Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This final action is in response to application filed on 12/12/2025. In this amendment, claims 1-6, 8-15, 18, 22-24 and 29 are amended. Claims 1-25 and 29 are pending, with claims 1, 15 and 29 being independent.
Priority
This application claims the benefit of PCT/EP2021/065275, filed on 06/08/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/01/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Objections
Objection are withdrawn in view of amended claims.
Rejections under 35 U.S.C. 112
Rejections are withdrawn in view of amended claims.
Rejections under 35 U.S.C. 101
Rejections are withdrawn in view of amended claims.
Independent Claims 1, 15, and 29
Applicant’s arguments with respect to claims 1, 15, and 29 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-5, 15-16, 18-19 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017).
As per claim 1, Soyannwo teaches a user terminal emulation server for providing communication services through one or more input and/or output, I/O, user devices to a user (Soyannwo fig. 2, col. 6 lines 60-67, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to coordinate content transfer between the computing devices 102 and the computing device 104), wherein the user terminal emulation server (Soyannwo fig. 2, one of servers 206) comprises at least one processor and at least one memory storing instructions executable by the at least one processor to perform operations (Soyannwo col. 6 lines 49-51, The cloud services 202, generally, refer to a network accessible platform implemented as a computing infrastructure of processors, storage) comprising:
registering the user with a network entity providing the communication services (Soyannwo col. 6 lines 60-67, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to… consume/stream entertainment (e.g., games, music, movies and/or other content, etc.); Soyannwo col. 8 lines 6-9, the cloud services 202 may authentic the user 112, as the service for streaming music may require a logon and/or the various other services may include authentication requirements. [In other words, user is registered with streaming service so that the user can logon for authentication/verification before accessing music service]);
predict that an I/O user device will become proximately located to the user (Soyannwo col. 7 lines 20-27, the user 112 moves from room 106 to room 108 along path 114. As the user moves along path 114, the computing device 102 captures sounds related to the user movement (such as footsteps). The computing device 102 may provide the captured sound to the cloud services 202 and one or more of the cloud services 202 may determine the direction and movement of the user 112 by processing the capture sound; Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office); and
based on the prediction that the I/O user device will become proximately located to the user (Soyannwo fig. 1, device 102, 104 are located in room 106, 108 respectively and col. 7 lines 20-27, the user 112 moves from room 106 to room 108 along path 114. As the user moves along path 114, the computing device 102 captures sounds related to the user movement (such as footsteps). The computing device 102 may provide the captured sound to the cloud services 202 and one or more of the cloud services 202 may determine the direction and movement of the user 112 by processing the capture sound; Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office),
before the user is predicted to become proximately located to the I/O user device (Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office), signaling the I/O user device to prepare for using the I/O user interface to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 16 CLM 10, which when executed by the one or more processors cause the processors to adjust a volume associated with the at least one speaker based at least in part on a distance of the user to the computing device. [The citations indicate that based on monitoring movement of user from room 106 to room 108, cloud service can predict the computing device 104 will become proximately located to the user in order to signal/ activate or wake the computing device 104]) through the network entity (Soyannwo fig. 2, servers 206 comprise one server for coordinating content transfer between the computing devices and different server [network entity] for streaming service; Soyannwo col. 8 lines 60-65, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to coordinate content transfer between the computing devices 102 and the computing device 104, perform database searches, locate and consume/stream entertainment (e.g., games, music, movies and/or other content, etc.)), wherein signaling the I/O user device to prepare for using the I/O user interface to provide the first communication service for the user through the network entity comprises initiating routing of communication traffic of the first communication service to the I/O user device before the user is predicted to become proximately located to the I/O user device (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108. [The citations indicate that based on monitoring movement of user from room 106 to room 108, cloud service can predict the computing device 104 will become proximately located to the user in order to output music from computing device 104 before user is being in room 108]).
