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 .
Status of the Claims
Claims 1-10 are currently pending.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim 1, the claim recites:
building a relationship model among coal mine multi-disaster fusion early warning object entities, constructing a relationship network of the coal mine multi-disaster fusion early warning object entities, and updating the network by rules;
building a sensor monitoring object trend prediction model based on a long short term memory model of an LSTM artificial neural network, and establishing natural fission analysis rules;
establishing an early anomaly identification method for a single disaster of a coal mine based on a disaster index prediction method and a disaster early warning system; and
after a disaster prediction and early warning index of a coal mine is abnormal, traversing other disaster prediction and early warning indices based on a depth-first traversal algorithm and natural fission rules, suggesting that targeting prediction and forecasting should be carried out to realize multi-disaster fusion early warning.
The limitation of building a relationship model among coal mine multi-disaster fusion early warning object entities and constructing a relationship network of the coal mine multi-disaster fusion early earning object entities, and updating the network by rules, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a network,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a network” language, “building” and “constructing” in the context of this claim encompasses the user manually determining relationships between the early warning object entities.
Similarly, the limitation of building a sensor monitoring object trend prediction model based on a long short term memory model of an LSTM artificial neural network, and establishing natural fission analysis rules, as drafted, is a process that, under broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a network,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a network” language, “building” and “establishing” in the context of this claim encompasses the user manually determining a model and establishing rules for the model. Furthermore, it recites data-collection, data-structuring, mathematical/algorithmic steps, and predictive modeling. All those steps can be performed in mentally or by using pen/paper.
Similarly, the limitation of establishing an early anomaly identification method for a single disaster of a coal mine based on a disaster index prediction method and a disaster early warning system, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “establishing” in the context of this claim encompasses the user manually determining a method for early anomaly identification.
Finally, the limitation of traversing other disaster prediction and early warning indexes based on a depth-first traversal algorithm and natural fission rules and also suggesting that targeted prediction and forecasting should be carried out to realize multi-disaster fusion early warning, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “traversing” and “suggesting” in the context of this claim encompass the user manually determining a method for early anomaly identification.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using networks in relation to the building and constructing steps. The networks in both steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of modeling information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using networks to perform both the “building” and “constructing” steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 1, characterized in that: in the S1, the coal mine multi-disaster fusion early warning object entities include prediction and early warning indexes for a single disaster and sensor monitoring objects; if a sequence {xt} of an object X and a sequence {yt} of an object Y have a relationship set R, the relationship therebetween is defined as XHY; according to different ways of relationship acquisition, the relationship is divided into a relationship R1 based on a formation mechanism and a production process, a position relationship R2 based on spatial topology and a numerical relationship R3 based on a correlation coefficient; the relationship R1 is inquired from coal mine disaster prediction and early warning data and production process specifications; the relationship R2 is obtained by a spatial topology algorithm of a coal mine geographic information system; and the relationship R3 is solved by a correlation coefficient index for a pooling value sequence for a specific time window; a relationship weight wR of the objects X and Y is the sum of multiple relationship weights thereof, and the specific formula is as follows: WRY WR (1)z=1,2,3 wherein WRisa weight corresponding to an ith relationship type between the objects X and Y; and a corresponding relationship between a relationship type and a relationship weight of multi- disaster fusion natural fission objects is: the relationship type is a mechanism process relationship based on a formation mechanism and a production process R1, an assessment index is a mechanism process, a relationship weight of a physical mechanics relationship is 1, and a relationship weight of upstream and downstream of the process is 0.5; the relationship type is a position relationship R2, an assessment index is spatial topology, a relationship weight of inclusion or intersection is 0.5, and a relationship weight of adjacency within 20m is 0.2; the relationship type is a numerical relationship R3, an assessment index is a correlation coefficient index P, a relationship weight of PE(0.8,1] is 0.8, a relationship weight of PE(0.6,0.8] is 0.5, and a relationship weight of PE(0.4,0.6] is 0.2.
