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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 9), an electronic device with structural components (claims 10-15), and a non-transitory computer-readable medium (claims 16 – 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 1,
Step 2A, Prong One
The claim recites in part:
obtaining time series data of multiple indicators, for which causal relationship is to be analyzed;
For example, a person can obtain or collect data recorded at different points in time through observation or review.
clustering the multiple indicators according to probability distributions of the time series data of the multiple indicators, wherein indicators of the same category are indicators of independent identically distribution;
For example, a person can group together similar indicators based on the probability distributions.
analyzing causal connection relationships and connection directions between each of the indicators based on the clustering result, and constructing a causal relationship network structure, the causal relationship network structure including indicator nodes and directed edges connecting the indicator nodes, the directed edges being used to represent causal relationships between the connected indicator nodes;
For example, a person can analyze relationships between information and organize those relationships into a diagram using mental evaluation and pen and paper.
obtaining conditional probability tables of each of the indicator nodes in the causal relationship network structure according to the time series data of the multiple indicators;
For example, a person can obtain a table by reviewing known data or relationships and recording the corresponding conditional probabilities.
obtaining Bayes Belief Networks, according to the causal relationship network structure and the conditional probability tables of each of the indicator nodes, to represent the causal relationships between each of the indicators.
For example, a person can obtain information representing variables and probabilistic relationships between those variables, which can be received, reviewed, or identified said person.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
wherein the clustering the multiple indicators according to probability distributions of the time series data of the multiple indicators comprises:
obtaining distances between each of the indicators according to the time series data of the multiple indicators, determining the correlations between each of the indicators on the probability distribution according to the distances between the indicators, obtaining an adjacency matrix according to the correlations between each of the indicators, and using the adjacency matrix as clustering result of the multiple indicators.
For example, a person can group together similar indicators based on their location in a matrix.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
wherein the method further comprises: after the clustering the multiple indicators according to the probability distributions of the time series data of the multiple indicators, and before the analyzing causal connection relationships and connection directions between each of the indicators based on the clustering result,
discretizing the time series data of each indicator to obtain a discretized data set corresponding to each indicator;
the analyzing the causal connection relationships and connection directions between each of the indicators based on the clustering result, and constructing a causal relationship network structure comprising:
determining the causal connection relationships and connection directions between each of the indicators based on the discretized data sets corresponding to each indicator in the adjacency matrix, and constructing the causal relationship network structure.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
wherein the determining the causal connection relationships and connection directions between each of the indicators comprises:
performing independence test on each of the indicator in the adjacency matrix using a conditional independence testing method, determining indicators with conditional independence and eliminating causal connection relationships between the indicators with conditional independence and other indicators, and determining connection directions in the causal connection relationships between each of the indicator according to V-Structure and Meek Rules method to obtain a directed acyclic graph or a maximal ancestral graph, and determining the directed acyclic graph or the maximal ancestral graph as the causal relationship network structure.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
wherein the determining the causal connection relationships and connection directions between each of the indicators based on the discretized data sets corresponding to each of the indicators in the adjacency matrix, and constructing a causal relationship network structure comprises:
selecting, from the discretized data sets corresponding to each of the indicators in the adjacency matrix, first discretized data sets of each of the indicators in the current time window;
determining the causal connection relationships and connection directions between each of the indicators based on the first discretized data sets, and constructing a causal relationship network structure.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
wherein the determining the causal connection relationships and connection directions between each of the indicators based on the discretized data sets corresponding to each of the indicators in the adjacency matrix, and constructing a causal relationship network structure comprises:
selecting, from the discretized data sets corresponding to each of the indicators in the adjacency matrix, second discretized data sets of at least one first indicators in the previous time window and third discretized data sets of at least one second indicators in the current time window;
determining, based on the second discretized data sets and the third discretized data sets, causal connection relationships and connection directions between each of the first indicators in the previous time window and each of the second indicators in the current time window, and constructing a causal relationship network structure.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
wherein the obtaining the conditional probability tables of each of the indicator nodes in the causal relationship network structure comprises:
obtaining, for any indicator node that has a parent indicator node, a conditional probability of the indicator node when its parent indicator node takes each of possible values, to obtain the conditional probability table of the indicator node; or
obtaining, for any indicator node that does not have a parent indicator node, the probability distribution of the indicator node, and determining the conditional probability table of the indicator node according to the probability distribution of the indicator node.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
after the obtaining the Bayes Belief Networks,
obtaining inference conditions and prior knowledge of the relationships between any two indicators; wherein the inference conditions are the values of part of the indicator nodes in the Bayes Belief Networks;
obtaining the Most Probable Explanation that satisfies the inference conditions according to the prior knowledge and the Bayes Belief Networks, and determining values of another part of the indicator nodes in the Bayes Belief Networks based on the Most Probable Explanation.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites in part:
wherein the obtaining the Most Probable Explanation that satisfies the inference conditions comprises
obtaining the Most Probable Explanation that satisfies the inference conditions
asynchronously by using a publish/subscribe mode.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Claim 10 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The electronic device, at least one processor, and a memory are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 11 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 12 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The non-transitory computer-readable storage medium and a processor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 17 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Porwal et al (US 2023/0051416) teaches systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups
Porwal et al does not disclose or suggest obvious analyzing causal connection relationships and connection directions between each of the indicators based on the clustering result, and constructing a causal relationship network structure, the causal relationship network structure including indicator nodes and directed edges connecting the indicator nodes, the directed edges being used to represent causal relationships between the connected indicator nodes; obtaining conditional probability tables of each of the indicator nodes in the causal relationship network structure according to the time series data of the multiple indicators; obtaining Bayes Belief Networks, according to the causal relationship network structure and the conditional probability tables of each of the indicator nodes, to represent the causal relationships between each of the indicators, in combination with the rest of the claimed limitations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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