Soyannwo does not explicitly disclose:
determining that the I/O user device has an I/O user interface that satisfies a capability criterion to enable the user to use a first one of the communication services, and
based on the satisfaction of the capability criterion, signaling the I/O user device, and controlling the I/O user device to perform one of the following with the communication traffic: buffer the communication traffic for subsequent playout through the I/O user interface responsive to the user becoming proximately located to the I/O user device and discard the communication traffic until the user becomes proximately located to the I/O user device.
Chung teaches:
determining that the I/O user device has an I/O user interface that satisfies a capability criterion (Chung col. 7 lines 59-64, In instances where no active device has desired functionality, such as microphone functionality, the user device may determine whether any connected devices in an inactive state has microphone functionality, and in certain instances, may attempt to activate such devices (e.g., by sending a wakeup command, etc.) to enable the user to use a first one of the communication services (Chung col. 4 lines 57-60, the user device may receive an indication that a first microphone at a first device is to be activated, which may indicate that a voice data stream is forthcoming);
based on the satisfaction of the capability criterion, signaling the I/O user device (Chung col. 7 lines 59-64, In instances where no active device has desired functionality, such as microphone functionality, the user device may determine whether any connected devices in an inactive state has microphone functionality, and in certain instances, may attempt to activate such devices (e.g., by sending a wakeup command, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Soyannwo in view of Chung for determining that the I/O user device has an I/O user interface that satisfies a capability criterion to enable the user to use a first one of the communication services and based on the satisfaction of the capability criterion, signaling the I/O user device.
One of ordinary skill in the art would have been motived because it offers the advantage of ensuring the device is able to providing the service.
Soyannwo-Chung does not explicitly disclose:
controlling the I/O user device to perform one of the following with the communication traffic: buffer the communication traffic for subsequent playout through the I/O user interface responsive to the user becoming proximately located to the I/O user device and discard the communication traffic until the user becomes proximately located to the I/O user device.
Murrells teaches:
controlling the I/O user device to perform one of the following with the communication traffic: buffer the communication traffic for subsequent playout through the I/O user interface responsive to the user becoming proximately located to the I/O user device (Murrells para. [0058], The user 140 moves from the first region 151 to the second region 152 where a second rendering device 111 is located, as shown by FIG. 3B. The second proximity detector 122 detects the first identification device 145 within a second proximity region 127, and alerts the second rendering device 112 that the user 140 is nearby … The second rendering device 112 buffers the media being rendered from the first rendering device 111 to facilitate a smooth handoff from the first rendering device 111 to the second rendering device) and discard the communication traffic until the user becomes proximately located to the I/O user device.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Soyannwo in view of Murrells for controlling the I/O user device to perform buffering the communication traffic for subsequent playout through the I/O user interface responsive to the user becoming proximately located to the I/O user device.
One of ordinary skill in the art would have been motived because it offers the advantage of facilitating a smooth handoff from the first rendering device to the second rendering device (Murrells para. [0058]).
As per claim 2, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses wherein signaling the I/O user device to prepare for using the I/O user interface to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 2 lines 1-3, the computing devices may be equipped with one or more microphones to capture sound from the environment, one or more speakers to output audio into the environment) through the network entity (Soyannwo col. 6 lines 60-67, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to… consume/stream entertainment (e.g., games, music, movies and/or other content, etc.)), comprises:
sending a message configured to trigger the I/O user device to report to the user terminal emulation server when the user has become proximately located (Soyannwo col. 14 lines 54-59, the cloud services request the other devices of the system to locate the user; Soyannwo col. 7 lines 54-62, the computing device 104 may monitor the room 108 for the presence of one or more users, such as user 112. Thus, as the user 112 moves along path 114 into room 108, the computing device 104 may detect the presence of user 112. For instance, the computing device 104 may detect the presence of the user 112 by monitoring a visual or audio input signal. In this implementation, the computing device 104 may notify the cloud services 202 that an unknown user has entered range).
