Granted Patent B2

US 12,603,090 · App. 18/530,125 · Granted Apr 14, 2026

Inventors: Angel A. Penilla (Sacramento, CA); Albert S. Penilla (Scotts Valley, CA)

Assignee: Emerging Automotive, LLC

G10L15/22B60R16/0373G01C21/3641G06V20/597G10L15/02G10L15/063G10L15/25G10L15/30G10L17/04G10L17/06G10L25/57G10L25/63H04L67/1097H04L67/12H04L67/306G06F3/013G06F3/017G10L2015/223G10L2015/227G10L2015/228G10L25/90H04L67/10

Abstract

Claims (34)

1 . A method for using an emotion of a human driver of a vehicle for processing a vehicle response, comprising,

capturing voice data from the human driver over a period of time while the human driver is associated with use of the vehicle;

processing the voice data using a machine learning algorithm to assist in prediction of the emotion of the human driver; and

generating input, responsive to the emotion predicted of the human driver, the input being for a computing system of the vehicle, the input is configured to communicate with an application-specific interface of the vehicle to control a vehicle system function of the vehicle, the input being selected based at least in part on the emotion of the human driver.

2 . The method of claim 1 , wherein the machine learning algorithm is performed by a processor of a cloud services system.

3 . The method of claim 1 , wherein the machine learning algorithm is performed by a processor associated with the vehicle.

4 . The method of claim 1 , wherein the machine learning algorithm produces a model of the human driver that includes contextual information related to interaction with the vehicle and characteristics of the captured voice data.

5 . The method of claim 1 , wherein the machine learning algorithm is customized for the human driver.

6 . The method of claim 1 , wherein the machine learning algorithm learns from a plurality of inputs that include one or more of input patterns made for the vehicle, conversation input detected, language tone, language dialect, driving history, biometrics, calendar data, vehicle preferences, learned vehicle preferences, past mood patterns, and geo-location of the vehicle.

7 . The method of claim 1 , further comprising,

capturing ambient sounds in the vehicle, the ambient sounds include a voice of at least one passenger; and

wherein the ambient sounds are processed in addition to the captured voice data from the human driver to assist in predicting emotion of the human driver.

8 . The method of claim 1 , wherein the input for a computing system of the vehicle is configured for calming or reducing distraction of the human driver.

9 . The method of claim 1 , further comprising,

using the input for the computing system to change an ambient light inside of the vehicle.

10 . The method of claim 1 , wherein the machine learning algorithm assists in updates to a model that identifies characteristics of the human driver, the machine learning algorithm uses as additional input one or more of;

physiological conditions of the human driver that include, one or more of heart rate, or body heat, or blood pressure, or skin temperature, or skin galvanic resistance, or temperature of skin, or blood rush to a face of the human driver, or eye iris or retina changes, or odor/scent changes, or combinations of two or more thereof.

11 . The method of claim 1 , wherein the machine learning algorithm assists in producing a model that identifies characteristics of the human driver, the machine learning algorithm uses as additional input one or more of;

physical conditions of the human driver that include, one or more of a change in a face of the human driver, or changes in a typing rhythm, or changes in gait, or changes in voice, or changes in voice tone, or voice inflections, or voice speed or slowness, or sharp voice harmonics, or voice identifiers or patterns, or elevated heat patterns, or palm veins, or eye iris or retina changes, heat rate changes, or eye gaze patterns or motions, or combinations of two or more thereof.

12 . The method of claim 1 , wherein the input for the computing system of the vehicle is a vehicle response;

wherein the vehicle response includes one of;

waking up the human driver with a sound or air if the emotion is a sleepy emotion, or generating warnings and/or signals to alert the human driver, or generating recommendations to calm the human driver from an angry emotion or agitated emotion, or sending a notification to a third party indicating that the human driver is tired, or generating a notification to the human driver suggesting a temperature change, or generating an automatic temperature change based on the emotion, or recommending the human driver stop driving when tired, or reducing recommendations to the human driver when the emotion is a rushed emotion, or changing lighting of the vehicle automatically, or changing temperature of the vehicle automatically, or turn up or down a volume of music, or adjust a seat position, or a combination of two or more thereof, and wherein at least one of said vehicle responses are predefined to reduce distracted driving or increase alertness of said human driver.

13 . The method of claim 1 , wherein the input for the computing system of the vehicle is a vehicle response;

wherein the vehicle response is further based on current use of the vehicle, and use of the vehicle includes one or more of geolocation of the vehicle, speed of the vehicle, direction of the vehicle, a route of the vehicle, a time of day, occupants in the vehicle, historical use of the vehicle, online data of a user accessible to the vehicle or server, or combinations of two or more thereof.

14 . The method of claim 1 , wherein a user account with a profile is provided for the human driver of the vehicle,

wherein the user account of the human driver is identified based on processing of credential data obtained from the human driver, the credential data used by electronics of the vehicle are to associated the human driver to the user account and to the profile, the electronics of the vehicle configured to interface over a network with a server for communication with the profile and associate updates to the profile as made via use of the vehicle or updates associated with learned behavior of the human driver.

15 . The method of claim 1 , wherein the voice data has features associated with a tone used to identify a tone learned from the human driver;

wherein tones learned includes one of a rushed tone, a frustrated tone, a happy tone, a slow tone, a sleepy tone, a mad tone, an urgent tone, or a normal tone.

16 . The method of claim 15 , wherein the tone is processed to identify an audio signature for the tone.

17 . The method of claim 1 , further comprising,

examining touch input received on an input surface of the vehicle, the touch input having a characteristic that is used to influence selection of the input for the computing system of the vehicle;

wherein the input is used to generate a vehicle response.

18 . The method of claim 1 , wherein a vehicle response is generated responsive to the input for the computing system of the vehicle.

19 . The method of claim 18 , wherein the vehicle response includes interfacing with one or more vehicle system functions, the interfacing includes one or more of adjusting a setting of the vehicle, providing data to a display, recommending an input for a vehicle setting, adjusting an audio volume, adjusting auto driver parameters, controlling speed settings, or a combination of two or more thereof.

Continuity (14)

References Cited (400)