Abstract: A method for a system. The method may include receiving information regarding a plurality of nodes of the transportation network, a map associated with the transportation network, and a current demand associated with multiple nodes in the transportation network. The method may further include generating, based on the received information, a demand grid related to the transportation network. The method may also include determining a set of service zones based on the received information and the generated demand grid. The method may further include generating, based on the received information and the set of service zones, a model for route optimization. The method may also include determining, based on the model, at least one candidate path for a vehicle associated with the on-demand transportation service.

Abstract: A method of providing optimal and personalized business decision making that leverages behavioral economics principles and machine learning techniques is discussed herein. The method may include collecting or simulating data relating to behavioral characteristics of a plurality of stakeholders and analyzing the collected data to construct behavioral economics and machine learning based models related to a business problem. These models can be used to optimize and personalize business interventions to influence consumers' purchasing behavior to achieve the best business outcome (in B2C use cases) and de-bias distorted information sharing in supply chains (in B2B use cases). By contrast, traditional consumer and supply chain analytics solutions lack behavioral insights and often lead to sub-optimal decision making because economic optimization approach alone is not adequate for decision making where behavioral biases are present.

Abstract: Example implementations described herein involve extracting keywords and dependency information from a text; and generating a co-occurrence dictionary for the text, the generating the co-occurrence dictionary involving selecting ones of the keywords for inclusion in the co-occurrence dictionary based on a number of times the ones of the keywords satisfy the dependency rules; determining for the selected ones of the keywords included in the co-occurrence dictionary, surrounding words to be associated with the selected ones of the keywords in the co-occurrence dictionary based on a number of instances of co-occurrence of the surrounding words with the selected ones of the keywords; and generating weights for each of the selected ones of the keywords in the co-occurrence dictionary based on a number of the surrounding words associated with the selected ones of the keywords.

Abstract: Example implementations described herein involve extracting keywords and dependency information from a text; and generating a co-occurrence dictionary for the text, the generating the co-occurrence dictionary involving selecting ones of the keywords for inclusion in the co-occurrence dictionary based on a number of times the ones of the keywords satisfy the dependency rules; determining for the selected ones of the keywords included in the co-occurrence dictionary, surrounding words to be associated with the selected ones of the keywords in the co-occurrence dictionary based on a number of instances of co-occurrence of the surrounding words with the selected ones of the keywords; and generating weights for each of the selected ones of the keywords in the co-occurrence dictionary based on a number of the surrounding words associated with the selected ones of the keywords.

Abstract: Provided is a production amount prediction system including: a storage unit which stores a production amount prediction model which is based on resources information including a resources amount obtained in a previously drilled wellbore and a resources recovery probability in the vicinity thereof; an input unit which receives a trajectory coordinate of a planned wellbore as an input; a production amount prediction unit which calculates a production amount of the planned wellbore based on the production amount prediction model by using a degree of influence of the previous wellbore on the planned wellbore as at least one parameter; and a display unit which displays the production amount of resources of the planned wellbore calculated by the production amount prediction unit.

Abstract: Provided is a production amount prediction system including: a storage unit which stores a production amount prediction model which is based on resources information including a resources amount obtained in a previously drilled wellbore and a resources recovery probability in the vicinity thereof; an input unit which receives a trajectory coordinate of a planned wellbore as an input; a production amount prediction unit which calculates a production amount of the planned wellbore based on the production amount prediction model by using a degree of influence of the previous wellbore on the planned wellbore as at least one parameter; and a display unit which displays the production amount of resources of the planned wellbore calculated by the production amount prediction unit.