# US 12,009,091 B2 · Pharmacy SIG codes auto-populating system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,009,091 B2 - Document ID: US12009091B2 - Status: Granted - Kind: B2 - Application number: 16/865,112 - Filed: 2020-05-01 - Published: 2020-11-05 - Granted: 2024-06-11 - Earliest claimed: 2019-05-03 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Ravi Shankar Karedla; Jingying Zhang; Paridhi Kabra; Shilka Roy - Web page: https://patentgrep.com/patent/US12009091B2 ## CPC - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - G06F 40/174: Form filling; Merging - G06F 40/205: Parsing - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06N 5/025: Extracting rules from data - G06N 20/00: Machine learning - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H 70/20: relating to practices or guidelines - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems, methods, and computer-readable media are configured to auto-populate pharmacy SIG codes based on pattern matching model in a pharmacy system. The prescriptions including one or more fields of prescription information in natural language are received in the system. The prescription information can be parsed according to rule-based pattern matching model. The parsed prescription information can be mapped to a token SIG using rule-based pattern matching model dictionaries. A prescription SIG code based on each of the token SIG can be generated. A confidence value of the prescription SIG code based on character coverage of the prescription can be calculated. ## Claims ### Claim 1 A method of auto-populating pharmacy SIG codes in a pharmacy system, the method comprising: receiving an electronic prescription including one or more fields of prescription information in natural language; parsing, by a processing device, the one or more fields of prescription information according to rule-based pattern matching model to identify one or more tokens; mapping, by the processing device, each of the identified one or more tokens to a token SIG corresponding to each of the identified one or more tokens using rule-based pattern matching model dictionaries; combining, by the processing device, each token SIG to generate a prescription SIG code, wherein the prescription SIG code is separate from the electronic prescription; and autonomously auto-populating a graphical user interface associated with the prescription information with the prescription SIG code; calculating, by the processing device, a confidence value of the prescription SIG code based on character coverage of the electronic prescription, and outputting, by the processing device, the confidence value associated with the prescription SIG code, wherein the confidence value is calculated as a function of a number of characters mapped by the rule-based pattern matching model dictionaries and a number of characters present in the electronic prescription. ### Claim 2 (depends on 1) The method of claim 1, wherein the confidence value indicates an action to be taken with the prescription SIG code by a healthcare professional. ### Claim 3 (depends on 1) The method of claim 1, wherein the confidence value is calculated as: Confidence ⁢ Value = Total ⁢ number ⁢ of ⁢ characters ⁢ mapped by ⁢ the ⁢ rule - based ⁢ pattern ⁢ matching ⁢ model ⁢ dictionaries Total ⁢ number ⁢ of ⁢ characters ⁢ present in ⁢ the ⁢ electronic ⁢ prescription × 100 ⁢ % ### Claim 4 (depends on 1) The method of claim 1, wherein the one or more fields of prescription information includes dosage, frequency, add-on, route, duration, verb, and medical conditions. ### Claim 5 (depends on 1) The method of claim 1, wherein the rule-based pattern matching model is a regular expressions pattern matching model. ### Claim 6 (depends on 5) The method of claim 5, further comprising: training the regular expressions pattern matching model using a set of training data. ### Claim 7 (depends on 1) The method of claim 1, further comprising: creating dictionaries for each of the one or more fields of prescription information, and updating the dictionaries based on the prescription SIG code. ### Claim 8 A non-transitory computer-readable medium storing instructions, wherein execution of the instructions by a processing device causes the processing device to implement a method of auto-populating pharmacy SIG codes in a pharmacy system, the method comprising: receiving an electronic prescription including one or more fields of prescription information in natural language; parsing, by the processing device, the one or more fields of prescription information according to rule-based pattern matching model to identify one or more tokens; mapping, by the processing device, each of the identified one or more tokens using rule-based pattern matching model dictionaries to a token SIG corresponding to each of the identified one or more tokens; combining, by the processing device, each token SIG to generate a