Training machine learning algorithms with temporaly variant personal data, and applications thereof

A computer-implemented method for linking ingested data, the method comprising:

• accessing, by one or more computing devices, data records of individuals;
• parsing, by the one or more computing devices, to locate each individual's demographic information;
• assigning, by the one or more computing devices, the demographic information into predetermined categories;
• comparing, by the one or more computing devices, each record against all other categorized records using a pair-wise function to determine if they are the same;
• calculating, by the one or more computing devices, a similarity score for each pair of records, wherein the similarity score is a ratio based on how many categories match for each data pair of records;
• determining, by the one or more computing devices, whether the similarity score meets or exceeds a similarity score threshold;
• based on determining the similarity score meets or exceeds the similarity score threshold, linking, by the one or more computing devices, the data pair in a group;
• determining, by the one or more computing devices, a most prevalent identity within the group;
• and
• modifying, by the one or more computing devices, the data records to match the most prevalent identity within the group.

A non-transitory computer readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations for linking ingested data, the operations comprising:

• accessing, by one or more computing devices, data records of individuals;
• parsing, by the one or more computing devices, to locate each individual's demographic information;
• assigning, by the one or more computing devices, the demographic information into predetermined categories;
• comparing, by the one or more computing devices, each record against all other categorized records using a pair-wise function to determine if they are the same;
• calculating, by the one or more computing devices, a similarity score for each pair of records, wherein the similarity score is a ratio based on how many categories match for each data pair of records;
• determining, by the one or more computing devices, whether the similarity score meets or exceeds a similarity score threshold;
• based on determining the similarity score meets or exceeds the similarity score threshold, linking, by the one or more computing devices, the data pair in a group;
• determining, by the one or more computing devices, a most prevalent identity within the group;
• and
• modifying, by the one or more computing devices, the data records to match the most prevalent identity within the group.

A system for linking ingested data comprising:

• a memory storing instructions;
• a computing device, coupled to the memory, configured to process the stored instructions to:
• access data records of individuals;
• parse to locate each individual's demographic information;
• assign the demographic information into predetermined categories;
• compare each record against all other categorized records using a pair-wise function to determine if they are the same;
• calculate a similarity score for each pair of records, wherein the similarity score is a ratio based on how many categories match for each data pair of records;
• determine whether the similarity score meets or exceeds a similarity score threshold;
• based on determining the similarity score meets or exceeds the similarity score threshold, link the data pair in a group;
• determine a most prevalent identity within the group;
• and
• modify the data records to match the most prevalent identity within the group.