Machine learning systems for remote role evaluation and methods for using same

This system uses machine learning to evaluate how suitable a job position is for remote work. It analyzes the skills and tasks required for a role, considers where an organization's employees are located, and then calculates how much physical presence is needed for each task. Based on these calculations, it assigns a remote work score to the position, and if that score is too low, the system can suggest a specific physical location for the role.

Why it matters: Filed before the widespread availability of large language models. Today, LLMs can significantly simplify the generation of job metadata, including skills and tasks, from unstructured job descriptions, and potentially assist in deriving "known proximity scores" mentioned in the claims.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedMachine learning algorithms, data processing, software logic

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