Abstract: A method can include generating a user interface portion for a user device. The user interface portion can be for one or more used payment methods for an order payment by a user. To generate the user interface portion, the method can include: (a) upon determining that a payment method count is greater than one, (i) determining a current payment method based on the one or more used payment methods and a payment-method-sequence rule; and (ii) generating a payment-method-selection area of the user interface portion; and (b) upon determining that the payment method count is not greater than one, using the one or more used payment methods as the current payment method. The method further can include generating a ledger area of the user interface portion for the current payment method to display information associated with the current payment method and the order payment.

Abstract: A pet medical text recognizer may include one or more machine learning classifiers. The one or more machine learning classifiers may be trained using training data to associate raw text with pet clinical event codes. A performance metric may be provided, and the highest performing classifier according to the performance metric may be selected as the model for the pet medical text recognizer. The pet medical text recognizer may accept input text from a veterinary practice management system and generate a pet clinical event code for the text. A set of codes associated with a single pet may be aggregated into a pet health record.

Abstract: A pet medical text recognizer may include one or more machine learning classifiers. The one or more machine learning classifiers may be trained using training data to associate raw text with pet clinical event codes. A performance metric may be provided, and the highest performing classifier according to the performance metric may be selected as the model for the pet medical text recognizer. The pet medical text recognizer may accept input text from a veterinary practice management system and generate a pet clinical event code for the text. A set of codes associated with a single pet may be aggregated into a pet health record.

Abstract: A pet medical text recognizer may include one or more machine learning classifiers. The one or more machine learning classifiers may be trained using training data to associate raw text with pet clinical event codes. A performance metric may be provided, and the highest performing classifier according to the performance metric may be selected as the model for the pet medical text recognizer. The pet medical text recognizer may accept input text from a veterinary practice management system and generate a pet clinical event code for the text. A set of codes associated with a single pet may be aggregated into a pet health record.