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 .
This office action is responsive to amendment filed on 04/27/2026.
Response to Amendment
The Examiner has acknowledged the amended specification.
Response to Arguments
Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive.
Regarding Applicant’s argument (page 11) that Gilani is directed to solving bandwidth insufficiency and network reliability problems in loT networks - specifically, aggregating multiple communication paths to boost bandwidth, providing fault-tolerant connectivity, and enabling real-time data transmission (Gilani III [0002]-[0003], [0015]-[0016]). Arnott is directed to a fundamentally different problem: periodically calibrating building management system (BMS) sensors using a mobile device and remote server (Arnott I [0005]). Arnott's system is manual (personnel physically scan QR codes), periodic (not real-time), and server-based (relying on external databases).
The stated rationale does not explain, with any technical specificity, why a person of ordinary skill would modify Gilani's real-time, low-latency communication gateway to include Arnott's periodic, manual, server-based calibration system. The two references address unrelated technical problems and operate in incompatible technical contexts. The proposed combination therefore lacks the articulated reasoning with rational underpinning required under *KSR*. *In re ICON Health & Fitness, Inc. 496 F.3d 1374, 1381 (Fed. Cir. 2007).
The Examiner respectfully disagrees with Applicant’s assertion because Gilani discloses that in FIG. 5 is a diagram illustrating data thinning capabilities of an intelligent multi-modal IoT gateway 10a according to the teachings of the present disclosure. At any one location, such as a large facility or for security intensive application, many video cameras 70a and sensors 62a may be deployed to monitor the perimeter and interior of a banking institution 72a, for example. The many video cameras 70a and sensors 62a generate a large volume of data that are transmitted to the intelligent multi-modal IoT gateway 10a, which then relays the data via telecommunications and/or computer networks to a monitoring entity 76 (see paragraph [0026].
Thus, Gilani shows the association of sensors with MDTU by showing with sensors that may detect the presence of smoke or fire may send active signals to the intelligent multi-modal IoT gateway 10, which may then automatically contact the monitoring entity 76, fire department, and law enforcement.(see paragraph [0030]).
The examiner has combined Gilani and Arnott to show the idea of calibrating sensors attached to devices in a building because Gilani is concerned with monitoring sensors to detect smoke or fire in an environment or place.
Arnott discloses that The calibration sensor 174 may be a stand-alone device in communication with the mobile computer device 172 or may be integral with the mobile computer device 172. In this example, the calibration sensor 174 is preferably a stand-alone device configured for communication with the mobile computer device 172 and the mobile computer device 172 is preferably a web-enabled smart-phone, tablet or similar mobile computing device. The calibration sensor 174 may communicate with the mobile computer device 172 via an input/output port 177 (I/O) of the mobile computer device 172 or via Bluetooth™ or other wireless I/O means.(see paragraph [0053]).
Thus, the Examiner contends that the combination of Gilani and Arnott is proper because Gilani discloses all the limitations, except for using a calibration system to calibrate the sensors. However, it is safe to have all sensors that are being monitored to be calibrated very often in order to output accurate data for safety.
Applicant argued that he Final Action cites Gilani I [0021] for splitting data streams (Final Action, p. 5). However, Gilani I [0021] discloses splitting data packets from a sensor - in the singular. The phrase "a sensor" appears repeatedly throughout Gilani's specification. Gilani does not disclose splitting a data stream from a plurality of sensors (multiple sensors, each potentially providing different data streams that are then split across communication paths). The Final Action does not address this distinction or explain why extending from a single sensor to a plurality of sensors would have been obvious.
The Examiner respectfully disagrees with Applicant’s argument because Gilani discloses that the intelligent multi-modal IoT gateway 10 has connectivity to a variety of networks, connections, and resources, including, for example, cellular network 12, SATA 14, LoRA 16, Zigbee 18, Bluetooth 20, WiFi 22, WiFi Direct 23, NFC 24, 433 MHz 25, PCI-Express 26, SD Card 28, USB 30, and wired and wireless sensors 32 and 34. the intelligent multi-modal IoT gateway 10 achieves robust and boosted connectivity using either built-in modules or pluggable hardware modules, or both, to support heterogeneous technologies (see paragraphs [0015], [0023]).
