Systems and methods for automated aircraft taxiing
US-2024144836-A1 · May 2, 2024 · US
US12506540B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12506540-B2 |
| Application number | US-202318176233-A |
| Country | US |
| Kind code | B2 |
| Filing date | Feb 28, 2023 |
| Priority date | Feb 28, 2023 |
| Publication date | Dec 23, 2025 |
| Grant date | Dec 23, 2025 |
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An online concierge system for establishing a communication session between devices using a light signal. A first client device captures video data depicting a light emitter of another client device. The first client device detects a light signal transmitted by the light emitter in the video data. The first client device extracts a handshake identifier from the light signal by decoding the light signal. A machine learning model may be used to translate the light signal into a numerical or an alphanumerical identifier. The first client device established a communication session with the other client device by transmitting a request to establish the communication session via an online concierge system. The request contains the extracted handshake identifier.
Opening claim text (preview).
What is claimed is: 1 . A method comprising, at a computer system comprising a processor and a computer-readable medium: capturing, by a camera of a first smartphone associated with a first user, video data depicting a light emitter of a second smartphone associated with a second user; detecting, in the video data by the processor, a light signal transmitted by the light emitter of the second smartphone, wherein detecting the light signal comprises applying a machine learning model to the captured video data, wherein the machine learning model is trained to detect a light signal depicted in video data; extracting, by the processor, a handshake identifier from the light signal by decoding the light signal; establishing, by the processor, a communication session with the second smartphone by transmitting a request to establish the communication session to an online system, wherein the request comprises the handshake identifier; receiving, through a user interface of the first smartphone, a text input by the first user, the text input describing a message from the first user to the second user; and transmitting, by the first smartphone, the text input to second smartphone via the communication session, wherein transmitting the text input to the second smartphone via the communication session causes the second smartphone to display the text input to the second user. 2 . The method of claim 1 , wherein capturing the video data comprises: receiving a user request via a client application operating on the first smartphone; and causing the camera of the first smartphone to capture the video data in response to receiving the user request. 3 . The method of claim 1 , wherein capturing the video data comprises: displaying the captured video data through on a display of the first smartphone; and displaying a reticule over the captured video data. 4 . The method of claim 3 , wherein detecting the light signal in the video data comprises: detecting the light signal within a portion of the video data within the reticule. 5 . The method of claim 1 , wherein extracting the handshake identifier from the light signal comprises: applying a machine learning model to the captured video data, wherein the machine learning model is trained to extract an encoded handshake identifier based on a light signal depicted in video data. 6 . The method of claim 1 , wherein decoding the light signal comprises: applying a decoding algorithm to the light signal that corresponds to an encoding algorithm used to encode the handshake identifier. 7 . The method of claim 6 , wherein the decoding algorithm is a Morse code decoding algorithm. 8 . The method of claim 1 , wherein establishing the communication session further comprises: transmitting the extracted handshake identifier to the online system. 9 . The method of claim 1 , wherein establishing the communication session comprises: initiating the communication session via a cloud service provider or via peer-to-peer networking. 10 . The method of claim 1 , wherein the first smartphone is associated with a picker and the second smartphone is associated with a customer, and wherein establishing the communication session comprises: sending, by the first smartphone, a message containing a confirmation of a delivery of an order by the picker to the customer. 11 . The method of claim 1 , wherein establishing the communication session further comprises: receiving a session confirmation message from the online system; and establishing the communication session based on the received session confirmation message. 12 . A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processor, cause the processor to: capture, by a camera of a first smartphone associated with a first user, video data depicting a light emitter of a second smartphone associated with a second user; detect, in the video data, a light signal transmitted by the light emitter of the second smartphone, wherein detecting the light signal comprises applying a machine learning model to the captured video data, wherein the machine learning model is trained to detect a light signal depicted in video data; extract a handshake identifier from the light signal by decoding the light signal; establish a communication session with the second smartphone other client device by transmitting a request to establish the communication session to an online system, wherein the request comprises the handshake identifier; receive, through a user interface of the first smartphone, a text input by the first user, the text input describing a message from the first user to the second user; and transmit, by the first smartphone, the text input to second smartphone via the communication session, wherein transmitting the text input to the second smartphone via the communication session causes the second smartphone to display the text input to the second user. 13 . The non-transitory computer-readable medium of claim 12 , wherein the instructions for capturing the video data comprise instructions that, when executed by the processor, cause the processor to: receive a user request via a client application operating on the first smartphone; and cause the camera of the first smartphone to capture the video data in response to receiving the user request. 14 . The non-transitory computer-readable medium of claim 12 , wherein the instructions for capturing the video data comprise instructions that, when executed by the processor, cause the processor to: display the captured video data through on a display of the first smartphone; and display a reticule over the captured video data. 15 . The non-transitory computer-readable medium of claim 12 , wherein the instructions for extracting the handshake identifier from the light signal comprise instructions that, when executed by the processor, cause the processor to: apply a machine learning model to the captured video data, wherein the machine learning model is trained to extract an encoded handshake identifier based on a light signal depicted in video data. 16 . The non-transitory computer-readable medium of claim 12 , wherein the instructions for decoding the light signal comprise instructions that, when executed, cause the processor to: apply a decoding algorithm to the light signal that corresponds to an encoding algorithm used to encode the handshake identifier. 17 . A system comprising: at least one processor; at least one non-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: capture, by a camera of a first smartphone associated with a first user, video data depicting a light emitter of a second smartphone associated with a second user; detect, in the video data, a light signal transmitted by the light emitter of the second smartphone, wherein detecting the light signal comprises applying a machine learning model to the captured video data, wherein the machine learning model is trained to detect a light signal depicted in video data; extract a handshake identifier from the light signal by decoding the light signal; establish a communication session with the second smartphone by transmitting a request to establish the communication session to an online system, wherein the request comprises the handshake identifier; receive, through a user interface of the first smartphone, a text input by the first user, the text input describing a message from the first user
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