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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/20/2026 has been entered.
Response to Arguments
3. Applicant’s arguments filed on 03/20/2026, with respect to the 35 U.S.C rejection(s) of claims 2-21 are rejected under as being unpatentable over U.S. Publication No. 20160261465 hereinafter Gupta in view of U.S. Publication No. 20190163973 hereinafter Keohane, and further in view of U.S. Publication No. 20160205123 hereinafter Almurayh have been fully considered. However, upon further consideration, a new ground(s) of rejection is made in view of amended claims.
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.
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.
4. Claims 2-21 are rejected under as being unpatentable over U.S. Publication No
20160261465 hereinafter Gupta in view of U.S. Publication No. 20190163973 hereinafter Keohane, and further in view of U.S. Publication No. 20160205123 hereinafter Almurayh, and further in view of U.S. Publication No. 20170301208 hereinafter Mytelka.
As per claim 2, Gupta discloses:
A method for authorizing operations in a network (para 0009 "For
example, according to various aspects, a method for monitoring Internet of
Things (IoT) device health may comprise modeling normal behavior associated
with an loT device in a local loT network, analyzing behavioral information
observed at the loT device, and comparing the analyzed behavioral information
to the modeled normal behavior associated with the loT device to determine
whether the behavioral information observed at the loT device indicates normal
behavior or anomalous behavior."), the method comprising:
storing, by a computer system communicatively coupled to a
communication network, data associated with actions of a device in the
communication network (para 0099 "In various embodiments, in order to enable
the on-device behavioral analysis, the loT device 900 may comprise an on-
device health monitoring platform 910 that includes at least an observation
module 920, a behavior vector extraction module 930, and an analysis module
940. Accordingly, in various embodiments, the observation module 920 may be
configured to monitor or otherwise collect local behavioral information on the loT
device 900 through one or more application program interface (API) calls and
minimal instrumentation at one or multiple levels in a mobile stack. The
observation module 920 may therefore utilize fast and efficient in-memory
processing to monitor, measure, or otherwise observe behavioral information
associated with the loT device 900 (e.g., heartbeats, sensor measurements,
power consumption, test results, etc.) and generate one or more action logs 922
that comprise one or more "features" describing the observed behavioral terms in
concise terms." Para 0100 "In various embodiments, the observation module 920
may then pass the one or more action logs 922 that include the features
describing the observed behavioral information to the behavior vector extraction
module 930, which may then map the features contained in the action logs 922
into an n-dimensional space in order to extract one or more behavior vectors 932
that represent the observed behaviors on the loT device 900."):
generating a data model based on a regular pattern of actions of the
device based on the data (para 0102 "Accordingly, the analysis module 940 does
not necessarily detect the behavioral anomalies 944 based on any one feature in
the behavior vectors 932. Instead, the analysis module 944 may use machine
learning to detect the behavioral anomalies 944 through evaluating the features
in the behavior vectors 932 in combination. Furthermore, in various
embodiments, the analysis module 940 may build a model representing normal
behavior associated with the loT device 900 over time, wherein the model
representing the normal behavior associated with the loT device 900 may be built
over time based on the model obtained from the manufacturer associated with
the loT device 900, the behavior vectors 932 generated in the on-device health
monitoring platform 910, behavior vectors and/or models corresponding to other
devices in the local loT network, behavior vectors and/or models corresponding
to inputs and interactions from one or more users associated with the local loT
network, the overall state model associated with the local loT network, and/or
any other suitable information that may have relevance to assessing normal
behavior versus anomalous behavior on the loT device 900.");
wherein the regular pattern of actions of the device comprises one or more
operations of the device (para 0107 and TABLE 2 (under paragraph 0007) "In
various embodiments, in the context associated with the on-device health
monitoring platform 910 shown in FIG. 9, the observer nodes 1012-1036 may
generally perform similar functionality to the observer module 920, whereby the
observer nodes 1012-1036 may monitor or otherwise collect local behavioral
information and generate one or more action logs that comprise one or more
features describing the observed behavior. Furthermore, the aggregator
nodes 1050-1052 and the analyzer nodes 1070-1072 may similarly observe local
behavior and generate one or more action logs that comprise features describing
the observed behavior. For example, exemplary features that may be contained
in the action logs generated at a toaster, a smoke detector, a refrigerator, and a
television are shown in the following table 7.")