Gilani further discloses that it may define and combine multiple wired and wireless data pipes to create the bonded VPN communication, and data is split into multiple data packets and sent over these multiple data pipes simultaneously)(paragraph [0021]).
Applicant argued that the Examiner has failed to identify any disclosure in the cited references for: (1) modifying sampling interval; (2) modifying sampling accuracy; (3) modifying frequency for sending data from the sensor.
The Examiner respectfully disagrees with Applicant’s argument because the claim recites “one or more “ and Gilani discloses that a second intelligent multi-modal IoT gateway 10b, temporarily lacking a connection to the Internet, may send the IoT gateway 10a data it receives from sensors and devices 70b in monitoring an ATM machine 72b, for example. Instead of retaining and sending all of the data to the monitoring entity 76, the intelligent multi-modal IoT gateways 10a and 10b employ logic and artificial intelligence to prioritize the data streams according to the type of data and the content of the data, and create a reduced data stream with only the selected or highest priority data for transmission (claimed frequency of data stream). (see paragraphs [0026], [0033]).
Thus, the Examiner contends that the prior art read on the claimed invention.
It appears that applicants are interpreting the claims very narrow without considering the broad teaching of the references used in the rejection. Applicants are reminded that the examiner is entitled to the broadest reasonable interpretation of the claims. The Applicants always have the opportunity to amend the claims during prosecution and broad interpretation by the examiner reduces the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Prater 162 USPQ 541,550-51 (CCPA 1969).
In view of such, the rejections are as follows:
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 55 – 59, 61 – 70, and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Gilani et al (US 2018/0198641; hereinafter Gilani) in view of Arnott et al (US 2018/0010935; hereinafter Arnott).
Regarding claim 55, Gilani discloses an IOT network node system, comprising:
a plurality of IOT network nodes (paragraphs [0021 - 0024), each node including at least one CPU, memory, firmware, an operating system and I/O for communicating with other nodes (paragraph [0018]);
wherein a first Multimode Data Transmission Unit (MDTU) is one of the plurality of IOT network nodes (paragraphs [0015, [0019], [0021], [0023 – 0024, [0026]);
a plurality of sensors, wherein the plurality of sensors are configured to provide sensor data to the first MDTU (paragraphs [0015, [0019], [0021], [0023 – 0024, [0026]);
wherein the first MDTU is configured to transmit the sensor data from the plurality of sensors to at least one other node of the IOT network node system (paragraphs [0015, [0019], [0021], [0023 – 0024, [0026]);
wherein the first MDTU is further configured to split a data stream from the plurality of sensors into different data streams and transmit the different data streams along different communication paths (paragraph [0021]; Gilani discloses that It may define and combine multiple wired and wireless data pipes to create the bonded VPN communication, and data is split into multiple data packets and sent over these multiple data pipes simultaneously) in conjunction with one or more of:
application requirements; distance between IOT network nodes; power consumption; quality of service for a communication protocol or application; data flow rate; a bandwidth; a payload form; delay of the data path; failure information of the IOT network node; a connection failure or disconnection of data connections between IOT network nodes; decision processing related with improving spectral efficiency, resource utilization, and/or real-time capability (paragraphs [0014 - 0016]; Gilani discloses that there are multiple paths of data bandwidth available at many locations. However, conventional devices and applications can only actively utilize one data bandwidth at any point in time. There are existing solutions that offer primary and backup connectivity in the event of failure of the primary network);
wherein the first MDTU is further configured to modify one or more of the following: a sampling interval; sampling accuracy; a frequency for sending data from the sensor (paragraphs [0026], [0033]; Gilani discloses that a second intelligent multi-modal IoT gateway 10b, temporarily lacking a connection to the Internet, may send the IoT gateway 10a data it receives from sensors and devices 70b in monitoring an ATM machine 72b, for example. Instead of retaining and sending all of the data to the monitoring entity 76, the intelligent multi-modal IoT gateways 10a and 10b employ logic and artificial intelligence to prioritize the data streams according to the type of data and the content of the data, and create a reduced data stream with only the selected or highest priority data for transmission. For example, the intelligent multi-modal IoT gateway 10a may analyze the video data from a video camera to determine if the image content has changed from time T1 to time T2. The intelligent multi-modal IoT gateway 10 may employ a rule engine that includes a rule, if <Image Content of Video Camera 1 at T2>=<Image Content of Video Camera 1 at T1>, then assign low importance to video data from T1 to T2 from Video Camera 1).
