IP Library Granted Patent US 12,451,133
Granted Patent B2
US 12,451,133 · App. 18/125,443 · Granted Oct 21, 2025

Voice-based menu personalization

Inventors: Jodessiah Sumpter (Alpharetta, GA); Christian McDaniel (Atlanta, GA); Kendall Marie Rose (Canton, MI); Shaundell D. Thompson (Loganville, GA)
Assignee: NCR Voyix Corporation
G10L15/22G10L15/26G06Q30/0635G06Q50/12G10L2015/227
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Quick Facts
Patent No.
US 12,451,133
App. No.
18/125,443
Granted
Oct 21, 2025
Kind
B2
Abstract

A natural-language voice chatbot engages a consumer in a voice dialogue. The chatbot is customized for engaging the specific consumer based on features and characteristics of that specific consumer's speech and a lexicon associated with terms, words, and commands for item ordering. The consumer can perform voice queries for specific items and/or specific establishments for placing a pre-staged order with the chatbot. Once the consumer selects options with a specific establishment, a pre-staged order is provided to the corresponding establishment on behalf of the user. Location data for a consumer-operated device is monitored and when it is determined that the consumer will arrive at the establishment within a time period required by the establishment to prepare the pre-staged order, a message is sent to the establishment to begin preparing the pre-staged order.

Claims (20)

1. A method, comprising:

engaging a consumer in a natural language voice dialogue to place an order at an establishment using voice characteristics specific to the consumer and a lexicon specific to ordering with the establishment;

translating speech of the consumer into text based on the voice characteristics that are specific to the consumer and the lexicon associated with ordering, wherein the translating utilizes a machine learning algorithm that receives as input the voice characteristics, an audio snippet of consumer speech, and the lexicon, and provides as output the text that corresponds to the speech;

customizing voice interaction using deep learning techniques by performing an initial voice training session to capture voice features and characteristics for the consumer;

wherein during the initial voice training session:

displaying text sentences on a display device;

receiving audio snippets of the speech from the consumer in response to prompts to the consumer to repeat the text sentences; and

training the machine learning algorithm to return voice features of the consumer, wherein corresponding features are retained in a voice profile specific to the consumer; and

training a non-user specific machine learning algorithm to utilize the voice profile to enable text translation for the consumer;

placing the order within an order system associated with the establishment using menu options obtained from the consumer during the engaging; and

sending a message to a fulfillment terminal of the establishment to begin order preparation when location data associated with a consumer device of the consumer indicates that the consumer is in route to the establishment to pick up the order and a calculated time in which the consumer is estimated to arrive at the establishment, wherein the calculated time corresponds with an order preparation time for the establishment to complete the order.

2. The method of claim 1 further comprising, sending a second message to the fulfillment terminal when the location data indicates the consumer has arrived at the establishment.

3. The method of claim 1 , wherein engaging further includes obtaining a voice profile for the consumer and identifying the voice characteristics from the voice profile.

4. The method of claim 3 , wherein engaging further includes obtaining the audio snippet from the voice profile.

5. The method of claim 4 , wherein engaging further includes obtaining the lexicon based on an establishment identifier associated with the establishment.

6. The method of claim 1 further comprising, updating the voice characteristics of the consumer based on the speech provided by the consumer during the engaging.

7. The method of claim 1 , wherein engaging further includes identifying the consumer based on a device identifier for a consumer device, wherein the speech of the consumer during the engaging is provided through a microphone of the consumer device.

8. The method of claim 7 , wherein engaging further includes obtaining the voice characteristics for the consumer from the consumer device.

9. The method of claim 1 , wherein engaging further includes communicating auto-generated speech at a start of the engaging to the consumer through a speaker of a consumer device, wherein the auto-generated speech communicates a list of available establishments.

10. The method of claim 9 , wherein engaging further includes identifying the establishment based on speech of the consumer responsive to the communicating of the auto-generated speech.

Assignments (3)
CHANGE OF NAME Recorded Nov 9, 2023
From: NCR CORPORATION
To: NCR VOYIX CORPORATION
Reel/Frame 065532/0893 →
SECURITY INTEREST Recorded Oct 25, 2023
From: NCR VOYIX CORPORATION
To: BANK OF AMERICA, N.A., AS ADMINISTRATIVE AGENT
Reel/Frame 065346/0168 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 30, 2023
From: SUMPTER, JODESSIAH; MCDANIEL, CHRISTIAN; ROSE, KENDALL MARIE; THOMPSON, SHAUNDELL D.
To: NCR CORPORATION
Reel/Frame 063166/0532 →
Continuity (2)
Continuation 17105159 · Nov 25, 2020
Related Publication 20230230590A1 · Jul 20, 2023
References Cited (22)
US 6212498B1 · Sherwood · 2001 [cited by examiner]
US 9741337B1 · Shastry · 2017 [cited by examiner]
US 10977606B1 · Mimassi · 2021 [cited by examiner]
US 20030120493A1 · Gupta · 2003 [cited by applicant]
US 20110295603A1 · Meisel · 2011 [cited by examiner]
US 20140214465A1 · L'heureux et al. · 2014 [cited by applicant]
US 20140236598A1 · Fructuoso · 2014 [cited by examiner]
US 20140370167A1 · Garden · 2014 [cited by applicant]
US 20160247113A1 · Rademaker · 2016 [cited by applicant]
US 20170124670A1 · Becker et al. · 2017 [cited by applicant]
US 20170345105A1 · Isaacson · 2017 [cited by examiner]
US 20180336904A1 · Piercy et al. · 2018 [cited by applicant]
US 20190049988A1 · Meij · 2019 [cited by applicant]
US 20190378495A1 · Kim · 2019 [cited by examiner]
US 20200143797A1 · Manoharan et al. · 2020 [cited by applicant]
US 20200311804A1 · Buckholdt · 2020 [cited by examiner]
US 20200311840A1 · Zuckerman · 2020 [cited by applicant]
US 20200395008A1 · Cohen · 2020 [cited by examiner]
US 20220165262A1 · Sumpter et al. · 2022 [cited by applicant]
US 20220327641A1 · Fox · 2022 [cited by examiner]
EP Examination Report Oct. 27, 2023. [cited by applicant]
Shah Khushbu: “New Domino's App Uses Voice Recognition to Let You Order Pizza—Eater”,Jun. 17, 2014 (Jun. 17, 2014), pp. 1-3, XP093091507, Retrieved from the Internet: URL:https://www.eater.com/2014/6/17/6206185/new-domi… [cited by applicant]