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US 12,008,472 B2Granted
Apparatus and method for generating a compiled artificial intelligence (AI) model
AssigneeDavid Cook
InventorDavid Cook
Life of this patent
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Abstract
An apparatus and method for generating a compiled artificial intelligence (AI) model. The apparatus incudes a processor that is configured to receive data sets from user devices. The processor is further configured to convert the data sets using a machine-learning model into a cleansed data format and generate an accumulated model using the converted data sets as training data.
Claims18
An apparatus for generating a compiled artificial intelligence (AI) model the apparatus comprising:
- a processor;
- and
- a memory communicatively coupled to the processer, the memory containing instructions configuring the processor to:
- receive a plurality of data sets, wherein:
- each data set of the plurality of data sets has a corresponding source;
- and
- receiving each data set includes receiving the data set from a user device of a plurality of user devices, wherein the received data set comprises meta data describing a data bucket associated with the received data set;
- generate, for each data set of the plurality of data sets, a serial identifier, wherein the serial identifier comprises entries that are verified on an immutable sequential listing, wherein the immutable sequential listing further verifies the corresponding source of each data set of the plurality of data sets which is used to generate an accumulated model;
- generate a smart contract associated with the plurality of data sets, wherein:
- generating the smart contract further comprises generating the smart contract as a function of each serial identifier and each user device of the plurality of user devices and wherein_the smart contract is configured to validate each user device as a function of the serial identifier;
- and
- the smart contract is configured to make a payment as a function of a conditional trigger;
- convert each data set of the plurality of data sets using a machine-learning model into a cleansed data format, wherein:
- the conversion comprises:
- parsing each data set of the plurality of data sets;
- correcting the parsed data set, wherein correcting the parsed data set further comprises:
- determining a selected unit of measurement for the parsed data based on an initial unit of measurement of the parsed data;
- and
- calculating an adjusted value of the parsed data set as a function of the selected unit of measurement;
- relabeling the parsed data set
- standardizing each data set of the plurality of data sets;
- and
- the cleansed data format comprises a plurality of converted data sets;
- and
- generate the accumulated model as a function of real time data received from a data well using the plurality of converted data sets as training data and the serial identifier, wherein the conditional trigger is configured to determine that the accumulated model is using a data set of the plurality of converted data sets and to make a payment to the corresponding source of a data set of the plurality of converted data sets as a function of the determination.
22 more elements
A method for generating a compiled artificial intelligence (AI) model, the method comprising:
- receiving, by a processor, a plurality of data sets, wherein:
- each data set of the plurality of data sets has a corresponding source;
- and
- receiving each data set includes receiving the data set from a user device of a plurality of user devices, wherein the received data set comprises meta data describing a data bucket associated with the received data set;
- generating, by the processor, for each data set of the plurality of data sets, a serial identifier, wherein the serial identifier comprises entries that are verified on an immutable sequential listing, wherein the immutable sequential listing further verifies the corresponding source of each data set of the plurality of data sets which is used to generate an accumulated model;
- generating, by the processor, a smart contract associated with the plurality of data sets, wherein:
- generating the smart contract further comprises generating the smart contract as a function of each serial identifier and each user device of the plurality of user devices;
- the smart contract is configured to validate each user device as a function of the serial identifier and wherein_the smart contract is configured to make a payment as a function of a conditional trigger;
- converting, by the processor, each data set of the plurality of data sets using a machine-learning model into a cleansed data format, wherein:
- the conversion comprises:
- parsing each data set of the plurality of data sets;
- correcting the parsed data set, wherein correcting the parsed data set further comprises:
- determining a selected unit of measurement for the parsed data based on an initial unit of measurement of the parsed data;
- calculating an adjusted value of the parsed data set as a function of the selected unit of measurement;
- and
- relabeling the parsed data set;
- standardizing each data set of the plurality of data sets;
- and
- the cleansed data format comprises a plurality of converted data sets;
- and
- generating, by the processor, accumulated model as a function of real time data received from a data well using the plurality of converted data sets as training data and the serial identifier, wherein the conditional trigger is configured to determine that the accumulated model is using a data set of the plurality of converted data sets and to make a payment to the corresponding source of a data set of the plurality of converted data sets as a function of the determination.
18 more elements
Dependencies are inferred from the claim text.