Gilani discloses all the limitations, but fails to specifically disclose a sensor calibration system;
Wherein the sensor calibration system operatively associated with the first MTDU, the sensor calibration system comprising:
a processing circuitry configured to receive sensor data detected by a sensor and reference values under comparable environmental conditions, and the processing circuitry is further configured to perform data processing on the sensor data and the reference values to generate calibration information; and
a calibration unit configured to apply the calibration information to the sensor data detected by the sensor to perform calibration on the sensor data.
Arnott, in an analogous art, discloses the idea of having a sensor calibration system (abstract; paragraphs [0005], [0010]; Arnott discloses a method for calibration a sensor of a building using a computer system);
Wherein the sensor calibration system operatively associated with the first MTDU (paragraphs [0005], [0010]; Arnott discloses the process of calibrating an array of building sensors), the sensor calibration system comprising:
a processing circuitry configured to receive sensor data detected by a sensor and reference values under comparable environmental conditions (paragraphs [0010 - 0011], [0019 – 0020]; Arnott discloses receiving, at the computer system, sensor data associated with the identification data of the sensor, the sensor data representing a sensor measured physical phenomenon provided by the sensor; d) calculating, in the computer system, a difference value between the measured data and the sensor data so as to provide calibration data), and the processing circuitry is further configured to perform data processing on the sensor data and the reference values to generate calibration information (paragraphs [0012], [0018 - 0020]; Arnott discloses a device authority associated with device authorization data by comparing the device authorization data with pre-determined device authorization data, the device authority enabling calibration of the sensor data.); and
a calibration unit configured to apply the calibration information to the sensor data detected by the sensor to perform calibration on the sensor data (paragraphs [0018 - 0020], [0055]; Arnott discloses that the sensor only has identification info in this regard; and b) apply the calibration information as it uses the input data from the calibration sensor device 174. There may be several grades of calibration sensors, some with temperature only, some with multiple sensors and would be modular for expansion or change-out of defective/damaged components. The configuration may include a range of different grades and types of components that could be attached to the base-sensing device, depending on user preferences.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Gilani by providing a sensor calibration system; wherein the sensor calibration system operatively associated with the first MTDU, the sensor calibration system comprising: a processing circuitry configured to receive sensor data detected by a sensor and reference values under comparable environmental conditions, and the processing circuitry is further configured to perform data processing on the sensor data and the reference values to generate calibration information; and a calibration unit configured to apply the calibration information to the sensor data detected by the sensor to perform calibration on the sensor data as evidenced by Arnott for the purpose of ensuring the correct calibration data is applied to building management system and the correct time and date of calibration is recorded for future reference.
Regarding claim 56, Gilani discloses the system of claim 55, wherein the sensor data is encrypted with one or more of: geographic location information of a gateway through which the sensor data passes; a location of a route point; intermediate node information; latitude and longitude of a coordinate data; communication serial number; transmission speed; and/or communication protocol information associated with transmission of the sensor data (paragraphs [0016], [0022]; Gilani discloses that depending on the nature of the application, the intelligent multi-modal IoT gateway may be configured to transmit non-critical data using best effort via only one connection, or in a delay-tolerant fashion, but with guaranteed delivery. A certain level of data security can be achieved, in addition to encryption because data are transmitted over multiple pathways, and listening on any one pathway would not yield all of the data packets).
Regarding claim 57, Gilani discloses the system of claim 55, wherein the sensor data is encrypted with time information associated with transmission of the sensor data (paragraphs [0016], [0022], [0026], [0033]).
Regarding claim 58, Gilani discloses the system of claim 55, wherein a decryption key for the sensor data is associated with network transmission path (paragraphs [0014], [0021 - 0022]); and wherein a receiving IOT node uses location information of a previous IOT node for decryption of received sensor data in sequence (paragraph [0021]; Gilani discloses that the cloud IoT gateway software component then assembles or oversees the assembly of the data packets and stores the reconstituted data into the cloud data storage device using the sequence numbers), thereby implementing layer by layer encryption and layer by layer decryption (paragraphs [0016], [0021 – 0022]; Gilani discloses multi-channel encrypted data delivery).
