DETAILED ACTION
Response to Amendment
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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.
Claims 1-5, 9, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kale et al. (U.S. Patent Application Publication Number 2019/0260751) and Sohail et al. (U.S. Patent Application Publication Number 2020/0403991).
Regarding Claims 1, 14, and 20, Kale discloses a computer-implemented method (as well as a computing apparatus comprising a processor and a memory storing instructions that when executed by the processor configure the apparatus [paragraph 0048], and a non-statutory computer-readable storage medium including instructions for a computer [paragraph 0049]) of detecting deviations from an expected power profile of an Internet-of-things (IoT) device comprising:
retrieving (Figure 3, item 40), over a network (Figure 1, items 20/24), a manufacturer usage description (MUD) associated with the IoT device (Figure 1, item 18, paragraph 0016; i.e., a MUD profile is obtained by the MUD controller 14 from the MUD server 12), wherein the MUD comprises a profile associated with the device (paragraph 0026);
wherein the IoT device is connected to the network and operating over the network (paragraph 0014);
generating one or more policies based on the MUD, wherein the one or more policies permit or deny the IoT device access to the network (Figure 3, item 46, paragraph 0029; i.e., the posture assessment engine 16 [Figure 1] receives the obtained MUD profile and subsequently generates a network access policy for the IoT device 18 which permits or denies access to the network 20/24 based on whether the IoT device 18 meets certain security requirements);
transmitting, over the network, the network access policy to a network access point (Figure 1, items 14/22, paragraph 0014) to which the IoT device is connected (paragraph 0029).
Kale does not expressly disclose wherein the MUD comprises a power profile associated with the device, and an expected power consumption parameter can be determined from the power profile;
monitoring an actual power consumption parameter of the IoT device while the IoT device is operating over the network;
comparing the expected power consumption parameter to the actual power consumption parameter of the IoT device while the IoT device is operating over the network;
determining a deviation between the actual power consumption parameter and the expected power consumption parameter indicated in the power profile; and
outputting a notification when the deviation is equal to or greater than a threshold value in the one or more policies.
In the same field of endeavor (e.g., monitoring IoT devices operating over a network), Sohail teaches wherein the MUD comprises a power profile associated with the device (paragraph 0046; i.e., vendor/manufacturer supplied power profile [equivalent to the claimed “manufacturer usage description”] includes “information regarding normal ranges of power usage” [equivalent to the claimed “power profile”] can be provided to determine the normal/baseline power consumption of the IoT device), and an expected power consumption parameter can be determined from the power profile (Figure 3, items 300 and 302, paragraphs 0045-0046 and 0058);
monitoring an actual power consumption parameter of the IoT device while the IoT device is operating over the network (Figure 3, item 304, paragraphs 0044 and 0059);
comparing the expected power consumption parameter to the actual power consumption parameter of the IoT device while the IoT device is operating over the network (Figure 3, item 304, paragraphs 0045 and 0060);
determining a deviation between the actual power consumption parameter and the expected power consumption parameter indicated in the power profile (Figure 3, item 304, paragraphs 0031 and 0060); and
outputting a notification when the deviation is equal to or greater than a threshold value in the one or more policies (Figure 3, item 308, paragraph 0060; i.e., the power consumption behavior analysis engine 221 generates a “policy” based on the vendor-created power profiles [i.e., the claimed “manufacturer usage description”] located within power profiles database 228; this “policy” is used to determine whether a particular IoT device 120 is operating outside of its designed power consumption [i.e., there is a deviation equal to or greater than a threshold value]; if a particular IoT device 120 is operating outside of its designed power consumption, an alert/notification is output to a system administrator node).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sohail’s teachings of monitoring IoT devices operating over a network with the teachings of Kale, for the purpose of ensuring that the IoT device remains within the specified power requirements, thereby preventing excessive power consumption in the system.
Regarding Claims 2 and 15, Sohail teaches generating correlation information associated with a type of IoT device (paragraph 0047; i.e., generating historical power consumption data of the same type of IoT device).