prescription SIG code, wherein the prescription SIG code is separate from the electronic prescription; and autonomously, by the processing device, auto-populating a graphical user interface associated with the prescription information with the prescription SIG code; calculating, by the processing device, a confidence value of the prescription SIG code based on character coverage of the electronic prescription, and outputting, by the processing device, the confidence value associated with the prescription SIG code, wherein the confidence value is calculated as a function of a number of characters mapped by the rule-based pattern matching model dictionaries and a number of characters present in the electronic prescription. ### Claim 9 (depends on 8) The medium of claim 8, wherein the confidence value indicates an action to be taken with the prescription SIG code by a healthcare professional. ### Claim 10 (depends on 8) The medium of claim 8, wherein the confidence value is calculated as: Confidence ⁢ Score = Total ⁢ number ⁢ of ⁢ characters ⁢ matched by ⁢ the ⁢ rule - based ⁢ pattern ⁢ matching ⁢ model ⁢ dictionaries Total ⁢ number ⁢ of ⁢ characters ⁢ present in ⁢ the ⁢ electronic ⁢ prescription × 100 ### Claim 11 (depends on 8) The medium of claim 8, wherein the one or more fields of prescription information includes dosage, frequency, add-on, route, duration, verb, and medical conditions. ### Claim 12 (depends on 8) The medium of claim 8, wherein the rule-based pattern matching model is a regular expressions pattern matching model. ### Claim 13 (depends on 12) The medium of claim 12, wherein execution of the instructions by the processing device causes the processing device to: train the regular expressions pattern matching model using a set of training data. ### Claim 14 (depends on 8) The medium of claim 8, wherein execution of the instructions by the processing device causes the processing device to: create dictionaries for each of the one or more fields of prescription information, and update the dictionaries based on the prescription SIG code. ### Claim 15 A system for auto-populating pharmacy SIG codes in a pharmacy system, the system comprising: a computer storage device storing rule-based pattern matching model; and a processing device programmed to execute the rule-based pattern matching model to: receive an electronic prescription including one or more fields of prescription information in natural language; parse, by the processing device, the one or more fields of prescription information according to the rule-based pattern matching model to identify one or more tokens; map, by the processing device, each of the identified one or more tokens using rule-based pattern matching model dictionaries to a token SIG corresponding to each of the identified one or more tokens; combine, by the processing device, each token SIG to generate a prescription SIG code, wherein the prescription SIG code is separate from the electronic prescription; and autonomously auto-populate, a graphical user interface associated with the prescription information with the prescription SIG code; calculate, by the processing device, a confidence value of the prescription SIG code based on character coverage of the electronic prescription, and output, by the processing device, the confidence value associated with the prescription SIG code, wherein the confidence value is calculated as a function of a number of characters mapped by the rule-based pattern matching model dictionaries and a number of characters present in the electronic prescription. ### Claim 16 (depends on 15) The system of claim 15, wherein the confidence value indicates an action to be taken with the prescription SIG code by a healthcare professional. ### Claim 17 (depends on 15) The system of claim 15, wherein the confidence value is calculated as: Confidence ⁢ Score = Total ⁢ number ⁢ of ⁢ characters ⁢ matched by ⁢ the ⁢ rule - based ⁢ pattern ⁢ matching ⁢ model ⁢ dictionaries Total ⁢ number ⁢ of ⁢ characters ⁢ present in ⁢ the ⁢ electronic ⁢ prescription × 100 ### Claim 18 (depends on 15) The system of claim 15, wherein the one or more fields of prescription information includes dosage, frequency, add-on, route, duration, verb, and medical conditions. ### Claim 19 (depends on 15) The system of claim 15, wherein the rule-based pattern matching model is a regular expressions pattern matching model. ### Claim 20 (depends on 19) The system of claim 19, wherein the processing device is programmed to execute the rule-based pattern matching model to: train the regular expressions pattern matching model using a set of training data. ### Claim 21 (depends on 15) The system of claim 15, wherein the processing device is programmed to execute the rule-based pattern matching model to: create dictionaries for each of the one or more fields of prescription information, and update the dictionaries based on the prescription SIG code.