DETAILED ACTION
This office action is responsive to the response filed 7/18/2025. The application contains claims 1-3, 5-10, 12-17, 19-20, all examined and rejected.
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 § 102
Claims 1-3, 5-10, 12-17, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dias et al. [“Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning”, hereinafter D1] Published 12/7/2016.
With regard to Claim 1,
D1 disclose a computer-implemented method, the method comprising:
receiving, by a processor, a first set of sensor data from a plurality of sensors (Col. 2, ¶1, “Our mechanism relies on real-time analysis of the data produced by sensor nodes to dynamically adapt the WSN operation to the current environmental conditions”, P.3, Col. 1, A. Observation, “First, we define wireless sensor nodes as the source of the observations made by an agent. Observations may vary, among other parameters, between temperature, relative humidity and solar radiation. In our scenario example, an observation ot is the temperature measured at time t”), the plurality of sensors having a sensor attribute associated with a first configuration (Abstract, “we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors’ sampling interval on-the-fly, according to environmental conditions”, P.1, Col. 1-2, “sampling interval affects the wireless medium access and stirs up network congestion, end-to-end delays, and sensor nodes’ energy consumption”);
determining, utilizing an artificial intelligence (AI) model, a first classification, wherein the first classification is determined based on the first set of sensor data from the plurality of sensors (Abstract, “we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors’ sampling interval on-the-fly, according to environmental conditions”, Col. 2, ¶1, “real-time analysis of the data produced by sensor nodes to dynamically adapt the WSN operation to the current environmental conditions”), wherein the Al model is trained using historical data, to classify between a plurality of contextual scenarios (P.2, A. Q-Learning, Eq. 1, “visiting several times each (s, a) pair, the agent learns which is the action that gives the best long-term reward in each state. Hence, if the number of states is high, the algorithm takes longer and requires more data to find the best action for each state, i.e., to converge. Therefore, it is critical to have a concise representation of the environment, thus to define the set of states according to the goals of the algorithm and do not include unnecessary information. In short, the set of states should illustrate only and all the characteristics that are relevant for the problem under consideration”, RL agent learn from past experience and different states is different contextual scenarios);
receiving a second set of sensor data from the plurality of sensors (P.5, Col. 1, ¶3, “every day the agent revisits states and updates its knowledge to set the most proper sampling intervals, which increases the time necessary to converge”), the second set of sensor data having a second configuration associated with the sensor attribute (P. 3, “C. Actions and transitions In the adaptive sampling interval problem, actions are used to control the sensor nodes’ sampling interval. An action can be specific, such as “set the sampling interval to 30 seconds”, or more abstract, like “increase the sampling interval””);
determining, utilizing the Al model, a second classification, wherein the second classification is determined based on the second set of sensor data from the plurality of sensors (P.5, Col. 1, ¶3, “every day the agent revisits states and updates its knowledge to set the most proper sampling intervals, which increases the time necessary to converge”);
determining whether a difference between a first quality value associated with the first classification and a second quality value associated with the second classification exceeds a threshold (P.3, Col. 1, ¶2, “As for the quality, we define the goal of the agent in terms of an accepted threshold τ . The algorithm should avoid that the absolute difference between consecutive measurements exceeds a pre-defined value (τ ), which is configured according to the monitoring application requirements. Meanwhile, higher sampling intervals are preferred to reduce the number of transmissions and, consequently, the energy consumption in sensor nodes”, P.3, Col. 2, “D. Reward, “the absolute difference between two consecutive measurements (δ) is less than τ”, P.4, Col. 1, ¶2, “If δ is smaller than one-half of τ , the sampling interval might be doubled, so we multiply the original reward by 1.5. Otherwise, if δ is greater than τ , the sampling interval is too long, and the reported data may be missing important changes in the environment. In this case, we multiply the original reward by −1”);
sending, automatically by the processor, a command to the plurality of sensors based on the difference between the first quality value and the second quality value, wherein the command sets the plurality of sensors to a configuration (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval. An action can be specific, such as “set the sampling interval to 30 seconds”, or more abstract, like “increase the sampling interval””, “action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”); and
directing, by the processor, the plurality of sensors to collect sensor data according to the configuration (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval. An action can be specific, such as “set the sampling interval to 30 seconds”, or more abstract, like “increase the sampling interval””, “action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”).
