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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 5-12 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites
1. A method comprising: identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors;
processing the identified and labeled patterns to estimate a probability and a lead time for a change to an area associated with the one or more sensors from a current sensor stage to another sensor stage,
the processing the identified and labeled patterns comprising a sequential error learning process configured to improve accuracy;
and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a sensor stage change for other areas associated with the neighboring sensors.
Examiner finds that the emphasized (bolded) portions of claim 1 above recite an abstract idea—namely, mental processes and mathematical concepts. See MPEP 2106.04(a)(2)(I) and (III):
The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.
. . .
Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
When read as a whole, the recited limitations are directed to using mental steps to observe, evaluate, and make judgements about electronic data and mathematical calculations.
Taking each element individually, Examiner provides the following analysis:
Bolded Abstract Idea Claim Elements
Examiner analysis of bolded abstract idea elements considered individually
1. A method comprising:
identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors;
This element merely requires observation and evaluation of the data and a judgment as to how to label to the streamed data.
processing the identified and labeled patterns to estimate a probability and a lead time for a change to an area associated with the one or more sensors from a current sensor stage to another sensor stage,
This element merely requires evaluations and judgments as to how to estimate the probability and lead time. This estimation is not limited to estimations that cannot be practically performed in the human mind.
Estimating probabilities are also mathematical calculations.
the processing the identified and labeled patterns comprising a sequential error learning process configured to improve accuracy;
This element merely describes the observations, evaluations, and judgments being performed. Examiner finds “a learning sequential error learning process configured to improve accuracy” is not limited to processes that cannot be performed in the human mind.
and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a sensor stage change for other areas associated with the neighboring sensors.
The recited probability merely requires observation, evaluation, and/or judgment. This recited probability is not limited to probabilities that cannot be practically performed in the human mind. Probabilities are also mathematical calculations.
The above analysis applies to claims 11 and 12.
Turning to the additional elements and whether they integrate the exception and whether they recite an inventive concept, Examiner provides the following analysis:
Italicized Additional elements
Examiner analysis of italicized additional elements and whether they integrate the exception and whether they recite an inventive concept
Relevant MPEP sections
1. A method comprising:
This element recites mere instructions to apply the exception on a computer. It does not integrate the exception and does not recite an inventive concept.
2106.05(f).
11. A non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising:
This element recites mere instructions to apply the exception on a computer. It does not integrate the exception and does not recite an inventive concept.
2106.05(f).
12. An apparatus, comprising: a processor, configured to:
This element recites mere instructions to apply the exception on a computer. It does not integrate the exception and does not recite an inventive concept.
2106.05(f).
The above analysis applies to claims 11 and 12.
The additional elements above “[a]dd nothing … that is not already present when the steps are considered separately’”. MPEP 2106.05 (I)(B)(quoting Alice).
As such, when the claim elements are considered as a whole and individually, claim 1 recites an abstract idea without significantly more.
Dependent claims 5-10 are rejected under 35 USC 101 for the reasons indicated below.
Claim
Bold = abstract idea
Italics = additional element
Analysis
MPEP
5. The method of claim 1, wherein the identifying and labeling the patterns based on the characteristics of the sensor data streamed from the one or more sensors in the network of sensors comprises
See claim 1 above.
executing outlier removal on the sensor data
Outlier removal merely requires observation and evaluation of the data and a judgment as to which datapoints are outliers.
2106.04(a)(2)(III)
based on metadata learning of audios and images.
The language “based on metadata learning of audios and images” generally links the abstract idea to the field of use of machine learning and thus does not integrate the exception and thus does not recite an inventive concept.
2106.05(h)
6. The method of claim 1, wherein the processing the identified and labeled patterns to estimate the probability and the lead time for a change to the area associated with the one or more sensors from the current stage to another stage
See claim 1 above.
is conducted through a stochastic process trained with temporal patterns to output the probability and the lead time for all types of labeled sensor stages
This language generally links the abstract idea to the field of use of stochastic machine learning and thus does not integrate the exception and thus does not recite an inventive concept.
2106.05(h)
7. The method of claim 1, wherein the sequential error learning process configured to improve accuracy comprises
See claim 1 above.
a self-feedback loop configured to learn errors inherited from a stochastic process and improve pseudo-labels between training phases.
This language generally links the abstract idea to the field of use of semi-supervised and reinforcement learning and thus does not integrate the exception and thus does not recite an inventive concept.
2106.05(h)
8. The method of claim 1, wherein the determining, for the neighboring sensors to the one or more sensors in the network of sensors, the probability of the sensor stage change for other areas associated with the neighboring sensors
See claim 1 above.
is based on a Bayesian learning model configured to determine the probability of the sensor stage change for the other areas and an estimated time of occurrence based on the fused fingerprint labels with geographical and temporal information from the neighboring sensors in the network of sensors.
This language generally links the abstract idea to the field of use Bayesian machine learning and thus does not integrate the exception and thus does not recite an inventive concept.
2106.05(h)
9. The method of claim 1, wherein the determining, for the neighboring sensors to the one or more sensors in the network of sensors, the probability of the sensor stage change for the other areas associated with the neighboring sensors further comprises
See claim 1 above.
generating a dynamic footprint comprising ones of the network of sensors having the probability of the sensor stage change , wherein the dynamic footprint provides a visualization of the ones of the network of sensors undergoing the state change in temporal order over time.