Regarding Claims 3 and 16, Sohail teaches correlating the actual power consumption parameter associated with the IoT device with a second actual power consumption parameter associated with a second device, wherein the IoT device and the second device are the type of IoT device (paragraph 0047; i.e., a database is created to store and track historical power consumption of the same types of IoT devices).
Regarding Claims 4 and 17, Sohail teaches reporting the correlation information and associated statistical behavior to a manufacturer of the type of IoT device (paragraph 0047; i.e., the vendor database is updated based on the historical power consumption identified, which would include reporting that information to the vendor).
Regarding Claims 5 and 18, Sohail teaches remediating the deviation in device operation relative to the power profile (Figure 3, item 314, paragraph 0061; i.e., the “remediation” could be to block the IoT device from continuing to operate over the network).
Regarding Claim 9, Sohail teaches wherein the manufacturer usage description (MUD) associated with the IoT device includes an emissions profile (paragraph 0046; i.e., the IoT device may be a wireless device; any power profile would therefore impact the RF energy emitted by that wireless device; therefore, it would include RF emissions since power consumption is directly proportional to the amount of RF radiation).
Regarding Claim 11, Sohail teaches monitoring a communication pattern of the IoT device; and determining a deviation between the communication pattern of the IoT device over a duration of time and an expected communication pattern of the IoT device indicated in the emissions profile (paragraph 0046; i.e., the variations in actual versus expected power consumption affect how much RF emissions are transmitted from the wireless device, which is equivalent to the claimed “communication pattern”).
Regarding Claims 13 and 19, Sohail teaches receiving a MUD associated with a device on a computer network, wherein the MUD is provided by a manufacturer of the IoT device (paragraph 0046; i.e., the power profiles 228 [Figure 2] are located within the servers 160, which are connected to the IoT devices 120 via network 130 [Figure 1]).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Kale and Sohail as applied to Claim 5, and further in view of Hauser et al. (U.S. Patent Application Publication Number 2019/0354681).
Regarding Claim 6, Kale and Sohail do not expressly disclose wherein the remediating includes power cycling the IoT device.
In the same field of endeavor (e.g., detection of non-compliant components), Hauser teaches wherein the remediating includes power cycling the IoT device (paragraphs 0080-0081).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Hauser’s teachings of detection of non-compliant components with the teachings of Kale and Sohail, for the purpose of allowing for recovery for errors that caused an energy drain. This may cause malware stored in RAM to be deleted/removed, resulting in clean power profiles. See Hauser, paragraph 0080.
Regarding Claim 7, Hauser teaches wherein the remediating includes eliminating a power supply associated with the IoT device, thereby powering off the IoT device, when the deviation is equal to or greater than the threshold value (paragraphs 0083-0084 and 0088).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kale and Sohail as applied to Claim 1, and further in view of Liebenow (U.S. Patent Application Publication Number 2006/0143483).
Regarding Claim 8, Kale and Sohail do not expressly disclose wherein the monitoring the actual power consumption parameter includes monitoring a battery life of the IoT device; and wherein the determining a deviation between the actual power consumption parameter and the expected power consumption parameter indicated in the power profile includes determining a deviation between the battery life of the IoT device over a duration of time and an expected battery life of the IoT device indicated in the power profile.
In the same field of endeavor (e.g., detection of non-compliant components), Liebenow teaches wherein the monitoring the actual power consumption parameter includes monitoring a battery (Figure 1A, item 110) life of the IoT device (Figure 1A, item 100); and wherein the determining a deviation between the actual power consumption parameter and the expected power consumption parameter (paragraph 0022; i.e., an expected power consumption is calculated) indicated in the power profile includes determining a deviation between the battery life of the IoT device over a duration of time and an expected battery life of the IoT device indicated in the power profile (paragraphs 0010 and 0024; i.e., the system can determine that the battery 110 is draining faster than expected [the claimed “deviation”] and can adjust power consumption accordingly).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Liebenow’s teachings of detection of non-compliant components with the teachings of Kale and Sohail, for the purpose of ensuring that the life of the battery is maximized. More specifically, by detecting that the battery life is draining faster than expected, power management can be adjusted so that it does not drain so quickly.
