosure as set forth above in claim 1, and further Snyder discloses wherein the sensor is in communication with a plurality of sensors associated with the location, the plurality of sensors being different from the sensor, and wherein the instructions further cause the at least one processor to: receive additional sensor data from the plurality of sensors; and generate the sensor profile data further based upon the additional sensor data (sensors 106, shown in Figures 1, 2 and described in Paragraphs 0019 and 0028).
As to claim 4, Snyder’s disclosure as set forth above in claim 1, and further Snyder discloses wherein the instructions further cause the at least one processor to: receive an input indicating that normal conditions are present at the location; generate initial sensor profile data based upon the location; input the initial sensor profile data to the machine learning model; receive the sensor model profile as an output from the machine learning model; and store the sensor model profile in the at least one memory as being associated with the location (described in Paragraph 0021).
As to claim 7, Snyder’s disclosure as set forth above in claim 1, and further Snyder discloses the sensor of claim 1, wherein the instructions further cause the at least one processor to: determine a severity level of the potential hazard at the location; and determine the alert from a plurality of alert options based upon the severity level, wherein the plurality of alert options include an audible alert outputted by the sensor and an alert message transmitted by the sensor to a computer device associated with the location (described in Paragraph 0021).
As to claim 8, the claim recites a system that parallels the system claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 8. Accordingly, claim 8 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 1.
As to claim 9, the claim recites a method that parallels the system claim 2. Therefore, the analysis discussed above with respect to claim 2 also applies to claim 9. Accordingly, claim 9 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 2.
As to claim 11, the claim recites a method that parallels the system claim 4. Therefore, the analysis discussed above with respect to claim 4 also applies to claim 11. Accordingly, claim 11 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 4.
As to claim 14, the claim recites a method that parallels the system claim 7. Therefore, the analysis discussed above with respect to claim 7 also applies to claim 14. Accordingly, claim 14 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 7.
As to claim 15, the claim recites a computer-implemented method that parallels the system claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 15. Accordingly, claim 15 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 1.
As to claim 17, the claim recites a method that parallels the system claim 4. Therefore, the analysis discussed above with respect to claim 4 also applies to claim 17. Accordingly, claim 17 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 4.
As to claim 20, the claim recites a method that parallels the system claim 7. Therefore, the analysis discussed above with respect to claim 7 also applies to claim 20. Accordingly, claim 20 is rejected by the prior art of Snyder under the same rationale as set forth above with respect to claim 7.
Allowable Subject Matter
Claims 3, 5-6, 10, 12-13, 16 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claim/s.
The following is an examiner’s statement of reasons for allowance:
As to claim 3, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 3, comprising limitations: wherein the plurality of sensors includes an electric vehicle (EV) sensor for monitoring charging of an EV, and wherein the potential hazard is associated with a potential electrical hazard associated with the charging of the EV, i.e. in the particular manner claimed is not taught or suggested in the prior art.
As to claim 5, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 5, comprising limitations: wherein the instructions further cause the at least one processor to: receive another input indicating that the sensor has been moved to a different location; generate updated sensor profile data associated with the different location based upon updated sensor data generated by the sensor proximate to the different location; input the updated sensor profile data to the machine learning model; receive an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and store the updated sensor model profile in the at least one memory as being associated with the different location, i.e. in the particular manner claimed is not taught or suggested in the prior art.
As to claim 10, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 10, comprising limitations: wherein the plurality of sensors includes an electric vehicle (EV) sensor for monitoring charging of an EV, and wherein the potential hazard is associated with a potential electrical hazard associated with the charging of the EV, i.e. in the particular manner claimed is not taught or suggested in the prior art.
As to claim 12, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 12, comprising limitations: wherein the instructions further cause the at least one processor to: receive another input indicating that the sensor has been moved to a different location; generate updated sensor profile data associated with the different location based upon updated sensor data generated by the sensor proximate to the different location; input the updated sensor profile data to the machine learning model; receive an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and store the updated sensor model profile in the at least one memory as being associated with the different location, i.e. in the particular manner claimed is not taught or suggested in the prior art.
As to claim 16, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 16, comprising limitations: wherein the sensor is in communication with a plurality of sensors associated with the location, the plurality of sensors being different from the sensor, and the computer-implemented method further comprising: receiving additional sensor data from the plurality of sensors, wherein the plurality of sensors includes an electric vehicle (EV) sensor for monitoring charging of an EV, and wherein the potential hazard is associated with a potential electrical hazard associated with the charging of the EV; and generating the sensor profile data further based upon the additional sensor data, i.e. in the particular manner claimed is not taught or suggested in the prior art.
As to claim 18, is allowed over prior art(s) of record because none of the prior arts have been found that individually or in combination with other prior art/s to fairly show, disclose or suggest the claimed limitations as recited in independent claim 18, comprising limitations: receiving another input indicating that the sensor has been moved to a different location; generating updated sensor profile data associated with the different location based upon updated sensor data generated by the sensor proximate to the different location; inputting the updated sensor profile data to the machine learning model; receiving an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and storing the updated sensor model profile in the at least one memory as being associated with the different location, i.e. in the particular manner claimed is not taught or suggested in the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following cited arts are further to show the state of related art.
U.S. Publication No. 2025/0029488 A1 of ZAHID et al, discloses one or more of accessing, by an application providing assistance to a vehicle, sensor data associated with an environment inside and outside of a vehicle and profile data associated with a vehicle occupant, determining, by the application, an initial condition of the vehicle occupant based on the sensor data and the profile data, responsive to the initial condition being above a health condition threshold, accessing, by the application, health data associated with the vehicle occupant from a mobile device, determining, by the application, an updated condition of the vehicle occupant based on the health data, creating, by the application, an alert to notify the occupant based on the updated condition and one or more current driving conditions of the vehicle identified by the sensor data, and performing, by the vehicle, one or more vehicle actions based on the alert and the one or more current driving conditions.
U.S. Publication No. 2025/0027789 A1 of ZAHID et al, discloses a process that performs one or more of the following determining, by a vehicle, that an occupant assist application is operating in the vehicle to assist a vehicle occupant during vehicle operation, determining, by the vehicle, that an unsafe driving condition is likely to occur via a monitoring application, prior to a time that the unsafe driving condition is expected to occur, ceasing, by the vehicle, the occupant assist application, and executing, by the vehicle, a driving assist application to assist with the vehicle operation during the unsafe driving condition.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISAY YACOB whose telephone number is (571)272-8562. The examiner can normally be reached Monday - Friday 10:30-07:00 ET.
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/SISAY YACOB/ January 25, 2026 Primary Examiner, Art Unit 2686