International Business Machines Corporation

Oracle International Corporation

Aswin Kannan

Kiran V Panchamgam

Ramasuri Narayanam

Su-Ming Wu

Mukundan Sundararajan

Krishnasuri Narayanam

Mark Steven Squillante

Yogish Sabharwal

Dinesh Garg

Vaibhav Saxena

Parikshit Ram

Balaji Ganesan

Shanmukha Chaitanya Guttula

Ashish Verma

Saurabh Manish Raje

Christian Toft-Nielsen

Jeanine C Chong

Alankar Jain

Aditya Basu

Jessica Lee Yau

Methods, systems, and computer program products for classifying data from de-identified content are provided herein. A computer-implemented method includes applying one or more rules to identify one or more structural elements of a document; determining, based at least in part on the one or more structural elements, one or more pairs of words within the document having a hypernym relationship; extracting de-identified content within the document based on one or more de-identification techniques applied to the document; and applying a set of causal rules to the de-identified content and the one or more pairs of words to annotate at a least a portion of the de-identified content as belonging to a class of protected content.

Classifying data from de-identified content

A system, computer program product, and method are presented for performing multi-objective automated machine learning, and, more specifically, to identifying a plurality of machine learning pipelines as Pareto-optimal solutions to optimize a plurality of objectives. The method includes receiving input data directed toward one or more subjects of interest and determining a plurality of objectives to be optimized. The method also includes ingesting at least a portion of the input data through one or more machine learning (ML) models. The method further includes aggregating the plurality of objectives into one or more aggregated single objectives. The method also includes determining a plurality of Pareto-optimal solutions, thereby defining a plurality of ML pipelines that optimize the one or more aggregated single objectives. The method further includes selecting one ML pipeline from the plurality of ML pipelines.

Multi-objective automated machine learning

Systems, methods, and other embodiments associated with generating a price schedule are described. In one embodiment, for each customer segment of a plurality of customer segments, a per-segment value of an approximate objective function for the customer segment is determined by an optimizer, and a ratio of the per-segment value to a sum of all per-segment values for the customer segments is computed. The inventory quantity is allocated amongst the customer segments according to the ratio for each customer segment to form an inventory quantity for each customer segment. For each customer segment, a promotion portion of the price schedule that maximizes the objective function by the optimizer is determined. A quantity of remaining inventory allocated to the plurality of customer segments at an end of the regular season is aggregated. A markdown portion of the price schedule for the item that maximizes the objective function is determined by the optimizer. The promotion portion and the markdown portion are combined to create the price schedule for the item.

Computerized promotion and markdown price scheduling

A method, computer system, and a computer program product for computing a product drag effect is provided. The present invention may include receiving a plurality of transaction record data. The present invention may then include tuning a plurality of parameters based on the received transaction record data. The present invention may further include determining a product drag frequency based on the authorized parameter tuning and received transaction record data. The present invention may then include calculating a drag probability based on the determined product drag frequency. The present invention may then include deriving an observation from the calculated drag probability. The present invention may lastly include outputting the derived observation to a user.

Extracting product drag effect from transaction records

One embodiment provides a method for optimizing schedules of a plurality of elevators in a building having a plurality of floors, including: receiving, from a user, an indication requesting that an elevator from the plurality of elevators stop at a particular floor, wherein each of the plurality of elevators has a schedule comprising floors where the elevator will stop; identifying the number of users in the elevator waiting area of each of the particular floors where an indication requesting a stop has been received; and updating, in view of the identified number of users at each of the particular floors, the schedule for the plurality of elevators to optimize the stops for the plurality of elevators, wherein the updating comprises dynamically routing the plurality of elevators upon (i) receipt of indications and (ii) identification of the number of users at each of the particular floors.

Elevator movement plan generation

Systems, methods, and other embodiments associated with generating a price schedule are described. An inventory quantity for the item is allocated amongst a plurality of customer segments based on a predicted contribution of each customer segment to the objective function. For each customer segment, based on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. Remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item. In one embodiment, a price schedule may be generated that includes promotions on top of markdown prices during the clearance season.

Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.

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Chennai, India

Aswin Kannan