Method for automatically linking associated incidents related to criminal activity
AssigneeDetective Analytics LLC
InventorDean TAKACS
Life of this patent
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Abstract
A method for automatically linking associated incidents related to criminal activity is disclosed. A system for processing the method is also disclosed. The method operates by scraping text from a number of incident reports. The scraped text is then analyzed to determine the present of one or more unique IDs, which are used to calculate similarity between each incident report. The system employs machine-learning to better identify these pairs in the future, and optionally with the assistance of a human user providing a feedback loop to enhance the machine-learning.
Claims19
A method for automatically linking related incidents related to criminal activity, comprising:
- receiving at least two incident reports, each incident report containing a portion of raw text, a time indicator, and a location indicator,
- wherein the portion of raw text contains information that relates to at least one item of information pertaining to a criminal event;
- applying at least one set of rules to the portion of raw text,
- wherein each set of rules is configured to determine whether one or more unique IDs is located in the portion of raw text from each of the at least two incident reports;
- determining through the application of the at least one set of rules to the portion of raw text, whether one or more unique IDs is present in any of the at least two incident reports;
- generating a vector for each pair of the at least two incident reports,
- wherein the vector has a plurality of dimensions comparing the pair of incident reports;
- applying one or more machine-learning algorithms to the vectors to determine a pairwise probability between each of the at least two incident reports;
- and
- automatically linking pairs of incident reports when the pairwise probability is above a predetermined threshold amount.
A method of creating synthetic positive links for use in a training dataset for training a model that takes a plurality of pairs, each comprised of two individual components, each pair as a single observation, along with a plurality of distance variables between each of the two individual components of the plurality of pairs, comprising:
- selecting an amount of synthetic positive links to be created;
- creating a synthetic subset of the training dataset having a plurality of rows of data,
- wherein each row corresponds to one of the plurality of pairs;
- creating a vector for the plurality of distance variables and similarity variables, where the length of each vector corresponds to the plurality of rows of data;
- synthetically populating the plurality of distance variables and similarity variables by selecting a random value equivalent to a percentile from a random distribution having a probability density,
- wherein the random distribution is within the range of 0 to 1, and
- wherein the probability density monotonically decreases within that range;
- extracting the percentile from all of the values for a given distance variable from the non-synthetic training set;
- extracting 1 minus the percentile from all of the values for a given similarity variable from the non-synthetic training set;
- repeating the previous steps for each distance variable;
- and
- adding each of the synthetic positive links as a positive link in the training set.
Dependencies are inferred from the claim text.