comparing the requested operation with the regular pattern of actions of
the device to determine whether the requested operation matches the regular
pattern of actions of the device (para 0009 "Furthermore, in various
embodiments, the local loT network that includes the loT device may be modeled
and the analyzed behavioral information may be compared to the modeled local
loT network to determine a current state associated with the local loT network.
For example, modeling the local loT network may comprise aggregating
attributes associated with each loT device in the local loT network, constructing a
topology associated with the local loT network, obtaining
behavioral models associated with each loT device in the local loT network from
a manufacturer associated with each loT device or one or more repositories
configured to store the behavioral models, and combining the aggregated
attributes associated with each loT device in the local loT network, the topology
associated with the local loT network, and the behavioral models associated with
each loT device in the local loT network to model the local loT network. As such,
the behavioral information observed at the loT device may be reported to a
customer service entity in response to determining that the behavioral
information observed at the loT device indicates anomalous behavior, wherein
the anomalous behavior may comprise a potential malicious attack against the
loT device or the local loT network that includes the loT device, a potential
malfunction or abnormal operating condition at the loT device, or another issue
that may require attention and/or remediation." para 0115 In various
embodiments, at block 1250, the comparison between the behavior vectors and
the normal device model and/or the normal environmental model may be
evaluated to determine whether one or more anomalies were detected.")
and in response to the requested operation not matching with the regular
pattern of actions of the device, sending a query to a user interface (para 0060
FIG. 2A illustrates a high-level example of an loT device 200A in accordance with
various aspects. While external appearances and/or internal components can
differ significantly among loT devices, most loT devices will have some sort of
user interface, which may comprise a display and a means for user input. loT
devices without a user interface can be communicated with remotely over a wired
or wireless network, such as air interface 108 in FIGS. 1A-1B." para 0068
"Referring to FIG. 3, the communication device 300 includes logic configured to
receive and/or transmit information 305." Para 0133 "According to various
aspects, FIG. 18 illustrates an exemplary communications device 1800 that may
be configured to observe, aggregate, and/or analyze loT device behavior through
communication over a proximity-based distributed bus using discoverable D2D
services in accordance with the various aspects and embodiments disclosed
herein." Para 0137 11 Additionally, in various embodiments, the communications
device 1800 may include a user interface 1840." para 0103 "For example, in one
embodiment, the analysis module 940 may invoke a request/response system to
enable back and forth messaging between the loT device 900 and customer
service such that more information can be gathered (e.g., the analysis
module 940 may act as a router between the loT device 900 and customer
service). In another example, the analysis module 940 may notify another
aggregator and/or analyzer node in the local loT network to request assistance
with remediating the behavioral anomalies 944 (e.g., in the event that the
behavioral anomalies 944 are severe such that the loT device 900 cannot
conduct the remediation locally) and/or to assist with building the overall state
associated with the local loT network." Also see 0115 and 0123)
Gupta does not disclose:
receiving, by a computer system from the device, a request to perform a
requested operation
wherein the query asks about a prior pattern of actions of the device
receiving a response to the query; and determining whether to allow the
requested operation based on response to the query
Keohane discloses:
receiving, by a computer system from the device, a request to perform a requested operation (Fig. 2, para 0045 "For example, device training program 200 may receive input indicating a current environment associated with a work location of the user of smart device 130. Device training program 200 identifies the current environment with a label, such as "main work location', which may distinguish from other occasional work locations for the user of smart device 130. In some embodiments, device training program 200 may receive user input of a label for the current environment, and in other embodiments, device training program 200 may generate a label for the current environment." Para 0053 "Having trained the model for given known input and output information, device training program 200 determines response actions (step 260). Training of the model may include various factors in considering whether smart device 130 is lost, stolen, or misused. Device training program 200 includes weighted values in determining the probability of the status of smart device 130. Some input factors may carry more weight than others lost or stolen determinations, taking into account variations of an environment in which smart device 130 is located. For example, the absence detecting devices of several co-workers from a known work location on a Friday in July may carry less weight in determining a probability that smart device 130 is lost, due to vacations. In the same circumstances, the location, time of day, and the detection of a known work wireless network may carry much greater weight in determination of a probability that smart device 130 is lost.")
