Anna M Bailey

US Patent:

20220343188, Oct 27, 2022

Filed:

Jun 28, 2022

Appl. No.:

17/852285

Inventors:

- San Francisco CA, US
David J. Klein - Los Altos CA, US
Silas M. Toms - San Francisco CA, US
Han-En Eric Kung - Livermore CA, US
Anna C.J. Bailey - San Francisco CA, US

International Classification:

G06N 5/02

Abstract:

Techniques are described herein for predicting content exposure that will result from installing a panel at a location at which no panel is currently installed. The location may include at least one electric vehicle charging station (EVCS) that includes an integrated or external panel for displaying content. The techniques involve training a machine learning engine based on information obtained about locations at which panels are already installed. The information used to train the machine learning engine includes, for each existing installation location: (a) features of the location, and (b) exposure data that has been generated for the location. When the machine learning engine has been trained, the trained machine learning engine predicts the content exposure for a location at which no panel has been installed based on the features of that location.

US Patent:

20220332209, Oct 20, 2022

Filed:

Apr 20, 2022

Appl. No.:

17/725464

Inventors:

- San Francisco CA, US
Anna C.J. Bailey - San Francisco CA, US
Praveen K. Mandal - San Francisco CA, US
Han-En Eric Kung - Livermore CA, US
Karen J. Zelmar - San Francisco CA, US
Scott Mercer - Pacifica CA, US

International Classification:

B60L 53/66
B60L 53/68
G06F 30/20

Abstract:

An approach is provided for estimating the optimal number and mixture of types of electric vehicle (EV) charging stations (EVCS) at one or more points of interest (POIs). A method includes generating, based on an EV adoption model, an EV adoption prediction. The method includes generating, based on a mobility simulation model, a driver-type prediction that predicts percentages of EV drivers qualifying for various EV driver types. The method includes generating, based on the EV adoption prediction, the driver-type prediction, and a visitation model, a visitation prediction that predicts how many EV drivers of each type of EV driver will visit the POI. The method includes determining and displaying, based on how many EV drivers of each type of EV driver is predicted to visit the POI, for each type of EV charging station of a plurality of types of EV charging stations, an optimal number of EVCS to install.

US Patent:

20220335545, Oct 20, 2022

Filed:

Apr 20, 2022

Appl. No.:

17/725455

Inventors:

- San Francisco CA, US
Anna C.J. Bailey - San Francisco CA, US
Praveen K. Mandal - San Francisco CA, US
Han-En Eric Kung - Livermore CA, US

International Classification:

G06Q 50/06
G06Q 30/02

Abstract:

An approach is provided for estimating electric vehicle (EV) charge needs among a population in a particular region. A method includes obtaining region-specific information associated with the particular region; wherein the region-specific information includes a number of EV drivers in the particular region. The method includes generating needs prediction data that indicates the EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region; wherein the mobility simulation comprises a plurality of probability distribution functions. The method includes generating, based on the needs prediction data, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

US Patent:

20220335546, Oct 20, 2022

Filed:

Apr 20, 2022

Appl. No.:

17/725461

Inventors:

- San Francisco CA, US
Anna C.J. Bailey - San Francisco CA, US
Praveen K. Mandal - San Francisco CA, US
Han-En Eric Kung - Livermore CA, US
Ionel-Alexandru Hosu Hosu - Bucharest, RO
Silas M. Toms - San Francisco CA, US
Scott Mercer - San Francisco CA, US

International Classification:

G06Q 50/06
G06Q 10/06
G06Q 30/02

Abstract:

An approach is provided for estimating electric vehicle (EV) charging station demand at specifics points of interest. A method includes determining a percentage of visitors to a point of interest (POI) that are electric vehicle (EV) drivers, a first percentage of the EV drivers qualifying as essential drivers, a second percentage of the EV drivers qualifying as opportunistic drivers. The method includes feeding input data to an inference engine, wherein the input data includes the above percentages, a number of visitors to the POI, and charge rates for the opportunistic and essential drivers. The inference engine generates output data regarding predicted EV charging demand for the POI, and generates a display based on the output data.

US Patent:

20230055326, Feb 23, 2023

Filed:

Aug 19, 2022

Appl. No.:

17/891885

Inventors:

- San Francisco CA, US
David J. Klein - Los Altos CA, US
Anna Bailey - San Francisco CA, US
Brian Bowen - San Francisco CA, US
Silas M. Toms - San Francisco CA, US

International Classification:

G06Q 30/06
G06K 9/62

Abstract:

An approach is provided for generating an electric vehicle score (EVScore), a rating scale representing, for a user, a future estimated ease of ownership and operation of an EV within a defined geographic region. The method includes receiving a plurality of regional engine inputs pertaining to a defined geographic region, and one or more user inputs pertaining to the user. The method also includes processing, via a predictive model, the plurality of regional engine inputs and the one or more user inputs to generate one or more intermediate scores for the defined geographic region. The method also includes receiving a plurality of projected inputs pertaining to projected ownership and operation costs and benefits of electric vehicles (EVs) for the defined geographic region. The method also includes processing, via a trained machine learning model, the plurality of projected inputs and the one or more intermediate scores to generate the EVS core.

US Patent:

20230038368, Feb 9, 2023

Filed:

Aug 5, 2022

Appl. No.:

17/882481

Inventors:

- San Francisco CA, US
David J. Klein - San Francisco CA, US
Anna Bailey - San Francisco CA, US
Brian Bowen - San Francisco CA, US
Silas M. Toms - San Francisco CA, US
Praveen Mandal - San Francisco CA, US

International Classification:

B60L 53/63
B60L 53/67
B60L 53/64
B60L 53/30

Abstract:

Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

US Patent:

20230045381, Feb 9, 2023

Filed:

Sep 15, 2022

Appl. No.:

17/932631

Inventors:

- San Francisco CA, US
David J. Klein - San Francisco CA, US
Anna Bailey - San Francisco CA, US
Brian Bowen - San Francisco CA, US
Silas M. Toms - San Francisco CA, US
Praveen Mandal - San Francisco CA, US

International Classification:

B60L 53/63
B60L 53/30
B60L 53/67
B60L 53/64

Abstract:

Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.