OBJECT-ORIENTED AI MODELING

1 . A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:

obtaining, with one or more processors, for a plurality of entities, datasets, wherein:

the datasets comprise a plurality of entity logs;

the entity logs comprise events involving the entities;

at least some of the events are actions by the entities;

at least some of the actions are targeted actions;

the entity logs comprise or are otherwise associated with attributes of the entities; and

the events are distinct from the attributes;

orchestrating, with one or more processors, an object-orientated application or service by:

forming a plurality of objects, wherein each object of the plurality of objects comprises a different set of attributes and events;

forming object-oriented labeled datasets based on the event and the attributes of each of the datasets;

forming a library or framework of classes with a plurality of object-orientation modelors; and

forming a plurality of object-manipulation functions, each function being configured to leverage a respective class among the library or framework of classes;

receiving, with one or more processors, a request to determine a set of actions to achieve, or increase likelihood of, a given targeted action;

assigning, with one or more processors, the given targeted action to a first subset of classes from the library or framework of classes of the object-orientated application or service; and

determining, with one or more processors, based on the assigning, the set of actions to achieve, or increase likelihood of, the given targeted action using a first subset of the plurality of object-manipulation functions leveraging the first subset of classes from the library or framework of classes of the object-orientated application or service.

2 . The medium of claim 1 , wherein the orchestrating further comprises:

adding version numbers to the datasets;

adding primary surrogate keys to the datasets and updating the version numbers; and

encoding the datasets in dimensional star schema and updating the version numbers.

3 . The medium of claim 1 , wherein the orchestrating further comprises:

forming a first training dataset from the datasets;

training, with one or more processors, a first machine-learning modeling pipeline on the first training dataset by adjusting parameters of the first machine-learning modeling pipeline to optimize a first objective function that indicates an accuracy of the plurality of object-orientation modelors in generating the library or framework of classes; and

storing, with one or more processors, the adjusted parameters of the trained first machine-learning modeling pipeline in memory.

4 . The medium of claim 3 , wherein training comprises steps for training.

5 . The medium of claim 1 , wherein the orchestrating further comprises:

forming a second training dataset from the datasets;

training, with one or more processors, a second machine-learning model on the second training dataset by adjusting parameters of the second machine-learning model to optimize a second objective function that determines the first subset of the plurality of object-manipulation functions; and

storing, with one or more processors, the adjusted parameters of the trained second machine-learning model in memory.

6 . The medium of claim 1 , wherein the plurality of entity logs comprise:

consumers;

communications to consumers by an enterprise;

communications to an enterprise by consumers;

purchases by consumers from an enterprise;

non-purchase interactions by consumers with an enterprise; and

a customer relationship management system of an enterprise.

7 . The medium of claim 6 , wherein:

the enterprise is a credit card issuer and the given targeted action is predicting whether a consumer will default;

the enterprise is a lender and the given targeted action is predicting whether a consumer will borrow;

the enterprise is an insurance company and the given targeted action is predicting whether a consumer will file a claim;

the enterprise is an insurance company and the given targeted action is predicting whether a consumer will sign-up for insurance;

the enterprise is a vehicle seller and the given targeted action is predicting whether a consumer will purchase a vehicle;

the enterprise is a seller of goods and the given targeted action is predicting whether a consumer will file a warranty claim,

the enterprise is a wireless operator and the given targeted action is predicting whether a consumer upgrade their cellphone, or

the enterprise is a bank and the given targeted action is predicting GDP variation.

8 . The medium of claim 1 , wherein the assignment of the given targeted action to a first subset of classes comprises:

assigning the given targeted action to a first subset of the plurality of objects using a second subset of the plurality of object-manipulation functions; and

determining the first subset of classes from the library or framework of classes of the object-orientated application or service that are related to the first subset of the plurality of objects.

9 . The medium of claim 8 , wherein second subset of the plurality of object-manipulation functions are configured to add new objects to the plurality of objects.

10 . The medium of claim 9 , wherein the new objects comprise attributes and events related to the given targeted action.

11 . The medium of claim 1 , wherein the datasets comprise:

a data frame;

a data stream;

a column in a table;

a row in a column;

a cell in a table;

structured data; and

unstructured data.