This element recites selecting a particular data source or type of data to be manipulated and thus recites insignificant extra solution activity. It does not integrate the exception.
This element recites storing and retrieving information in memory and thus does not recite an inventive concept because it recites a well-understood, routine, and conventional computer function.
2106.05(g)
2106.05(d)(II)
10. The method of claim 1, wherein each of the current sensor stage and the another sensor stage is one of a human disturbance, a dense chainsaw, a light chainsaw, or a quiet time.
This element merely recites the data being evaluated.
2106.04(a)(2)(III)
The additional elements above “[a]dd nothing … that is not already present when the steps are considered separately’”. MPEP 2106.05 (I)(B)(quoting Alice).
As such, when the claim elements above are considered as a whole and individually, claims recite an abstract idea without significantly more.
Allowable Subject Matter
Claims 2-4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Reasons for Indicating Allowable Subject Matter
Patent Eligibility
Claim 2 recites “2. The method of claim 1, wherein the identifying and labeling the patterns based on the characteristics of the sensor data streamed from the one or more sensors in the network comprises: converting acoustic data in the streamed sensor data from time domain to frequency domain; deriving frequency domain features from the converted acoustic signals; applying dimension reduction to the frequency domain features; clustering the dimension reduced frequency domain features; and applying fingerprint analysis to identify sensor stages.”
When read in combination with claim 1 these elements cannot be practically performed in the human mind and are not mathematical concepts. These elements recite an inventive concept because they go beyond generally linking the use of the judicial exception to a particular technological environment and are also “specific limitation[s] other than what is well-understood, routine, conventional activity in the field.” MPEP 2106.05(I)(A).
Claim 4 recites “4. The method of claim 1, wherein the identifying and labeling patterns based on the characteristics of the sensor data streamed from the one or more sensors in the network of sensors further comprises synthesizing data to augment the sensor data; the synthesizing data comprising: extracting signal features from the separated noise signal and the target signal to replicate anomalous signal and the noise signal; combining the replicated anomalous signal and the noise signal through super imposing to form synthesized data; and validating the synthesized data.”
When read in combination with claim 1 these elements cannot be practically performed in the human mind and do not recite mathematical concepts per se. These elements recite an inventive concept because they go beyond generally linking the use of the judicial exception to a particular technological environment and are also “specific limitation[s] other than what is well-understood, routine, conventional activity in the field.” MPEP 2106.05(I)(A).
Prior Art
The prior art of record teaches the following claim elements:
1. A method comprising: identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors;
Mporas, Illegal Logging Detection Based on Acoustic
Surveillance of Forest, 21 Oct 2020 in Fig. 1 (network of sensors)p. 4 first full paragraph;
p. 4 last paragraph
The detection is performed using pretrained acoustic models for logging and the classification is binary, i.e., detection of logging sounds or not. Once a logging activity is detected, an alarm is activated to inform forest authorities. This can be done either by direct connection to a forest management/monitoring system. . . .
p. 5 first full paragraph (. . . assigned binary label. . . “)
1. A method comprising: identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors;
VAN STOKKOM
An Innovative Early Warning System to Tackle Illegal Deforestation p. 1
Current forest monitoring systems using remotely sensed data, are widely available (FAO, 2020). However, to effectively action illegal deforestation these systems are often too reactive, are hampered by cloud cover, or are not inclusively developed, implemented and used by stakeholders in well-defined protocols.
1. A method comprising: identifying and labeling patterns based on characteristics of sensor data streamed from one or more sensors in a network of sensors;
Arevalo Towards Real-time Illegal Logging Monitoring:
Gas-powered Chainsaw Logging Detection System
using K-Nearest Neighbors, Nov 2020 p. 158 section A
VAN STOKKOM further teaches the importance of knowing “lead time” with respect to illegal logging activities on p. 4 section 2.1:
With the EWS, decision-makers can anticipate when deforestation is going to happen, identify whether it is illegal, prioritise the deforestation predictions and plan interventions accordingly. This allows e.g. law enforcers, national park rangers and land surveyors to improve their effectiveness in protecting forested areas where the risk and impact of potential illegal deforestation is high. The time between an alert and the intervention, the so called “lead time”, is reduced ideally to such a degree that the interventions take place before the deforestation happens. This in turn will reduce illegal activities and deter future offenders. Moreover, motives for land clearance can be addressed and local communities and businesses can be stimulated to choose other paths. Furthermore, ensuring transparency1 of alerts, interventions and their effectiveness, is important to allow for informed decision making by all involved stakeholders that have different interests and priorities in the landscape.
Prior art of record fails to teach or suggest “processing the identified and labeled patterns to estimate a probability and a lead time for a change to an area associated with the one or more sensors from a current sensor stage to another sensor stage, the processing the identified and labeled patterns comprising a sequential error learning process configured to improve accuracy; and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a sensor stage change for other areas associated with the neighboring sensors”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALBERT M PHILLIPS, III whose telephone number is (571)270-3256. The examiner can normally be reached 10a-6:30pm EST M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALBERT M PHILLIPS, III/ Primary Examiner, Art Unit 2159