System and method for artificial intelligence issue spotting

1 . A method comprising:

training a general use generative Artificial Intelligence (AI) model, the general use generative AI model configured to receive input text and output generative text;

receiving, at a computer system, a plurality of cause of action records, each record in the cause of action records recording aspects of a historical legal event and containing a cause of action description of the historical legal event;

executing, via at least one processor of the computer system for each cause of action record in the plurality of cause of action records, a machine-executed search of one or more legal data sources associated with an individual cause of action, resulting in jurisdictional rules for each cause of action record in the plurality of cause of action records;

preparing, via the at least one processor using the jurisdictional rules, a plurality of cause of action models, wherein each cause of action model in the plurality of cause of action models comprises a cause of action record in the plurality of cause of action records modified according to the jurisdictional rules;

tuning, via at least one processor of the computer system, the general use generative AI model using the plurality of cause of action models, resulting in a tuned legal AI model; and

converting, via the at least one processor, the tuned legal AI model into an AI algorithm, wherein:

input to the AI algorithm comprises a jurisdiction and at least one piece of dispute information associated with a dispute; and

output of the AI algorithm comprises a cause of action for the dispute and elements of the cause of action.

2 . The method of claim 1 , wherein each cause of action record within the plurality of cause of action records further comprises:

a name of a cause of action;

a jurisdiction of the cause of action; and

at least one legal element of the cause of action.

3 . The method of claim 2 , wherein each cause of action record within the plurality of cause of action records further comprises:

text of at least one previous legal case associated with the cause of action, the at least one previous legal case having been decided within the jurisdiction; and

text of jury instructions associated with the cause of action.

4 . The method of claim 3 , wherein the at least one previous legal case comprises at least two previous legal cases, the at least two previous legal cases comprising:

at least one precedential case; and

at least one non-precedential case.

5 . The method of claim 2 , wherein each cause of action record within the plurality of cause of action records further comprises:

at least one of a legal precedent or a statutory authority, wherein the at least one legal element of the cause of action is provided by the at least one of a legal precedent or the statutory authority.

6 . The method of claim 1 , wherein the plurality of cause of action records are generated using crowdsourcing by one or more additional users, wherein each record in the plurality of cause of action records comprises:

a name of the cause of action;

a jurisdiction of the cause of action;

a text description of the cause of action;

at least one legal element of the cause of action;

text of at least two previous legal cases associated with the cause of action, the at least two previous legal cases having been decided within the jurisdiction,

the at least two previous legal cases comprising:

at least one precedential case; and

at least one non-precedential case;

text of jury instructions associated with the cause of action; and

at least one defense of the cause of action.

7 . The method of claim 6 , wherein, upon the AI algorithm identifying the cause of action, an identity of a specific user is identified within the one or more additional users, wherein the specific user generated the cause of action record associated with the cause of action.

8 . The method of claim 7 , wherein the computer system pays the specific user based on usage of the cause of action record.

9 . The method of claim 1 , wherein the input to the AI algorithm further comprises a location of the dispute; and

wherein the output of the AI algorithm further comprises at least one previously decided case, the at least one previously decided case identifying a basis for judgment in the at least one previously decided case was determined, the judgment relying on the cause of action for the dispute.

10 . The method of claim 1 , further comprising:

receiving, at the computer system, input information about a specific party involved in a specific dispute, the input information comprising:

a location of the specific party;

at least one piece of evidence, the at least one piece of evidence comprising one or more of: a picture, an email, or a transcription; and

at least one piece of dispute information associated with the specific party;

executing, via the at least one processor, the AI algorithm wherein:

inputs to the AI algorithm comprise the input information; and

outputs of the AI algorithm comprise a cause of action for the specific dispute and the elements of the cause of action for the specific dispute; and

transmitting, from the computer system to a user computing device, the outputs.

