Abstract: This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

Abstract: This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

Abstract: This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

Abstract: An annotation corresponding to a first range of text of a first document may be received. Based on the annotation, comparisons may be performed between a text string that comprises the first range of text and a group of text of a second document at different positions in the group of text. Based on the comparisons, similarity scores between the text string and the group of text may be determined at the different positions in the group of text. A position for the annotation in the group of text may be selected based on the similarity scores at the different positions. The annotation may be associated with a second range of text in the group of text that corresponds to the position.

Abstract: An annotation corresponding to a first range of text of a first document may be received. Based on the annotation, comparisons may be performed between a text string that comprises the first range of text and a group of text of a second document at different positions in the group of text. Based on the comparisons, similarity scores between the text string and the group of text may be determined at the different positions in the group of text. A position for the annotation in the group of text may be selected based on the similarity scores at the different positions. The annotation may be associated with a second range of text in the group of text that corresponds to the position.

Abstract: An annotation corresponding to a first range of text of a first document may be received. Based on the annotation, comparisons may be performed between a text string that comprises the first range of text and a group of text of a second document at different positions in the group of text. Based on the comparisons, similarity scores between the text string and the group of text may be determined at the different positions in the group of text. A position for the annotation in the group of text may be selected based on the similarity scores at the different positions. The annotation may be associated with a second range of text in the group of text that corresponds to the position.

Abstract: This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

Abstract: Digital annotation and digital content linking techniques implemented by digital content generation system as part of a computing device are described. The system is configured to convert a digital document including an object from a first format to a second format, receiving the digital document in the second format with an annotation associated with the object, converting the digital document in the second format with the annotation associated with the object to the first format, and maintaining the association between the annotation and the object subsequent to an alteration in a page sequence of the document in the first format, or movement of the object.

Abstract: Digital annotation and digital content linking techniques implemented by digital content generation system as part of a computing device are described. The system is configured to convert a digital document including an object from a first format to a second format, receiving the digital document in the second format with an annotation associated with the object, converting the digital document in the second format with the annotation associated with the object to the first format, and maintaining the association between the annotation and the object subsequent to an alteration in a page sequence of the document in the first format, or movement of the object.

Abstract: An annotation corresponding to a first range of text of a first document may be received. Based on the annotation, comparisons may be performed between a text string that comprises the first range of text and a group of text of a second document at different positions in the group of text. Based on the comparisons, similarity scores between the text string and the group of text may be determined at the different positions in the group of text. A position for the annotation in the group of text may be selected based on the similarity scores at the different positions. The annotation may be associated with a second range of text in the group of text that corresponds to the position.