Advanced language model for dynamic entity matching

1 . A processing system, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to:

receive, from an assistant user, a first user input comprising one or more assistant attributes;

generate an assistant profile based on the one or more assistant attributes;

match the assistant profile to a target user profile and a task associated with the target user profile;

receive interaction data between the assistant user and a target user corresponding to the target user profile while the assistant user completes the task;

receive, from the target user, a second user input comprising feedback about the assistant user completing the task;

generate a prompt comprising a set of instructions for a language model to generate a feature set associated with the assistant user based on the one or more assistant attributes, the interaction data, and the feedback;

provide the prompt, the first user input, the interaction data, and the second user input to the language model;

receive the feature set associated with the assistant user from the language model;

generate an updated assistant profile by modifying the assistant profile based on the feature set; and

match the updated assistant profile with a new target user profile associated with a new target user and a new task.

2 . The processing system of claim 1 , wherein;

to cause the processing system to match the updated assistant profile with the new target user profile, the one or more processors are configured to cause the processing system to match the updated assistant profile with the new target user profile based on rule-based entity matching, and

to cause the processing system to match the updated assistant profile with the new target user profile based on the rule-based entity matching, the one or more processors are configured to cause the processing system to:

apply one or more predefined rules to compare features of the updated assistant profile and the new target user profile, the one or more predefined rules including at least one of: matching required jurisdiction or matching required certification;

determine that the updated assistant profile meets a minimum number of years of experience requirement associated with the new task;

determine that the updated assistant profile meets an availability requirement to complete the new task; and

determine that the updated assistant profile meets a minimum historical user satisfaction score and a minimum performance threshold.

3 . The processing system of claim 1 , wherein to cause the processing system to match the updated assistant profile with the new target user profile, the one or more processors are configured to cause the processing system to generate, using a machine learning model, a recommendation to match the updated assistant profile with the new target user profile,

wherein the machine learning model is a supervised learning model selected from a group comprising a gradient boosting machine, a random forest, or a deep neural network, and is trained to predict a compatibility score between the updated assistant profile and a candidate target user profile based on features in the feature set associated with the assistant user, the features in the feature set including assistant expertise, historical task types, user feedback ratings, task complexity, and prior matching outcomes.

4 . The processing system of claim 3 , wherein to cause the processing system to generate, using the machine learning model, the recommendation to match the updated assistant profile with the new target user profile, the one or more processors are configured to cause the processing system to generate a second prompt comprising a second set of instructions for the language model to:

analyze information from the updated assistant profile and the candidate target user profile;

identify and extract a plurality of features from the updated assistant profile and the candidate target user profile, wherein the plurality of features are relevant to matching the updated assistant profile to the new target user profile;

structure the plurality of features into a format suitable for processing by the machine learning model; and

provide the plurality of features to the machine learning model for generating a compatibility score or a ranking for the candidate target user profile relative to the updated assistant profile.

5 . The processing system of claim 1 , wherein the one or more processors are further configured to execute the computer-executable instructions and cause the processing system to:

determine a set of recency values related to how recently a set of corresponding data points of the interaction data were received;

determine a set of weights associated with each data point of the interaction data and the feedback for determining which data point of the interaction data and the feedback will be relied on to match the updated assistant profile with the new target user profile;

apply a first weight to a first data point of the interaction data and the feedback and a second weight to a second data point of the interaction data and the feedback based on the set of recency values, such that the language model is configured to match the updated assistant profile with the new target user profile based on the first data point of the interaction data and the feedback, wherein the first weight is higher than the second weight and the first data point is more recent than the second data point; and

match the updated assistant profile with the new target user profile based on the first data point of the interaction data and the feedback.

6 . The processing system of claim 1 , wherein to cause the processing system to match the updated assistant profile with the new target user profile, the one or more processors are configured to cause the processing system to filter out candidate target user profiles based on not meeting one or more jurisdictional requirements associated with the task.

7 . The processing system of claim 1 , wherein:

the assistant profile includes a performance trend indicator based on aggregated feedback over a plurality of completed tasks, and

the one or more processors are further configured to execute the computer-executable instructions and cause the processing system to update the performance trend indicator based on the feature set.

8 . The processing system of claim 1 , wherein to cause the processing system to match the updated assistant profile with the new target user profile associated with the new target user and the new task, the one or more processors are configured to cause the processing system to:

determine a complexity level of the new task;

determine an experience level of the assistant user associated with the updated assistant profile; and

match the updated assistant profile with the new target user profile based on matching the complexity level of the new task to the experience level of the assistant user.

9 . The processing system of claim 1 , wherein the one or more processors are further configured to execute the computer-executable instructions and cause the processing system to:

assign the new task to a complexity category of a plurality of complexity categories; and

match the updated assistant profile with the new target user profile based on an experience level of the assistant user associated with the updated assistant profile corresponding to the complexity category to which the new task was assigned.

