# US 2019/0174419 A1 · Adjusting mobile device state based on user intentions and/or identity > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0174419 A1 - Document ID: US20190174419A1 - Status: Published application - Kind: A1 - Application number: 16/261,126 - Filed: 2019-01-29 - Published: 2019-06-06 - Earliest claimed: 2012-07-20 - Assignees: Facebook, Inc. - Applicants: Facebook, Inc. - Inventors: Benoit M. Schillings; David Harry Garcia - Web page: https://patentgrep.com/patent/US20190174419A1 ## CPC - H04W 52/0254: {detecting a user operation or a tactile contact or a motion of the device} - G06F 1/3203: Power management, i.e. event-based initiation of a power-saving mode - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0499: Feedforward networks - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06N 3/096: Transfer learning - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - H04Q 9/00: Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom - H04W 8/22: Processing or transfer of terminal data, e.g. status or physical capabilities - H04W 52/0212: {managed by the network, e.g. network or access point is leader and terminal is follower} - H04Q 2209/10: using a centralized architecture - H04Q 2209/43: using wireless personal area networks [WPAN], e.g. 802.15, 802.15.1, 802.15.4, Bluetooth® or Zigbee® - H04Q 2209/47: using RFID associated with sensors - H04Q 2209/50: using a mobile data collecting device, e.g. walk by or drive by - H04Q 2209/823: where the data is sent when the measured values exceed a threshold, e.g. sending an alarm - Y02D 30/70: in wireless communication networks - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04Q: SELECTING (switches, relays, selectors H01H; wireless communication networks H04W) - Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE ## Abstract In one embodiment, a method includes receiving data associated with multiple first client devices, the data corresponding to a first set of sensor values and physical-state indicators of the first client devices; receiving data associated with a second client device corresponding to a second set of sensor values, the second client device including a first and a second set of sensors; generating functions that each determine a probability that the second client device is in a particular physical state, wherein the determination includes correlating the second set of sensor values with a corresponding physical state of the second client device based on the data associated with the multiple first client devices, and wherein a first function is based on values of the first set of sensors and a second function is based on values of the second set of sensors; and sending the generated functions to the second client device. ## Claims ### Claim 1 A method comprising: by a computing device, receiving data associated with a plurality of first client devices, the data associated with each of the first client devices corresponding to a first set of sensor values and one or more indicators that each correspond to one of a plurality of physical states of the each of the first client devices; by the computing device, receiving data associated with a second client device corresponding to a second set of sensor values, the second client device comprising a first and a second set of sensors; by the computing device, generating a plurality of functions that each determine a probability that the second client device is in a respective one of the physical states, wherein the determination comprises correlating the second set of sensor values with a corresponding physical state of the second client device based on the data associated with the plurality of first client devices, and wherein a first function of the plurality of functions is based on sensor values of the first set of sensors and a second function of the plurality of functions is based on sensor values of the second set of sensors; and by the computing device, sending the plurality of functions to the second client device. ### Claim 2 (depends on 1) The method of claim 1, further comprising segregating the data based on a sensor type. ### Claim 3 (depends on 1) The method of claim 1, further comprising: representing the sensor values as a vector; and arranging the data in a data structure. ### Claim 4 (depends on 3) The method of claim 3, wherein the first function comprises a plurality of weights that are multiplied to each value of the vector. ### Claim 5 (depends on 1) The method of claim 1, wherein the plurality of functions are generated at pre-determined intervals. ### Claim 6 (depends on 1) The method of claim 1, wherein the first function comprises one or more logical operators. ### Claim 7 (depends on 1) The method of claim 1, wherein the second function is computationally more complex than the first function. ### Claim 8 (depends on 1) The method of claim 1, wherein the first function is configured to output a value for each physical state of the second client device. ### Claim 9 (depends on 1) The method of claim 1, wherein the second function is configured to adjust the probability the second client device is in one of the particular physical states based on a time of day. ### Claim 10 (depends on 1) The method of claim 1, wherein the first set of sensor values comprises training data for a neural network. ### Claim 11 (depends on 10) The method of claim 10, wherein the neural network generates first or second function using backpropagation training. ### Claim 12 (depends on 10) The method of claim 10, wherein the neural network comprises a Hopfield, Elman, Jordan, Echo state, long short-term memory, bidirectional, or continuous time recurrent neural network. ### Claim 13 (depends on 10) The method of claim 10, wherein the neural network comprises one or more adaptive linear elements. ### Claim 14 (depends on 1) The method of claim 1, wherein the first set of sensors comprise low-power sensors. ### Claim 15 (depends on 14) The method of claim 14, wherein the first set of sensors are one or more capacitive touch sensors. ### Claim 16 (depends on 1) The method of claim 1, wherein the second set of sensors comprise sensors that are inactive in a locked or sleep mode of the second client device. ### Claim 17 (depends on 16) The method of claim 16, wherein the second set of sensors are an accelerometer, gyrometer, proximity sensor, or light sensor. ### Claim 18 (depends on 1) The method of claim 1, wherein the plurality of physical states each correspond to a particular use of a respective first or second client device. ### Claim 19 One or more computer-readable non-transitory storage media embodying software that is operable when executed to: receive data associated with a plurality of first client devices, the data associated with each of the first client devices corresponding to a first set of sensor values and one or more indicators that each correspond to one of a plurality of physical states of the each of the first client devices; receive data associated with a second client device corresponding to a second set of sensor values, the second client device comprising a first and a second set of sensors; generate a plurality of functions that each determine a probability that the second client device is in a respective one of the physical states, wherein the determination comprises correlating the second set of sensor values with a corresponding physical state of the second client device based on the data associated with the plurality of first client devices, and wherein a first function of the plurality of functions is based on sensor values of the first set of sensors and a second function of the plurality of functions is based on sensor values of the second set of sensors; and send the plurality of functions to the second client device. ### Claim 20 A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive data associated with a plurality of first client devices, the data associated with each of the first client devices corresponding to a first set of sensor values and one or more indicators that each correspond to one of a plurality of physical states of the each of the first client devices; receive data associated with a second client device corresponding to a second set of sensor values, the second client device comprising a first and a second set of sensors; generate a plurality of functions that each determine a probability that the second client device is in a respective one of the physical states, wherein the determination comprises correlating the second set of sensor values with a corresponding physical state of the second client device based on the data associated with the plurality of first client devices, and wherein a first function of the plurality of functions is based on sensor values of the first set of sensors and a second function of the plurality of functions is based on sensor values of the second set of sensors; and send the plurality of functions to the second client device.