Activity level measurement using deep learning and machine learning

• Main Authors: LATHAM SETHI TEJA, RUSSELL DENNIS, KIERZKOWSKI KAROLINA, KRAMSKAIA ALLA, DAVID. ERICSON, LIU HUAN-OU
• Format: Patent
• Language: Chinese; English
• Published: 28.04.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

A method for assessing an activity level of an entity is provided. The method includes (i) receiving source data regarding a plurality of entities from a source, (ii) analyzing the source data to produce (a) a source data assessment indicating whether the source data is included in a score dataset, and (b) a calculated accuracy that is a weighted accuracy assessment of the source data, (iii) receiving entity data regarding an entity of interest, (iv) generating an entity description representing attributes of the entity of interest from the entity data and the calculated accuracy, (v) analyzing the source data assessment and the entity description to produce an activity score, the activity score being an estimate of an activity level of the entity of interest; and (vi) issuing a recommendation on processing of the entity of interest based on the activity score. 提供了用于评估实体的活动性水平的方法。该方法包括(i)从源接收关于多个实体的源数据，(ii)分析源数据以产生(a)源数据评估，该源数据评估指示是否将源数据包括在得分数据集中，以及(b)计算出的准确性，该计算出的准确性是源数据的加权准确性评估，(iii)接收关于关注的实体的实体数据，(iv)根据实体数