Patent application EP20178471A (EP), filed 2020-06-05
Publication number: EP3748936A1
Title: CABIN EXPERIENCE NETWORK WITH A SENSOR PROCESSING UNIT
Abstract: [0001]    A computer-implemented method of processing raw sensor data in an Aviation Internet of Things (AloT) system is provided. The computer-implemented method comprises obtaining, by a network interface of a sensor processing unit (SPU), raw sensor data from one or more systems of a vehicle; providing, by the network interface, the raw sensor data to one or more processing units of the SPU; performing, by the one or more processing units, one or more processing tasks on the raw sensor data to produce processed data; and providing, by the network interface, the processed data to the one or more systems.
IPC codes: G06F  15/    17            A I
H04L  29/    08            A I
H04L  67/    12            A I
Claims: number: 1
text: A computer-implemented method of processing raw sensor data, for example in an Aviation Internet of Things system (400), the computer-implemented method comprising:
number: 2
text: The computer-implemented method of claim 1, wherein the one or more systems comprise one or more displays (133, 137), one or more storage devices (164, 188, 194), one or more projector devices (114, 202, 204, 206, 208, 216, 218, 220, 224, 232, 234, 236, 238, 240, 248, 256, 258, 260, 262 264, 270, 272, 280), or one or more off-board systems (120, 126, 130, 138, 146).
number: 3
text: The computer-implemented method of any of claims 1 or 2, wherein the sensor processing unit (201) comprises six parallel multi-core computers (304, 306, 308, 310, 312, 314) that are dedicated to processor the raw sensor data.
number: 4
text: The computer-implemented method of claim 3, wherein one of the six parallel multi-core computer (304, 306, 308, 310, 312, 314) is trained using a machine-learning algorithm, optionally a trained neural network.
number: 5
text: The computer-implemented method of claim 4, wherein the machine-learning algorithm is trained during one or more stages in aircraft flight operations to detect and classify a detected vibration or a detected sound.
number: 6
text: The computer-implemented method of claim 5, wherein a location of the detected vibration or the detected sound is determined based on the machine-learning algorithm.
number: 7
text: The computer-implemented method of any of claims 1-6, wherein the sensor processing unit (201) is in data communication with a switch of a Cabin Experience Network system.
number: 8
text: The computer-implemented method of claim 7 when dependent upon claim 5 or 6, wherein an indication of the detected vibration or the detected sound is provided to the sensor processing unit (201) via the switch of a Cabin Experience Network system.
number: 9
text: The computer-implemented method of any of claims 1-8, wherein:
number: 10
text: A computer system comprising:
number: 11
text: A Cabin Experience Network system comprising:
number: 12
text: The Cabin Experience Network system of claim 11, further comprising:
a second Cabin Experience Network switch (158) in communication with the Cabin Experience Network server (102), wherein the second Cabin Experience Network switch (158) is in communication with at least one of one or more second projector devices (204) and one or more second cameras (212), wherein the sensor processing unit (201) in communication with the second Cabin Experience Network switch (158), wherein the sensor processing unit (201) obtains raw sensor data from the second Cabin Experience Network switch (158) and processes the raw sensor data that is provided to the second Cabin Experience Network switch (158) as processed sensor data.
number: 13
text: The Cabin Experience Network system of claim 12, wherein the sensor processing unit (202, 300) comprises six parallel multi-core computers (304, 306, 308, 310, 312, 314) that are dedicated to process the raw sensor data and, optionally one of the six parallel multi-core computers (304, 306, 308, 310, 312, 314) is trained using a machine-learning algorithm such as a trained neural network.
number: 14
text: The Cabin Experience Network system of any of claims 11 to 13, configured such that a further sensor may be added to the Cabin Experience Network system, and wherein the first Cabin Experience Network switch (150) is in communication with the further sensor and the sensor processing unit (201).
number: 15
text: An aircraft comprising the Cabin Experience Network system of any of claims 11 to 14.
Office action: search_report, dated 2020-12-09
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2017357866A1
ref_type: patent
severity: background
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ref_id: US2015339241A1
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ref_type: patent
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ref_id: US2012007981
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ref_id: US9197906
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severity: background
ref_id: US2015339241
ref_type: patent
severity: background
ref_id: US2017357866
ref_type: patent
severity: novelty_destroying
ref_id: US2018047288
ref_type: patent
severity: novelty_destroying
ref_id: US2018068004
ref_type: patent
severity: novelty_destroying
ref_id: US2018288182
ref_type: patent
severity: novelty_destroying
ref_id: US2018292983
ref_type: patent
severity: novelty_destroying
ref_id: US2019102689
ref_type: patent
severity: novelty_destroying
Final disposition: pending (2020-12-09)