11 . A system comprising:

at least one processor; and

a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

training a general use generative Artificial Intelligence (AI) model, the general use generative AI model configured to receive input text and output generative text;

receiving a plurality of cause of action records, each record in the cause of action records recording aspects of a historical legal event and containing a cause of action description of the historical legal event;

executing, for each cause of action record in the plurality of cause of action records, a machine-executed search of one or more legal data sources associated with an individual cause of action, resulting in jurisdictional rules for each cause of action record in the plurality of cause of action records;

preparing, using the jurisdictional rules, a plurality of cause of action models, wherein each cause of action model in the plurality of cause of action models comprises a cause of action record in the plurality of cause of action records modified according to the jurisdictional rules;

tuning the general use generative AI model using the plurality of cause of action models, resulting in a tuned legal AI model; and

converting the tuned legal AI model into an AI algorithm, wherein:

input to the AI algorithm comprises a jurisdiction and at least one piece of dispute information associated with a dispute; and

output of the AI algorithm comprises a cause of action for the dispute and elements of the cause of action.

12 . The system of claim 11 , wherein each cause of action record within the plurality of cause of action records further comprises:

a name of a cause of action;

a jurisdiction of the cause of action; and

at least one legal element of the cause of action.

13 . The system of claim 12 , wherein each cause of action record within the plurality of cause of action records further comprises:

text of at least one previous legal case associated with the cause of action, the at least one previous legal case having been decided within the jurisdiction; and

text of jury instructions associated with the cause of action.

14 . The system of claim 13 , wherein the at least one previous legal case comprises at least two previous legal cases, the at least two previous legal cases comprising:

at least one precedential case; and

at least one non-precedential case.

15 . The system of claim 14 , wherein each cause of action record within the plurality of cause of action records further comprises:

at least one of a legal precedent or a statutory authority, wherein the at least one legal element of the cause of action is provided by the at least one of a legal precedent or the statutory authority.

16 . The system of claim 11 , wherein the plurality of cause of action records are generated using crowdsourcing by one or more additional users, wherein each record in the plurality of cause of action records comprises:

a name of the cause of action;

a jurisdiction of the cause of action;

a text description of the cause of action;

at least one legal element of the cause of action;

text of at least two previous legal cases associated with the cause of action, the at least two previous legal cases having been decided within the jurisdiction,

the at least two previous legal cases comprising:

at least one precedential case; and

at least one non-precedential case;

text of jury instructions associated with the cause of action; and

at least one defense of the cause of action.

17 . The system of claim 16 , wherein, upon the AI algorithm identifying the cause of action, an identity of a specific user within the one or more additional users is identified, wherein the specific user generated the cause of action record associated with the cause of action.

18 . The system of claim 11 , wherein the input to the AI algorithm further comprises a location of the dispute.

19 . The system of claim 11 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving input information about a specific party involved in a specific dispute, the input information comprising:

a location of the specific party;

at least one piece of evidence, the at least one piece of evidence comprising one or more of: a picture, an email, or a transcription; and

at least one piece of dispute information associated with the specific party;

executing the AI algorithm wherein:

inputs to the AI algorithm comprise the input information; and

outputs of the AI algorithm comprise a cause of action for the specific dispute and the elements of the cause of action for the specific dispute; and

transmitting, to a user computing device, the outputs.

20 . A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

training a general use generative Artificial Intelligence (AI) model, the general use generative AI model configured to receive input text and output generative text;

receiving a plurality of cause of action records, each record in the cause of action records recording aspects of a historical legal event and containing a cause of action description of the historical legal event;

executing, for each cause of action record in the plurality of cause of action records, a machine-executed search of one or more legal data sources associated with an individual cause of action, resulting in jurisdictional rules for each cause of action record in the plurality of cause of action records;

preparing, using the jurisdictional rules, a plurality of cause of action models, wherein each cause of action model in the plurality of cause of action models comprises a cause of action record in the plurality of cause of action records modified according to the jurisdictional rules;

tuning the general use generative AI model using the plurality of cause of action models, resulting in a tuned legal AI model; and

converting the tuned AI model into an AI algorithm, wherein:

input to the AI algorithm comprises a jurisdiction and at least one piece of dispute information associated with a dispute; and

output of the AI algorithm comprises a cause of action for the dispute and elements of the cause of action.