12 . The medium of claim 1 , wherein the plurality of object-manipulation functions comprises:

a sequence function used to change a collection of events into a time sequences for processing;

a feature function used to gather features of a first object-orientation modelor and then use the features in a second object-orientation modelor;

an economic function used to:

gather economic objectives and economic constraints of an entity; and

employ an allocation algorithm to maximize the objectives; and

an ensembling function used to combine a first subset of the library or framework of classes.

13 . The medium of claim 12 , wherein the plurality of object-manipulation functions are arranged to perform in series.

14 . The medium of claim 12 , wherein the plurality of object-manipulation functions are arranged to change orders dynamically based on the given targeted action.

15 . The medium of claim 1 , wherein the plurality of object-orientation modelors comprises:

a scaled propensity modelor used to calculate probability of a customer making an economic commitment;

a timing modelor used to calibrate moments in time when a customer is likely to engage with the given targeted action;

an affinity modelor used to capture ranked likes and dislikes of an entity's customers for a first subset of targeted actions;

a best action modelor used to create a framework for concurrent Key Performance Index of the given targeted action at different points in a customer's journey; and

a cluster modelor used to group an entity's customers based on the customers' behavior into a finite list.

16 . The medium of claim 15 , wherein the plurality of object-orientation modelors are arranged to perform in series.

17 . The medium of claim 15 , wherein the plurality of object-orientation modelors are arranged to perform in parallel.

18 . The medium of claim 15 , wherein the plurality of object-orientation modelors are arranged to change orders dynamically based on the given targeted action.

19 . The medium of claim 1 , wherein:

the given targeted action comprises a plurality of sub-targets; and

at least some targets of the plurality of sub-targets are expected to happen at different times in future.

20 . The medium of claim 1 , wherein:

the given targeted action comprises a plurality of sub-targets; and

the plurality of object-orientation modelors comprises:

a scaled propensity modelor used to calculate probability of a customer making an economic commitment;

a timing modelor used to calibrate moments in time when a customer is likely to engage with each subset of the plurality of sub-targets;

an affinity modelor used to capture ranked likes and dislikes of an entity's customers for a first subset of targeted actions;

a best action modelor used to create a framework for concurrent Key Performance Index for each subset of the plurality of sub-targets at different points in a customer's journey;

a cluster modelor used to group an entity's customers based on the customers' behavior into a finite list; and

wherein:

a first subset of the plurality of object-orientation modelors are used for a first subset of the plurality of sub-targets;

a second subset of the plurality of object-orientation modelors are used for a second subset of the plurality of sub-targets; and

wherein the order in which the first subset of the plurality of object-orientation modelors perform is different from the order in which the second subset of the plurality of object-orientation modelors perform.

21 . The medium of claim 1 , wherein the object-oriented labeled datasets formed according to an ontology of events.

22 . The medium of claim 21 , wherein the ontology of events comprises Concurrent Ontology Labelling Datastore (COLD) methodology.

23 . The medium of claim 1 , wherein the object-oriented labeled datasets formed according to a hierarchal taxonomy of events.

24 . The medium of claim 1 , the operations comprising:

steps for determining the set of actions required to achieve the given targeted action.

25 . A method, comprising:

obtaining, with one or more processors, for a plurality of entities, datasets, wherein:

the datasets comprise a plurality of entity logs;

the entity logs comprise events involving the entities;

at least some of the events are actions by the entities;

at least some of the actions are targeted actions;

the entity logs comprise or are otherwise associated with attributes of the entities; and

the events are distinct from the attributes;

orchestrating, with one or more processors, an object-orientated application or service by:

forming a plurality of objects, wherein each object of the plurality of objects comprises a different set of attributes and events;

forming object-oriented labeled datasets based on the event and the attributes of each of the datasets;

forming a library or framework of classes with a plurality of object-orientation modelors; and

forming a plurality of object-manipulation functions, each function being configured to leverage a respective class among the library or framework of classes;

receiving, with one or more processors, a request to determine a set of actions to achieve, or increase likelihood of, a given targeted action;

assigning, with one or more processors, the given targeted action to a first subset of classes from the library or framework of classes of the object-orientated application or service; and

determining, with one or more processors, based on the assigning, the set of actions to achieve, or increase likelihood of, the given targeted action using a first subset of the plurality of object-manipulation functions leveraging the first subset of classes from the library or framework of classes of the object-orientated application or service.