Granted Patent B2

US 12,505,291 · App. 19/019,365 · Granted Dec 23, 2025

Inventors: Shardul Malviya (London, GB); Wayne Liao (London, GB); Deepak Jain (London, GB); Samantha Cory (London, GB); Mariusz Saternus (Cracow, PL); Daniel Lewandowski (Cracow, PL); Biraj Krushna Rath (London, GB); Stuart Murray (London, GB); Philip Davies (London, GB); Payal Jain (London, GB); Tariq Husayn Maonah (London, GB)

G06F40/216

Abstract

Claims (84)

1 . A non-transitory computer-readable storage medium comprising instructions stored thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:

obtain (1) an output generation request for generation of an output and (2) one or more indicators of a first set of actions,

wherein the output generation request includes one or more indicators of a set of guidelines, and

wherein the first set of actions are configured to adhere to constraints of the set of guidelines;

using the one or more indicators of the set of guidelines, determine a set of inputs for one or more guidelines in the set of guidelines,

wherein at least one input of the set of inputs for a particular guideline includes one or more of: (1) the particular guideline, (2) a set of pre-loaded query context defining the first set of actions, (3) a set of keywords associated with a set of textual content of corresponding guidelines of the first set of actions, or (4) the set of textual content; and

associate, using an AI model, the one or more guidelines of the set of guidelines to one or more actions in the first set of actions by:

supplying one or more inputs of the set of inputs of a corresponding guideline into the AI model, and

receiving, from the AI model, a generated second set of actions including the one or more actions of the first set of actions associated with the corresponding guideline of the set of guidelines.

2 . The non-transitory, computer-readable storage medium of claim 1 , wherein the set of guidelines includes one or more of: text, image, audio, or video format data.

3 . The non-transitory, computer-readable storage medium of claim 1 , wherein the AI model is a first AI model, and wherein the instructions further cause the system to:

supply the set of guidelines into a second AI model;

receive, from the second AI model, a set of summaries summarizing the set of guidelines; and

include at least one summary in the set of inputs.

4 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

identify sensitive information within the set of guidelines using at least one predefined indicator of the sensitive information; and

mask the identified sensitive information.

5 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

receive an indicator of a type of operation associated with the set of guidelines; and

obtain, via an application programming interface (API), the set of guidelines.

6 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

associate each guideline with a type of guideline; and

generate the second set of actions using the type of guideline of each guideline in the set of guidelines.

7 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

validate the generated second set of actions by:

comparing each action in the second set of actions against predefined validation criteria; and

generating a validation score for each action based on the comparisons.

8 . A system comprising:

at least one hardware processor; and

at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:

obtain (1) an output generation request for generation of an output and (2) one or more indicators of a first set of actions,

wherein the output generation request includes one or more indicators of a set of guidelines, and

wherein the first set of actions are configured to adhere to constraints of the set of guidelines;

using the one or more indicators of the set of guidelines, determine a set of information for one or more guidelines in the set of guidelines,

wherein at least a portion of the set of information for a particular guideline includes one or more of: (1) the particular guideline, (2) a set of pre-loaded query context defining the first set of actions, (3) a set of keywords associated with a set of textual content of corresponding guidelines of the first set of actions, or (4) the set of textual content; and

associate, using an AI model, the one or more guidelines of the set of guidelines to one or more actions in the first set of actions by:

supplying the set of information of a corresponding guideline into the AI model, and

receiving, from the AI model, a generated second set of actions including the one or more actions of the first set of actions associated with the corresponding guideline of the set of guidelines.

9 . The system of claim 8 , wherein the system is further caused to:

calculate a set of token counts within the set of guidelines; and

partition the set of guidelines into a set of segments based on predetermined character limits of the set of token counts.

10 . The system of claim 8 , wherein the one or more indicators of the set of guidelines include one or more of:

electronic tags associated with guideline types,

metadata indicating operative standard categories, or

scenario attributes defining one or more operational contexts.

11 . The system of claim 8 , wherein the set of information include one or more of:

vector representations of alphanumeric characters,

textual content extracted from the guidelines using natural language processing, or

pre-established mappings between operative standards and identified gaps.

12 . The system of claim 8 , wherein the system is further caused to:

generate a confidence score for each association between the one or more guidelines and a third set of actions; and

generate the second set of actions by filtering the associations based on a predetermined confidence threshold.

13 . The system of claim 8 , wherein the system is further caused to:

detect a set of changes in the set of guidelines; and

automatically update the second set of actions based on the detected changes.

14 . The system of claim 8 , wherein the system is further caused to:

generate a set of metadata documenting the association between the one or more guidelines and the one or more actions,

wherein the set of metadata includes one or more of: a timestamp, model version, or a confidence metric of the association.

15 . A method performed by a computer device for associating guidelines with one or more actions, the method comprising:

obtain (1) an output generation request for generation of an output and (2) one or more indicators of a first set of actions,

wherein the output generation request includes one or more indicators of a set of guidelines, and

wherein the first set of actions are configured to adhere to constraints of the set of guidelines;

using the one or more indicators of the set of guidelines, determine a set of information for one or more guidelines in the set of guidelines,

wherein the set of information for a particular guideline includes one or more of: (1) the particular guideline, (2) a set of pre-loaded query context defining the first set of actions, (3) a set of keywords associated with a set of textual content of corresponding guidelines of the first set of actions, or (4) the set of textual content; and

causing an AI model to associate the one or more guidelines of the set of guidelines to one or more actions in the first set of actions using at least a portion of the set of information of a corresponding guideline.

16 . The method of claim 15 , further comprising:

generating a set of association rules using historically associated guidelines; and

supplying the association rules to the AI model to generate subsequently associated guidelines associated subsequent to the historically associated guidelines.

17 . The method of claim 15 , further comprising:

classifying each guideline in the set of guidelines in a predefined domain of a set of predefined domains,

wherein each predefined domain is associated with a domain-specific query context; and

selecting, a particular domain-specific query context for the AI model using a corresponding predefined domain.

18 . The method of claim 15 , further comprising obtaining a knowledge graph including:

temporal dependencies between one or more guidelines,

contextual variations of the set of guidelines, and

historical patterns of associations between one or more guidelines and one or more actions.

19 . The method of claim 15 , further comprising:

identifying a set of relationships between the set of guidelines and the set of actions,

wherein each relationship in the set of relationship corresponds to a type of relationship;

assigning, for each relationship in the set of relationship, a weighted score using the type of relationship; and

aggregating the weighted scores to generate an overall score of the one or more guidelines,

wherein the AI model is caused to associate the one or more guidelines to the one or more actions using the overall score.

20 . The method of claim 15 , further comprising:

assigning a version to one or more of: (1) the one or more guidelines or (2) the one or more actions.

Assignments (1)

Continuity (6)

References Cited (125)