Regarding claim 3, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 2, characterized in that: the numerical relationship R3 is solved by a correlation coefficient of a pooling value sequence for a specific time window, specifically: for a prediction and early warning index A and a sensor monitoring object B corresponding to a coal mine disaster, a time step of A is set to At; based on data in the most recent period, a maximum value, a minimum value and a mean value of a pooling index of B in a time window At are used to generate a pooling sequence Bk, wherein k=1,2,3; then a correlation coefficient rk of a sequence pair of A and Bk is calculated; a time window of the prediction and early warning index A is At, and a correlation coefficient index of the prediction and early warning index A and the sensor monitoring object B is calculated by the following formula:PAB=max(Irk1)k=1,2,3 (2) for a sensor monitoring object Bp and a sensor monitoring object Bq, based on the data in data in the most recent period, the mean value of the pooling index in the time window is used to obtaintwo mean sequences BR and Bq, then a correlation coefficient absolute value |rpql of the sequence pair is calculated, and a correlation coefficient index of the sensor monitoring objects BR and Bq is calculated by the following formula:PBpBqIrpql (3).
Regarding claim 4, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 1, characterized in that: in the S1, the step of constructing a relationship network of coal mine multi-disaster fusion early warning objects and updating the network by rules is specifically: for a disaster prediction and early warning index set {Ai} and a sensor monitoring object set {Bj}, i,jER+, relationships R1, R2and R3 between Ai and B1 are analyzed in turn, a relationship weight wR is looked up in a table and calculated, and a relationship A t is established when wR> 1; for the sensor monitoring object set {Bj}, j ER+, relationships R1, R2and R3 between objects BR and Bq are analyzed in turn, a relationship weight wR is looked up in a table and R:WR calculated, and a relationship ByBq is established when wR>1; and the relationship network of coal mine multi-disaster fusion early warning objects is constructed; the relationship R1 between objects established according to the formation mechanism is a fixed relationship, the relationship R1 between objects established according to the production process is updated synchronously after a process flow is changed, the relationship R2 between objects established according to the spatial topology is updated synchronously as positions of the objects are changed, and the relationship R3 between objects established according to numerical correlation indexes is updated every other week.
Regarding claim 5, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 1, characterized in that: in the S2, hyper-parameter selection of the LSTM model needs to refer to stationarity and periodicity of time sequences of coal mine sensor monitoring objects; before model training, data is standardized by a normalization method; and for the model training, a cross entropy is used as a loss function, a gradient clipping method is used to constrain a gradient, a stochastic gradient descent method is used to optimize the model, and a prediction precision of the model on a test set is calculated successively.
Regarding claim 6, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 1, characterized in that: a window mean sequence of the sensor monitoring objects in the most recent period is used in analysis of the stationarity and periodicity of the time sequences of the sensor monitoring objects; stationarity analysis is performed on the sequence by a unit root test method; if the sequence is not stationary, stationarity analysis is performed after the sequence is further differentiated until the resulting sequence is stationary, so as to obtain a differential order Nd; if the sequence is stationary, the differential order is set to Nd= 0; periodicity analysis is performed on the sequence by fast Fourier transform, a spectrum map of the sequence is drawn with a frequency as an abscissa and an amplitude as an ordinate, and a specific frequency F of a signal is obtained by identifying a peak value, so as to obtain a sequence period T =1/F; and if no significant peak value exists in the spectrum map, the sequence does not have periodicity, and T=0.
Regarding claim 7, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 5, characterized in that: parameter selection of the LSTM model is: for the LSTM model of a data input number d, a batch size n and a hidden unit number h of a sensor monitoring object, input data are XtERn"xd and ytEand a design initialization hyper-parameter selection formula is as follows:d= max (( x 1.5),h=(d*ln(Nd (4) wherein T is a period of the sequence, At is a pooling time window of the mean sequence, and Nd is a stationary differential order of the sequence; and ( ) is a rounding operation.
