The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Systems and methods are disclosed herein for monitoring a location of a client device associated with a transportation service and generating augmented reality images for display on the client device. The systems and methods use sensor data from the client device and a device localization process to monitor the location of the client device by comparing renderings of images captured by the client device to renderings of the vicinity of the pickup location. The systems and methods determine navigation instructions from the user's current location to the pickup location and select one or more augmented reality elements associated with the navigation instructions and/or landmarks along the route to the pickup location. The systems and methods instruct the client device to overlay the selected augmented reality elements on a video feed of the client device.

A method and apparatus for predicting power consumption, a device, and a readable storage medium. The method includes: acquiring a reference variable generated in a history time period; and acquiring predicted power consumption in a target time period by inputting a variable characteristic into a power consumption prediction model, the target time period and the history time period having a corresponding relationship, and the power consumption prediction model being obtained by training a sample reference variable marked with sample power consumption. In the method, the reference variable including a discrete reference variable and a continuous reference variable in the history time period is acquired, and a characteristic of the acquired reference variable is acquired; and an extracted variable characteristic is input into a variable prediction model to output the predicted power consumption in the target time period.

A method and apparatus for predicting power consumption, a device, and a readable storage medium. The method includes: acquiring a reference variable generated in a history time period; and acquiring predicted power consumption in a target time period by inputting a variable characteristic into a power consumption prediction model, the target time period and the history time period having a corresponding relationship, and the power consumption prediction model being obtained by training a sample reference variable marked with sample power consumption. In the method, the reference variable including a discrete reference variable and a continuous reference variable in the history time period is acquired, and a characteristic of the acquired reference variable is acquired; and an extracted variable characteristic is input into a variable prediction model to output the predicted power consumption in the target time period.

Provided is a patent transaction prediction method, comprising the following steps: obtaining to-be-predicted patent data(S1); constructing a prediction model, which is executed by a computer to predict a transaction probability of to-be-predicted patent data(S2); and displaying the transaction probability in a data attribute of the to-be-predicted patent data(S3). According to the method, the probability of patent transactions is displayed in the attribute of the patent data, improving the probability of patent transaction. Also provided are a patent transaction prediction system and a patent transaction platform, which also have the above-mentioned advantages.

An equilibrium state calculation apparatus according to an embodiment is an equilibrium state calculation apparatus for calculating an equilibrium state of a congestion game. The equilibrium state calculation apparatus includes an input unit input with graph information representing a set of strategies represented by a combination of items, in a zero-suppressed binary decision diagram, the strategies used by a player of the congestion game, and a calculation unit that calculates, by using the graph information input through the input unit, equilibrium state information including a proportion of players selecting the strategies in the equilibrium state by a variant of the Frank-Wolfe algorithm.

A required time prediction apparatus (2000) acquires utilization facility information and assumed date and time information. The utilization facility information is information enabling to determine a departure facility and a destination facility. The assumed date and time information is information enabling to determine an assumed departure date and time assuming departure from the departure facility or an assumed arrival date and time assuming arrival at the destination facility. The apparatus (2000) estimates a waiting time at each facility being present between the departure facility and the destination facility, regarding a case of assuming departure from the departure facility at the assumed departure date and time, or a case of assuming arrival at the destination facility at the assumed arrival date and time. Then, the apparatus (2000) computes, based on the estimated waiting time, a predicted required time from departure from the departure facility to arrival at the destination facility.

A required time prediction apparatus (2000) acquires utilization facility information and assumed date and time information. The utilization facility information is information enabling to determine a departure facility and a destination facility. The assumed date and time information is information enabling to determine an assumed departure date and time assuming departure from the departure facility or an assumed arrival date and time assuming arrival at the destination facility. The apparatus (2000) estimates a waiting time at each facility being present between the departure facility and the destination facility, regarding a case of assuming departure from the departure facility at the assumed departure date and time, or a case of assuming arrival at the destination facility at the assumed arrival date and time. Then, the apparatus (2000) computes, based on the estimated waiting time, a predicted required time from departure from the departure facility to arrival at the destination facility.

An estimation program according to the application concerned causes a computer to execute an obtaining step and an estimating step. The obtaining step includes obtaining behavior information which indicates the behavior exhibited by a specific fish species having a predetermined physical abnormality. The estimating step includes estimating, based on the behavior information obtained at the obtaining step and based on state information indicating the state of the target fish for processing, behavioral features of the target fish for processing that are attributable to the predetermined physical abnormality seen in the target fish for processing.