Gusto. has been granted a patent for a central database system that manages human resources functions by assessing the default probability of entities. The system utilizes a machine-learned model to evaluate risk tolerance and streamline database interactions, enhancing the submission of evidence from entities. GlobalData’s report on Gusto gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Gusto, Social media analytics was a key innovation area identified from patents. Gusto's grant share as of June 2024 was 68%. Grant share is based on the ratio of number of grants to total number of patents.
Central database system for managing human resources functions
The patent US12045881B2 outlines a method and system designed to enhance database interactions through the use of a machine-learned model. This model is trained on a dataset that includes historical interactions and characteristics of entities that have previously engaged with a central database system. The primary function of the model is to determine the rate at which evidence submissions are required from these entities. If the model identifies that this rate is insufficient, it can be retrained by adjusting its weights and parameters to increase the submission rate. The characteristics of historical entities may include factors such as past failures, account age, and operational duration, which can influence the model's training and effectiveness.
Additionally, the patent describes the submission process for evidence, which can occur through a second interface created by the central database system. This interface may feature functionalities such as file uploads or third-party API access for document retrieval. The evidence required can include identity verification or documents, either directly from the entity or through third-party systems. The patent also encompasses a non-transitory computer-readable medium that contains the necessary program code to execute these processes, ensuring that the system can adapt and improve its evidence submission requirements based on ongoing interactions and data analysis.
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