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for , a method for monitoring Internet of Things (loT) device health of Gupta to include receiving, by a computer system from the device, a request to perform a requested operation, as taught by Keohane.
The motivation would have been to properly detect if a person and/or a device deviates from normal pattern of behavior.
Gupta in view of Keohane does not disclose:
wherein the query asks about a prior pattern of actions of the device
receiving a response to the query; and determining whether to allow the
requested operation based on response to the query
Almurayh discloses:
receiving, by a computer system from the device, a request to perform a
requested operation, receiving a response to the query; and determining whether
to allow the requested operation based on response to the query (para 0029
"FIG. 2 is an exemplary illustration of software architecture for a smart home
anomaly detector 102, according to certain embodiments. Processing circuitry of
the SHAD 102 continuously learns the status of the at least one smart appliance
114 in the home, detects a change in status of at least one smart appliance 114,
determines whether the change in status of the at least one smart appliance 114
is normal and/or expected, issues alerts to the homeowner via the computer 110
or mobile device 112 if an activity related to one of the appliances is abnormal,
and learns from the homeowner's response to the anomalous event." Para 0033
" The detector module 202 compares the observed status for the appliance to a
normal status of the appliance that has been determined by the learner module
204, as will be discussed further herein. Throughout the disclosure, the "normal"
status of an appliance refers to an "expected" or "learned" status based on user
preferences, learned patterns of appliance use, and the like. In addition, an
"abnormal" or "anomalous" status of an appliance refers to an "unexpected"
status that differs from one or more normal or learned statuses of the appliance." Para 0036 "According to certain embodiments, the informer module 206 receives a response from the external device of an interested party, such as the homeowner, verifying whether the status of the smart appliance is actually anomalous. For example, based on the learned patterns of use of a washing machine, the processing circuitry of the detector module 202 may determine that the status of "running" for the washing machine at 12:00 PM on a Wednesday is anomalous, and the informer module 206 issues an alert to the mobile device 112 of the homeowner via the network 104 regarding the anomalous event of the running washing machine. If, in fact, the homeowner is using the washing machine at the time that the alert is issued, the homeowner can respond to the alert to indicate that the running washing machine is a normal event. The informer module 206 outputs the response from the interested party to the learner module 204 so that the historical data can be updated based on the response of the homeowner. Details regarding processes performed by the informer module 206 are discussed further herein.")
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for monitoring Internet of Things (IoT) device health of Gupta in view of Keohane to include receiving, by a computer system from the device, a request to perform a requested operation, receiving a response to the query; and determining whether to allow the requested operation based on response to the query as taught by Almurayh.
The motivation would have been to properly detect if a person and/or a device deviates from normal pattern of behavior.