Regarding claim 8, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 5, characterized in that: after the LSTM model is trained, the same parameters of the data are standardized to make predictions, and predicting results need to be reversely standardized for use; and based on the latest set of data input at the current time, an input meanvalue E and a predicted value f1 are obtained, fi is pushed into the data input to obtain a predicted value f2, and the formula of a prediction trend index of the sensor monitoring object is as follows:S= max (fi,(f2-91)*£+E) (5) wherein E is a scaling factor which takes different values for different types of sensor monitoring objects;a duration of a first training data set of the LSTM model is not less than Tm; when increment in the data reaches Tm and a total duration of the training data set is less than 10Tm, the model is retrained once, and prediction precisions of two adjacent models in the next K times are compared and selected, during which the original model is still used for model prediction to balance data feature extraction and over-fitting.
Regarding claim 9, the claim recites: The coal mine multi-disaster fusion natural fission early warning method as claimed in claim 1, characterized in that: in the S4, after a prediction and early warning index of a coal mine has an early anomaly feature, the depth-first algorithm is used to traverse the relationship network of coal mine multi-disaster fusion early warning objects, and for each specific sensor monitoring object traversed, the LSTM model is used to calculate a prediction trend index S; if the value of the index S is normal, a current node is no longer deeply traversed; and if the value of the index S exceeds a threshold, it is considered that a current sensor monitoring object conforms to the natural fission rules, and the depth traversal is continued until the entire relationship network of early warning objects is traversed, so as to obtain correlation prediction and early warning indexes for natural fission anomaly deduction located at network endpoints, thus suggesting that the management should carry out targeted prediction and forecasting to realize multi-disaster fusion early warning.
Regarding claim 10, the claim recites: 10. (Original) A coal mine multi-disaster fusion natural fission early warning system, characterized in that: the system comprises: a monitoring and early warning data acquisition and storage module used to collect sensor monitoring data and prediction and early warning index data of a coal mine safety monitoring system and various disaster prediction and early warning systems at a specified frequency and store the data in a fusion early warning database; a relationship network constructing and updating module used to collect basic information of each sensor monitoring object and each prediction and early warning index object in a coal mine to generate a relationship entity list, then calculate a relationship weight between sensor monitoring objects and between a sensor monitoring object and a prediction and early warning index object by regularly editing a mechanism and process relationship and calculating geometric topology and correlation coefficients, and establish or update a relationship network of coal mine multi-disaster fusion early warning objects; a fission model training and updating module used to regularly train LSTM parameters in each sensor monitoring data trend prediction model and select appropriate parameters by comparing prediction precisions of two prediction models; a natural fission analyzing and pushing module used to traverse other disaster prediction and early warning indexes based on a depth-first traversal algorithm and natural fission rules after collecting abnormal information of a disaster prediction and early warning index, so as to highlight results on a user interface and push back the results to each prediction and early warning system; the monitoring and early warning data acquisition and storage module is used for external data access to form an object data set; the relationship network constructing and updating module generates relationship entity object list data and weight data of the relationship between objects based on the object data set, and finally generates an object relationship network; meanwhile, the fission model training and updating module trains the LSTM model based on the object data set; and after the monitoring and early warning data acquisition and storage module recognizes an early anomaly feature of a prediction and early warning index, the natural fission analyzing and pushing module uses the depth-first algorithm to traverse the object relationship network, finds a prediction and early warning index for anomaly association based on the natural fission rules, and pushes result data.
Regarding claims 2-10, The limitations above as drafted, is a process that, under broadest reasonable interpretation covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a network,” nothing in the claim element precludes the step from practically being performed in the mind. Furthermore, it recites data-collection, data-structuring, mathematical/algorithmic steps, and predictive modeling. All those steps can be performed in mentally or by using pen/paper.
Conclusion
There are no prior art rejections of claims 1-10; however, they are not allowable due to the 101 rejections as discussed above.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shao et al. (US 12,333,470),
Nguyen et al. (US 11,640,163),
Zou et al. (US 2021/0390230), and
Balkanski et al. (US 2021/0248241).
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/KERRI L MCNALLY/Primary Examiner, Art Unit 2686