Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is in response to the restriction requirement to the Application No. 18/971,266 filed on 8/20/26. Elected Claims 1- 14 (without traverse) has been examined.
Claim Rejections - 35 USC § 102
3. 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.
4. 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.
5. Claim(s) 1 – 3, 5 – 11, 13 – 14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Martin et al. (US 2020/0059776, Martin hereafter).
Regarding claim 1, Martin teaches A method, comprising:
determining data from one or more Internet of Things (loT) devices indicates an emergency event (In some embodiments, the electronic device is a wearable device (e.g., a smartwatch). In some embodiments, the electronic device is an Internet of Things (IoT) device, such as a home assistant (e.g., an Amazon Echo) or a connected smoke detector (e.g., a Nest Protect smoke and carbon monoxide alarm). In some embodiments, the electronic device is a walkie-talkie or two-way radio, paragraph 41, 50, 65, 68, 74, 99);
filtering the data from the one or more loT devices to create relevant data for the emergency event (please refer to Fig. 6 – 8,12; specifically, it can determine type of emergency; the relevancy determination module employs machine learning techniques when calculating a relevancy score for data or multimedia received from a sensor pertinent to an emergency. For example, in some embodiments, when the relevancy determination module calculates and assigns a relevancy score to data or multimedia received from a sensor pertinent to an emergency, the relevancy determination module stores information regarding the data or multimedia along with the relevancy score in a database (hereinafter “relevancy score database”). In some embodiments, the information regarding the data or multimedia stored in the relevancy score database can include sensor type, device type, timestamp, likely nature of emergency, audio or visual cues, or any other information regarding the data or multimedia. In some embodiments, as will be described below, after identifying a set of sensors pertinent to an emergency, receiving data or multimedia from the set of sensors pertinent to the emergency, and determining a set of relevant sensors from the set of sensors pertinent to the emergency, the IMS or EMS can transmit data or multimedia from the relevant sensors to an emergency service provider (ESP), paragraph 123, 148 – 152; In some embodiments, a machine learning algorithm is applied to classify multimedia content into a category such as relevance (e.g., relevant or irrelevant to an emergency). In some embodiments, the application applies at least one machine learning algorithm to multimedia content to determine an emergency type (e.g., injury or accident, medical problem, shooting, violent crime, robbery, tornado, or fire) and/or emergency level (e.g., safe, low, medium, high), paragraph 157); and
communicating the relevant data for the emergency event to a public safety answering point (PSAP) or a control center (the clearinghouse 170 automatically pushes the emergency data to a receiving party such as the PSAP, paragraph 51 – 52, 67, 76, 82, 84).
Regarding claim 2, Martin teaches The method of Claim 1, Martin further teaches wherein determining that the data from the one or more loT devices indicates the emergency event is performed by a computer model or machine learning (In some embodiments, the application applies at least one machine learning algorithm to multimedia content to determine an emergency type (e.g., injury or accident, medical problem, shooting, violent crime, robbery, tornado, or fire) and/or emergency level (e.g., safe, low, medium, high), paragraph 157, also paragraphs 123, 148 - 152).
Regarding claim 3, Martin teaches The method of Claim 1, Martin further teaches wherein the data is filtered using a computer model or machine learning (the application applies at least one machine learning algorithm to multimedia content to determine an emergency type (e.g., injury or accident, medical problem, shooting, violent crime, robbery, tornado, or fire) and/or emergency level (e.g., safe, low, medium, high), paragraph 157).
Regarding claim 5, Martin teaches The method of Claim 1, Martin further teaches wherein at least one of the one or more loT devices is a vehicle loT device and the relevant data for the emergency event is communicated to the public safety answering point (PSAP) (the clearinghouse 170 automatically pushes the emergency data to a receiving party such as the PSAP, paragraph 51 – 52, 67, 76, 82, 84).
Regarding claim 6, Martin teaches The method of Claim 1, Martin further teaches wherein at least one of the one or more loT devices is an industrial loT device and the relevant data for the emergency event is communicated to the control center (the clearinghouse 170 automatically pushes the emergency data to a receiving party such as the PSAP, paragraph 51 – 52, 67, 76, 82, 84; ESP, paragraph 51 - 52).