Regarding claim 59, Gilani discloses the system of claim 55, wherein the sensor data sampling is optimized based on network condition for transmission of the sensor data (paragraphs [0021 – 0022], [0029]; Gilani discloses that the most critical data is sent in duplicate through multiple data paths if they are available. Depending on the nature of the application, the intelligent multi-modal IoT gateway may be configured to transmit non-critical data using best effort via only one connection, or in a delay-tolerant fashion, but with guaranteed delivery).
Regarding claim 70, Gilani discloses the system of claim 65, wherein the sensor data sampling and the sensor data transmission linkage are both optimized based on network condition for transmission of the sensor data (paragraphs [0019], [0021 – 0022], [0029], [0026], [0033]; Gilani discloses that the intelligent multi-modal IoT gateway 10 has the capability to aggregate available bandwidth resources to boost the speed to transmit data from, e.g., video cameras and sensors, to the monitoring entity 76.).
Regarding claim 72, Gilani discloses the system of claim 65, wherein the first MDTU is configured to prioritize transmission of the sensor data based on network resources (paragraphs [0026], [0028 – 0029], [0032]; Gilani discloses that instead of retaining and sending all of the data to the monitoring entity 76, the intelligent multi-modal IoT gateways 10a and 10b employ logic and artificial intelligence to prioritize the data streams according to the type of data and the content of the data, and create a reduced data stream with only the selected or highest priority data for transmission.).
Claims 60 and 71 are rejected under 35 U.S.C. 103 as being unpatentable over Gilani et al (US 2018/0198641; hereinafter Gilani) in view of Arnott et al (US 2018/0010935; hereinafter Arnott), and further in view of Coutinho et al (US 2017/0150299; hereinafter Coutinho).
Regarding claim 60, Gilani and Arnott disclose all the limitations in claim 55, but fail to specifically disclose that the first MDTU is configured to change encoding of the sensor data and transmit the sensor data in a newly encoded format.
Coutinho, in an analogous art, discloses the idea of changing encoding of the sensor data and transmit the sensor data in a newly encoded format (paragraph [0166]; Coutinho discloses that the network may, for example, use a feedback-based approach in which a receiving network node notifies the sending network node when decoding is completed on information that was previously sent, signaling that new encoded data can then be sent).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Gilani and Arnott by changing encoding of the sensor data and transmit the sensor data in a newly encoded format as evidenced by Coutinho for the purpose of highly increasing security in the transmission of data in a network environment; thereby improving the overall network throughput.
Claims 73 – 74 are rejected under 35 U.S.C. 103 as being unpatentable over Gilani et al (US 2018/0198641; hereinafter Gilani) in view of Arnott et al (US 2018/0010935; hereinafter Arnott), and further in view Cella et al (US 2018/0284737; hereinafter Cella).
Regarding claim 73, Gilani discloses all the limitations in claim 65, but fail to specifically disclose that the system is configured to provide virtual reality based on the sensor data.
Cella, in an analogous art, discloses that the system is configured to provide virtual reality based on the sensor data (paragraphs [0368 – 0369]; Cella discloses that a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Gilani and Arnott by showing that the system is configured to provide virtual reality based on the sensor data as evidenced by Cella for the purpose of ensuring the component stands out in a virtual reality environment being used to help a user monitor devices in an efficient and reliable manner.
Regarding claim 74, Gilani, Arnott, and Cella disclose the system of claim 74, wherein the system is configured to determine a temporal evolution of the virtual reality based on sensor data (Cella: paragraph [1251]; Cella discloses that the visual attributes may provide near real-time portrayal of trends of the sensed data and/or of derivatives thereof. In embodiments, the visual attributes may be the actual data being captured, or the derived data, such as a trend of the data and the like). Same motivation as in claim 73.
Claims 60 – 69, and 71 incorporate substantially all the limitations of claims 55 – 59 with minor modifications in the claimed language. The reasons for rejecting claims 55 – 59 apply in claims 60 – 69, and 71. Therefore, claims 60 – 69, and 71 are rejected for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YVES DALENCOURT whose telephone number is (571)272-3998. The examiner can normally be reached M-F 8AM-5:30PM.
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/YVES DALENCOURT/Primary Examiner, Art Unit 2457
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