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kale and Sohail as applied to Claim 9, and further in view of Nanni et al. (U.S. Patent Number 5,487,183).
Regarding Claim 10, Kale and Sohail do not expressly disclose monitoring a frequency output of the IoT device; and determining a deviation between the frequency output of the IoT device over a duration of time and an expected frequency output of the IoT device indicated in the emissions profile.
In the same field of endeavor (e.g., detection of non-compliant components), Nanni teaches monitoring a frequency output of the IoT device; and determining a deviation between the frequency output of the IoT device over a duration of time and an expected frequency output of the IoT device indicated in the emissions profile (Column 4, lines 30-47; i.e., an actual frequency output is compared against an expected frequency output).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Nanni’s teachings of detection of non-compliant components with the teachings of Kale and Spitz, for the purpose of ensuring that the system is not outputting data too quickly. By doing so, it can detect whether an output component is defective.
Regarding Claim 12, Kale and Sohail discloses monitoring a transmit power of the IoT device (Sohail, paragraph 0045).
Kale and Spitz do not expressly disclose determining a deviation between the transmit power of the IoT device and an expected frequency output of the IoT device indicated in the emissions profile.
In the same field of endeavor, Nanni teaches determining a deviation between the transmit power of the IoT device (Column 2, lines 61-63; i.e., the power output of a voltage controlled oscillator) and an expected frequency output of the IoT device indicated in the emissions profile (Column 3, lines 13-28; i.e., the power supplied to the VCO corresponds to the output frequency; therefore, the comparison of expected frequency output and actual frequency output [Column 4, lines 30-47] is based on a deviation of the transmit power of the VCO).
The motivation discussed above with regards to Claim 10 applies equally as well to Claim 12.
Response to Arguments
Applicant's arguments filed 2/6/26 have been fully considered but they are not persuasive.
Regarding Claim 1, Applicant argues “[u]nder the plain meaning of ‘comprises,’ the power profile must be part of the MUD itself, not merely associated with the device elsewhere in the system. Neither Kale nor Sohail discloses or suggests embedding power profiles within a MUD.” Response, page 9. The examiner disagrees. Initially, it is noted that the claim requires the MUD comprises “a power profile”, rather than “power profiles” (plural) as Applicant has argued. Further, Sohail teaches a vendor (i.e., manufacturer)-created power profile that contains “information regarding normal ranges of power usage”. The vendor-created power profile in Sohail has been equated to the claimed “manufacturer usage description”, while the information regarding the power ranges within the vendor-created power profile is equivalent to the claimed “power profile”. Accordingly, it can be seen that Sohail does in fact teach the argued feature.
Regarding Claim 1, Applicant argues “[i]n Sohail, expected power behavior is inferred from monitored data, not determined from a static, manufacturer-defined power profile retrieved over a network.” Response, page 10. The examiner disagrees. Sohail states “the power consumption behavior analysis engine 221 utilizes information in the database of power profiles 228 and/or the database of learned behavioral patterns 229 to analyze the streamed power consumption/usage data of the IoT devices 120 and detect for anomalies associated with, e.g., abnormal power consumption or other abnormal behaviors.” Sohail, paragraph 0045. Sohail further states “[t]he vendor-created power profiles provide an initial baseline of power usage information which can be compared against the actual power consumption (e.g., average power over a period of time) of a given IoT device 120 within the device network 110 to determine if the given IoT device 120 is consuming a normal or abnormal amount of power for a given application and/or configuration.” Id. at paragraph 0046. Therefore, the equated “power profile” (i.e., the “information regarding normal ranges of power usage” located within the vendor-created power profile) can be a static, manufacturer-defined power profile rather than power behavior inferred from monitored data as Applicant has argued. Accordingly, it can be seen that Sohail does in fact teach that the expected power consumption parameter is determined from the power profile that is comprised within the manufacturer usage description.