Regarding claim 7, The method of Claim 1, wherein each of the one or more loT devices are different types of loT devices (In some embodiments, various IoT devices like temperature sensors and motion sensors function via a hub or intermediary device, which may be controlled via a user's mobile phone. However, because these devices are all around users at all times, there may be certain devices that a user may not want to provide access to. For example, a person may not want to provide access a smart camera installed in their bedroom under any circumstances. Or, for example, they may not want to provide access to the smart camera installed in their bedroom unless the emergency is a fire, paragraph 135).
Regarding claim 8, the system substantially has same limitations as claim 1, thus the same rejection is applicable.
Regarding claim 9, the system substantially has same limitations as claim 2, thus the same rejection is applicable.
Regarding claim 10, the system substantially has same limitations as claim 3, thus the same rejection is applicable.
Regarding claim 13, The system of Claim 8, wherein at least one of the one or more loT devices is a video camera (camera, Fig. 5, paragraph 100, 102 - 110).
Regarding claim 14, the system substantially has same limitations as claim 7, thus the same rejection is applicable.
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. 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.
9. Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martin et al. (US 2020/0059776, Martin hereafter) in further view of Sheth et al. (US 2023/0188968, Sheth hereafter).
Regarding claim 4, Martin teaches claim 1, however does not specifically teach and/or suggest wherein data from a specific loT device does not indicate the emergency event, however, when the data from the specific loT device is combined with data from one or more other loT devices, the data from the specific loT device and the data from the one or more other loT devices indicates the emergency event.
In the same field of endeavor, Sheth teaches wherein data from a specific loT device does not indicate the emergency event, however, when the data from the specific loT device is combined with data from one or more other loT devices, the data from the specific loT device and the data from the one or more other loT devices indicates the emergency event (the emergency event data 150 can originate from multiple sources, including the victim device 102, the bystander device 116, the other data source(s) 136, the landline device(s) 138, and the IoT device(s) 140. The emergency network 110 can include an emergency event data aggregator 160 to collect the emergency event data 150 from these disparate sources, aggregate the emergency event data 150 for the emergency event 106, and provide the emergency event data 150 to the appropriate PSAP 114 for handling by the emergency personnel 112, paragraph 45).
It would have been obvious to one of the ordinary skilled in the art at the time of the filing to combine the teachings of Sheth’s generate the emergency event with the use of multiple IoT instead of only single IoT with the system of Martin. One would be motivated to combine these teachings because in doing so it can provide accurate data of the emergency event so the it can send PSAP to appropriate handling by the emergency personnel.
10. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martin et al. (US 2020/0059776, Martin hereafter) in further view of Sakata (US 2022/0157090).
Regarding claim 12, Martin teaches claim 8, however does not specifically teach wherein at least one of the one or more loT devices is a vehicle loT device.
Sakata teaches wherein at least one of the one or more loT devices is a vehicle loT device (A connected car is an automobile (vehicle) that uses IoT (Internet of Things) technology and functions as a terminal. The connected car obtains data about the status of its own vehicle and various data such as the surrounding road conditions from sensors. The connected cars are expected to generate new value through the accumulation and analysis of data. Specifically, systems that automatically make an emergency call in the event of an accident, and systems that track the location of a vehicle when the vehicle is stolen are being put to practical use, paragraph 2).
It would have been obvious to one of the ordinary skilled in the art at the time of the filing to combine the teachings of Sakata’s vehicle IoT with the system of Martin. One would be motivated to combine these teachings because in doing so it can provide accurate data of the emergency event in vehicle crash or driver emergency and can send to to the appropriacte agency for help.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TANMAY K SHAH whose telephone number is (571)270-3624. The examiner can normally be reached Mon - Fri - 8:00 - 5:00.
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/TANMAY K SHAH/Primary Examiner, Art Unit 2632
TANMAY K. SHAH
Primary Examiner
Art Unit 2632
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