10 . The processing system of claim 1 , wherein the one or more processors are further configured to execute the computer-executable instructions and cause the processing system to:

receive, from the new target user, task data associated with the new task;

determine a complexity level of the new task;

generate a recommendation for the new target user to use a particular assistant user of a plurality of assistant users for completing the new task; and

receive a third user input confirming the recommendation for the new target user to use the particular assistant user for completing the new task,

wherein to cause the processing system to match the updated assistant profile with the new target user profile associated with the new target user and the new task, the one or more processors are configured to cause the processing system to match the updated assistant profile with the new target user profile based on receiving the third user input confirming the recommendation.

11 . A method, comprising:

receiving, from an assistant user, a first user input comprising one or more assistant attributes;

generating an assistant profile based on the one or more assistant attributes;

matching the assistant profile to a target user profile and a task associated with the target user profile;

receiving interaction data between the assistant user and a target user corresponding to the target user profile while the assistant user completes the task;

receiving, from the target user, a second user input comprising feedback about the assistant user completing the task;

generating a prompt comprising a set of instructions for a language model to generate a feature set associated with the assistant user based on the one or more assistant attributes, the interaction data, and the feedback;

providing the prompt, the first user input, the interaction data, and the second user input to the language model;

receiving the feature set associated with the assistant user from the language model;

generating an updated assistant profile by modifying the assistant profile based on the feature set; and

matching the updated assistant profile with a new target user profile associated with a new target user and a new task.

12 . The method of claim 11 , wherein matching the updated assistant profile with the new target user profile is based on rule-based entity matching comprising:

applying one or more predefined rules to compare features of the updated assistant profile and the new target user profile, the one or more predefined rules including at least one of: matching required jurisdiction or matching required certification;

determining that the updated assistant profile meets a minimum number of years of experience requirement associated with the new task;

determining that the updated assistant profile meets an availability requirement to complete the new task; and

determining that the updated assistant profile meets a minimum historical user satisfaction score and a minimum performance threshold.

13 . The method of claim 11 , wherein matching the updated assistant profile with the new target user profile comprises generating, using a machine learning model, a recommendation to match the updated assistant profile with the new target user profile,

wherein the machine learning model is a supervised learning model selected from a group comprising a gradient boosting machine, a random forest, or a deep neural network, and is trained to predict a compatibility score between the updated assistant profile and a candidate target user profile based on features in the feature set associated with the assistant user, the features in the feature set including assistant expertise, historical task types, user feedback ratings, task complexity, and prior matching outcomes.

14 . The method of claim 13 , wherein generating, using the machine learning model, the recommendation to match the updated assistant profile with the new target user profile comprises generating a second prompt comprising a second set of instructions for the language model to:

analyze information from the updated assistant profile and the candidate target user profile;

identify and extract a plurality of features from the updated assistant profile and the candidate target user profile, wherein the plurality of features are relevant to matching the updated assistant profile to the new target user profile;

structure the plurality of features into a format suitable for processing by the machine learning model; and

provide the plurality of features to the machine learning model for generating a compatibility score or a ranking for the candidate target user profile relative to the updated assistant profile.

15 . The method of claim 11 , further comprising:

determining a set of recency values related to how recently a set of corresponding data points of the interaction data were received;

determining a set of weights associated with a set of respective data points of the interaction data and the feedback for determining which data point of the interaction data and the feedback will be relied on to match the updated assistant profile with the new target user profile;

applying a first weight to a first data point of the interaction data and the feedback and a second weight to a second data point of the interaction data and the feedback based on the set of recency values, such that the language model is configured to match the updated assistant profile with the new target user profile based on the first data point of the interaction data and the feedback, wherein the first weight is higher than the second weight and the first data point is more recent than the second data point; and

matching the updated assistant profile with the new target user profile based on the first data point of the interaction data and the feedback.

16 . The method of claim 11 , wherein matching the updated assistant profile with the new target user profile further comprises filtering out candidate target user profiles based on not meeting one or more jurisdictional requirements associated with the task.

17 . The method of claim 11 , wherein:

the assistant profile includes a performance trend indicator based on aggregated feedback over a plurality of completed tasks, and

the method further comprises updating the performance trend indicator based on the feature set.

18 . The method of claim 11 , wherein matching the updated assistant profile with the new target user profile associated with the new target user and the new task comprises:

determining a complexity level of the new task;

determining an experience level of the assistant user associated with the updated assistant profile; and

matching the updated assistant profile with the new target user profile based on matching the complexity level of the new task to the experience level of the assistant user.

19 . The method of claim 11 , further comprising:

assigning the new task to a complexity category of a plurality of complexity categories; and

matching the updated assistant profile with the new target user profile based on an experience level of the assistant user associated with the updated assistant profile corresponding to the complexity category to which the new task was assigned.

20 . The method of claim 11 , further comprising:

receiving, from the new target user, task data associated with the new task;

determining a complexity level of the new task;

generating a recommendation for the new target user to use a particular assistant user of a plurality of assistant users for completing the new task; and

receiving a third user input confirming the recommendation for the new target user to use the particular assistant user for completing the new task,

wherein matching the updated assistant profile with the new target user profile associated with the new target user and the new task is based on receiving the third user input confirming the recommendation.