Regarding Claim 1, Applicant argues “Sohail's thresholds and quantitative decision criteria reside within monitoring, analytics, and tiering logic, not within network access policies generated from a MUD.” Response, page 12. The examiner disagrees. Sohail states “the power consumption behavior analysis engine 221 utilizes information in the database of power profiles 228 and/or the database of learned behavioral patterns 229 to analyze the streamed power consumption/usage data of the IoT devices 120 and detect for anomalies associated with, e.g., abnormal power consumption or other abnormal behaviors.” Sohail, paragraph 0045. “In one embodiment, this process can be implemented by the power consumption behavior analysis engine 221 processing the collected power consumption data against learned behavioral patterns of power consumption of the sensor nodes, which are stored in the learned behavioral patterns database 229, or otherwise using baseline or updated power profiles of the sensor nodes, which are stored in the power profiles database 228.” Id. at paragraph 0059. “A determination is made as to whether any of the sensor nodes currently operating within the sensor network are detected as exhibiting abnormal power consumptions”. Id. at paragraph 0060. As can be seen, the power consumption behavior analysis engine 221 generates a “policy” based on the vendor-created power profiles (i.e., the claimed “manufacturer usage description”) located within power profiles database 228. This “policy” is used to determine whether a particular IoT device 120 (Figure 1) is operating outside of its designed power consumption (i.e., there is a “deviation equal to or greater than a threshold value”). If a particular IoT device 120 is operating outside of its designed power consumption, an alert/notification is output to a system administrator node. See id. at Figure 3, item 308. Accordingly, it can be seen that Sohail does in fact teach the argued feature.
Regarding Claim 1, Applicant argues “[t]he Office Action's proposed combination of Kale and Sohail is improper because it would change the principle of operation of Kale's system.” Response, page 12. Applicant further argues “Sohail's system is reactive and inferential, not declarative. Power profiles and thresholds in Sohail evolve based on observed behavior and analytics, rather than being defined in advance by a manufacturer usage description.” Id. at page 13. The examiner disagrees. As stated above, the power profiles in Sohail (equivalent to the claimed “manufacturer usage description”) contains static information about the expected power consumption of each of the IoT devices 120 (Figure 1), rather than “reactive” and “inferential” data as Applicant has argued. See Sohail, paragraphs 0045-0046. Accordingly, it can be seen that the combination of Kale with Sohail would not change the principle of operation of Kale’s system.
Regarding Claim 1, Applicant argues “Kale and Sohail address different problems at different architectural layers: Kale addresses network security enforcement, using a manufacturer usage description (MUD) to declare permissible network behavior and generate access control policies. Sohail addresses device behavior analysis, using power consumption monitoring and analytics to detect anomalies and assess trustworthiness.” Response, page 14. The examiner disagrees. Contrary to Applicant’s argument, Sohail is very much concerned with declaring permissible network security enforcement. For example, the title of Sohail is “Security for Network Environment Using Trust Scoring Based on Power Consumption of Devices within Network”. Further, Sohail states “[t]he power consumption analysis and anomaly detection methods as discussed herein can be readily embodied as an add-on to existing network security solutions (e.g., anomaly detection, intrusion detection, etc.) to provide power consumption/usage information as an additional metric that is used in conjunction with other commonly used metrics (network activity, communication patterns between different IoT devices, behaviors of IoT devices, etc.) to detect for vulnerabilities, security breaches, anomalous device behaviors, device malfunctions, etc., within a network of devices.” Sohail, paragraph 0017. Therefore, contrary to Applicant’s argument, both Kale and Sohail address similar problems and similar architectural layers. Further, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Therefore, the claims stand as previously rejected.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAISAL M ZAMAN, ESQ. whose telephone number is (571)272-6495. The examiner can normally be reached Monday - Friday, 8 am - 5 pm, alternate Fridays.
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/FAISAL M ZAMAN/Primary Examiner, Art Unit 2175