KR-102963329-B1 - Booth system for patient facial recognition and prescription matching

Abstract

The present invention relates to a customer identity verification and prescription data management system utilizing facial recognition data, and more specifically, to a technology that improves the efficiency and safety of medical services by accurately verifying a customer's identity based on facial data and integrally generating, verifying, predicting, and managing the customer's prescription data.

Inventors

• 이경철
• 도준호
• 강종석

Assignees

• 주식회사 올댓페이

Dates

Publication Date

20260512

Application Date

20250828

Claims (2)

1. It includes a data collection unit, a data generation unit, a customer verification unit, a data verification unit, a statistical analysis unit, a predictive analysis unit, a notification warning unit, and a facial recognition booth, and The above data collection unit converts the facial data collected from the above facial recognition booth into vector values and stores them, and The above data generation unit analyzes a prescription submitted by a customer to generate and store prescription data including customer information, matches facial data transmitted from the above facial recognition booth to integrate with customer identification information, and generates and manages detailed data of the drug name, dosage, and time of administration of the prescription data. The above customer verification unit re-recognizes the facial data of the customer called after the completion of medication preparation, compares it with existing data to verify identity, and if it matches the existing facial data, proceeds with medication guidance and payment. The above data verification unit analyzes prescription data through natural language processing, detects and corrects errors, extracts prescription text using OCR, and analyzes the drug name, dosage, and time of administration. The above statistical analysis unit generates statistical information based on collected prescription data and customer information, and generates reports usable by hospitals and pharmacies to support data-driven decision-making. The above-mentioned predictive analysis unit predicts medication patterns and drug consumption based on customer data and prescription data, calculates drug consumption by analyzing the customer's health status and medication history, and The above notification and warning unit detects the possibility of medication errors or provides notifications and warnings when medication time is missed, and The above facial recognition booth is, Equipped with a space and structure for facial recognition, A booth entrance is formed open at the front, and a booth space is provided inside that allows people to enter and exit and forms a standing space for facial recognition. A booth system for patient facial recognition and prescription matching, comprising an upper ring-shaped rail, a lower ring-shaped rail, a module vertical frame, a module lifting/lowering gear, a gear shaft, a circular moving part, an articulated frame, a facial scanner, and a scanner housing.
2. delete

Description

Booth system for patient facial recognition and prescription matching The present invention relates to a customer identity verification and prescription data management system utilizing facial recognition data, and more specifically, to a technology that improves the efficiency and safety of medical services by accurately verifying a customer's identity based on facial data and integrally generating, verifying, predicting, and managing the customer's prescription data. Currently, pharmacies and hospitals often perform manual entry and verification tasks to manage prescription data and confirm customer identities. This not only slows down data processing speed but also causes the following problems: There is a possibility of medical accidents occurring due to incorrect entry of drug names or medication information. There is a risk of incorrect medication guidance due to confusion of prescriptions among customers. Failure to systematically manage data on individual customer medication history and drug consumption may lead to drug inventory shortages or excesses. Some existing facial recognition technologies merely perform customer identification and fail to provide additional functions such as data processing, verification, and predictive analytics. Furthermore, technologies for automatically analyzing complex prescription data and detecting errors in medical data management systems have not yet been sufficiently commercialized. Figure 1 illustrates an overall relationship diagram according to the present invention. FIGS. 2 to 5 illustrate the configurations of a facial recognition booth according to the present invention. Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention. Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another. Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a basic artificial intelligence model is trained using a number of training data by a learning algorithm, thereby creating predefined rules of operation or artificial intelligence models configured to perform desired characteristics (or objectives). Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above. An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple nodes and weight values, and performs neural network operations through calculations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be stabilized by the learning results of the artificial intelligence model. For example, multiple weights may be updated so that the loss value or cost value obtained by the artificial intelligence model during the learning process is reduced or minimized. Additionally, to minimize the loss value or cost value, multiple weights may be updated in a direction that mini