Gupta in view of Keohane does not disclose:
wherein the query asks about a prior pattern of actions of the device
Mytelka discloses:
wherein the query asks about a prior pattern of actions of the device (Figs. 4 and 5, para 0029 “Motion sensor triggers when client enters dining area 46, and sends signal to gateway, data captured by the System 10 is time of entrance. Client proceeds to use area, motion sensor continues to trigger movement until client has sat down. The opening of the refrigerator or use of a cooking appliance upon which an Accelerometer sensor is affixed will alert the System 10 that meals or snacks are being consumed. Upon exiting the dining area 46, the motion sensor initially triggers the motion of the client exiting the dining area 46, and when out of range from dining area 46 sensor, that sensor will stop triggering, indicating the meal has been finished. The System 10 processes the data received as follows: time of entrance to dining area 46, remains in an open State (triggered) until client leaves dining area 46, and end time is processed, recorded and the total time in dining area 46 is tabulated, and posted to the APP. See FIGS. 1-6. This total time and the frequency of times over a 24 hour period is and compared with prescribed Pattern time programmed previously into the system 10 for the particular client (which may be altered by client or caregiver over a period of time based on information of actual Pattern times practiced). If total time tabulated is within the Pattern, it is designated as normal, and posted to the database and Mobile App (data is used to determine patterns over time, see FIG. 3, Historical Graphs) of the System 10. If total time tabulated falls below or exceeds the Pattern time, it is designated as an alert and posted to the database and Mobile App of the System 10 (see FIG. 1). If sensors continue to triggers within the dining area 46 for a unacceptable length of time, or if they stop sensing movement, and no other sensors in the living environment triggers indicating movement, a Red Alert is reported to the System 10, which triggers monitoring alert to be issued to both caregivers (via the Mobile App) and an independent contract monitoring service with instructions to call in EMS (an Emergency Medical Services).” Mytelka discloses providing an alert that includes past pattern of actions of a device and the care giver or 24/7 monitoring system can response to that alert. The alert is a query/challenge as described in Applicant’s specification on paragraph 0056 where a notification with a prior pattern of action is present to a user which Mytelka discloses in the alert. )
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for monitoring Internet of Things (IoT) device health of Gupta in view of Keohane and Almurayh to include wherein the query asks about a prior pattern of actions of the device, as taught by Mytelka.
The motivation would have been to properly detect whether a user and a device associated with that device is deviating from normal user/device activities.
As per claim 3, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 2, wherein the regular pattern of actions comprises at
least one of functions, operations, or operating states of the device in association
with the communication network (Gupta para 0107 and TABLE 2 (under
paragraph 0007).
As per claim 4, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 2, wherein the data model comprises an artificial
neural network (ANN) (Keohane para 0016 and 0035, The motivation would have
been to properly detect if a person and/or a device deviates from normal pattern
of behavior).
As per claim 5, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 4, wherein generating the data model comprises
training the ANN to detect the regular pattern of actions (Keohane para 0016
and 0035, The motivation would have been to properly detect if a person and/or
a device deviates from normal pattern of behavior).
As per claim 6, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 4, wherein comparing the requested operation with
the regular pattern of actions comprises providing the requested operation to the
ANN and receiving, from the ANN an indication of whether the requested
operation matches the regular pattern of actions or not (Keohane para 0035,
0036, 0038 and 0062, The motivation would have been to properly detect if a
person and/or a device deviates from normal pattern of behavior).
As per claim 7, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 2, wherein the query is based on the regular pattern
of actions (Gupta para 0103, 0115 and 0123).
As per claim 8, Gupta in view of Keohane, Almurayh and Mytelka discloses:
The method of claim 2, wherein the user interface is associated with the
device (Gupta para 0060, 0068, 0103, 0133, and 0137).
As per claim 9, the implementation of the method of claim 2 will execute the
apparatus of claim 9. The claim is analyzed with respect to claim 2.
As per claim 10, the claim is analyzed with respect to claim 3.
As per claim 11, the claim is analyzed with respect to claim 4.
As per claim 12, the claim is analyzed with respect to claim 5.
As per claim 13, the claim is analyzed with respect to claim 6.
As per claim 14, the claim is analyzed with respect to claim 7.
As per claim 15, the claim is analyzed with respect to claim 8.
As per claim 16, the implementation of the method of claim 2 will execute
the non-transitory computer-readable storage medium claim 9. The claim is
analyzed with respect to claim 2.
As per claim 17, the claim is analyzed with respect to claim 3.
As per claim 18, the claim is analyzed with respect to claim 4.
As per claim 19, the claim is analyzed with respect to claim 5.
As per claim 20, the claim is analyzed with respect to claim 6.
As per claim 21, the claim is analyzed with respect to claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY S GRACIA whose telephone number is (571)270-5192. The examiner can normally be reached Monday-Friday 9am-6pm.
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/GARY S GRACIA/Primary Examiner, Art Unit 2499
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