With regard to Claim 2,
D1 disclose the computer-implemented method of claim 1, wherein determining whether a difference between a first quality value associated with the first classification and a second quality value associated with the second classification exceeds a threshold, includes:
identifying that the difference between a first quality value associated with the first classification and a second quality value associated with the second classification exceeds a threshold (P.3, Col. 1, ¶2, “As for the quality, we define the goal of the agent in terms of an accepted threshold τ . The algorithm should avoid that the absolute difference between consecutive measurements exceeds a pre-defined value (τ ), which is configured according to the monitoring application requirements. Meanwhile, higher sampling intervals are preferred to reduce the number of transmissions and, consequently, the energy consumption in sensor nodes”, P.3, Col. 2, “D. Reward, “the absolute difference between two consecutive measurements (δ) is less than τ”, P.4, Col. 1, ¶2, “If δ is smaller than one-half of τ , the sampling interval might be doubled, so we multiply the original reward by 1.5. Otherwise, if δ is greater than τ , the sampling interval is too long, and the reported data may be missing important changes in the environment. In this case, we multiply the original reward by −1”); and sending the command to the plurality of sensors to set the sensor attribute to the first configuration (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval. An action can be specific, such as “set the sampling interval to 30 seconds”, or more abstract, like “increase the sampling interval””, “action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”).
With regard to Claim 3,
D1 disclose the computer-implemented method of claim 1, wherein determining whether a difference between a first quality value associated with the first classification and a second quality value associated with the second classification exceeds a threshold includes:
identifying that the difference between a first quality value associated with the first classification and a second quality value associated with the second classification does not exceed a threshold (P.4, Col. 1, ¶2, “If δ is smaller than one-half of τ , the sampling interval might be doubled, so we multiply the original reward by 1.5”);
sending the command to the plurality of sensors to set the first data attribute to a third configuration (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval … action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”);
receiving a third set of sensor data from the plurality of sensors, the plurality of sensors having the sensor attribute having a third configuration (P.5, Col. 1, ¶3, “every day the agent revisits states and updates its knowledge to set the most proper sampling intervals, which increases the time necessary to converge”);
determining a third classification, utilizing the Al model, utilizing the third set of sensor data (Abstract, “we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors’ sampling interval on-the-fly, according to environmental conditions”, P.2, ¶5, “By visiting several times each (s, a) pair, the agent learns which is the action that gives the best long-term reward in each state”);
identifying that a difference between the first quality value associated with the first classification and a third quality value associated with the third classification exceeds the threshold (P.4, Col. 1, ¶2, “Otherwise, if δ is greater than τ , the sampling interval is too long, and the reported data may be missing important changes in the environment. In this case, we multiply the original reward by −1”); and
sending a second command to the plurality of sensors to set the sensor attribute to the second configuration (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval … action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”).
With regard to Claim 5,
D1 disclose the computer-implemented method of claim 1, wherein the first configuration and the second configuration associated with the sensor attribute are associated with at least one of a volume of data collected from the plurality of sensors, a frequency of data collected from the plurality of sensors (¶3, Col. 2, ¶3, “actions are used to control the sensor nodes’ sampling interval. An action can be specific, such as “set the sampling interval to 30 seconds”, or more abstract, like “increase the sampling interval””, “action α can take one of the following values:(i) increase the sampling interval; (ii) keep the sampling interval; or (iii) reduce the sampling interval”), and a variation in types of data collected from the plurality of sensors.
With regard to Claim 6,
D1 disclose the computer-implemented method of claim 1, wherein the first quality value and the second quality value are associated with an accuracy of classification (P.5, Col. 1, ¶3, “every day the agent revisits states and updates its knowledge to set the most proper sampling intervals, which increases the time necessary to converge”, P.4, Col. 1, ¶2, “If δ is smaller than one-half of τ , the sampling interval might be doubled, so we multiply the original reward by 1.5. Otherwise, if δ is greater than τ , the sampling interval is too long, and the reported data may be missing important changes in the environment. In this case, we multiply the original reward by −1”).
With regard to Claim 7,
D1 disclose the computer-implemented method of claim 1, wherein the first quality value and the second quality value are associated with anomalies in sensor data (Col. 1, ¶2, “If δ is smaller than one-half of τ , the sampling interval might be doubled, so we multiply the original reward by 1.5. Otherwise, if δ is greater than τ , the sampling interval is too long, and the reported data may be missing important changes in the environment. In this case, we multiply the original reward by −1”).
With regard to Claim 8,
Claim 8 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 9,
Claim 9 is similar in scope to claim 2; therefore it is rejected under similar rationale.
With regard to Claim 10,
Claim 10 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 12,
Claim 12 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 13,
Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale.
With regard to Claim 14,
Claim 14 is similar in scope to claim 7; therefore it is rejected under similar rationale.
With regard to Claim 15,
Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 16,
Claim 16 is similar in scope to claim 2; therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 20,
Claim 20 is similar in scope to claim 6; therefore it is rejected under similar rationale.
Response to Arguments
Applicant arguments related to the 35 USC 101 rejection is persuasive. Therefore the examiner respectfully withdraw the rejection.
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 2020/0007420 A1 filed by Shemer et al. that disclose the ability to collect sensors data and applying different configurations based on different rules See at least Fig. 3.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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