As per claim 4, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses wherein to signaling the I/O user device to prepare for using the I/O user interface to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 2 lines 1-3, the computing devices may be equipped with one or more microphones to capture sound from the environment, one or more speakers to output audio into the environment) through the network entity (Soyannwo col. 6 lines 60-67, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to… consume/stream entertainment (e.g., games, music, movies and/or other content, etc.)) further comprises:
controlling the I/O user device to perform initiating playout of the communication traffic through the I/O user interface (Soyannwo col. 7 lines 40-42, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104; Soyannwo col. 3 lines 20-23, if the cloud service was streaming music to the first device, the cloud service may stop providing the music to the first computing device and start streaming the music to the second computing device).
As per claim 5, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses the operations further comprising:
establishing a communication session with the I/O user device which connects the I/O user device to the user terminal emulation server (Soyannwo col. 7 lines 40-42, the cloud services 116 may activate or wake the computing device 104) before the user is predicted to become proximately located to the I/O user device (Soyannwo fig. 1, device 102, 104 are located in room 106, 108 respectively and col. 7 lines 20-27, the user 112 moves from room 106 to room 108 along path 114. As the user moves along path 114, the computing device 102 captures sounds related to the user movement (such as footsteps). The computing device 102 may provide the captured sound to the cloud services 202 and one or more of the cloud services 202 may determine the direction and movement of the user 112 by processing the capture sound; Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office); and
responsive to determining that the user has become proximately located to the I/O user device (Soyannwo col. 8 lines 59-62, the computing device 104 may determine that the user 112 is entering room 108 and that the conversation should be relayed or transferred to the computing device 104), initiating routing of communication traffic of the first communication service through the communication session established with the I/O user device (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104; Soyannwo col. 13 lines 32-34, The servers 500 may then execute the content transfer module 510, to cause the second computing device to provide the conversation to the first computing device) and cease routing of the communication traffic of the first communication service through another communication session established with a previously proximate I/O user device that is no longer proximately located to the user (Soyannwo col. 3 lines 15-23, if the user moves from a position near the first computing device to a position near the second computing device, the cloud services may transfer the content from the first computing device to the second computing device. For instance, if the cloud service was streaming music to the first device, the cloud service may stop providing the music to the first computing device and start streaming the music to the second computing device).
Per claims 15-16 and 18-19, they do not teach or further define over the limitations in claims 1-2 and 4-5 respectively. As such, claims 15-16 are rejected for the same reasons as set forth in claims 1-2 and 4-5 respectively.
Per claim 29, it does not teach or further define over the limitations in claim 1. As such, claim 29 is rejected for the same reasons as set forth in claim 1. Soyannwo also discloses a computer program product comprising: a non-transitory computer readable medium storing program instructions executable by at least one processor of a user terminal emulation server for providing communication services through one or more input and/or output, I/O, user devices to a user (Soyannwo fig. 4 and col. 10 lines 35-43, Depending on the configuration of the computing device 400, the computer-readable media 412 may be an example of tangible non-transitory computer storage media and may include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of information such as computer readable instructions or modules, data structures, program modules or other data).
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of Watson et al. (US 2020/0359290, Pub. Date: Nov. 12, 2020), in view of O'Keeffe (US 2020/0252233, Filed: Apr. 21, 2020).
As per claim 3, Soyannwo-Chung-Murrells discloses the server according to claim 2, as set forth above, Soyannwo does not explicitly disclose the operations further comprising:
repetitively send the message; and
adjusting a rate at which the message is repetitively sent based on at least one of: a determined probability that the I/O user device will become proximately located to user; and a prediction of when the I/O user device will become proximately located to the user.
Watson teaches:
repetitively send the message (Watson fig. 4, waiting 100 milliseconds at 460 before looping back to operation 410 and transmitting a next probe request message); and
adjusting a rate at which the message is repetitively sent based on at least one of: a determined indication that a device will become proximately located to user (Watson para. [0063], When the wireless access point 121 detects that the mobile communication device 110 moves nearer to the wireless access point 121, such as due to a detected increase in a power level of receiving wireless communications 152 over time, the wireless access point 121 reduces the rate at which probe request communications 151 are communicated to the mobile communication device 110; Watson para. [0009], the communication management resource initiates a handoff of the mobile communication device from the first wireless access point to a second wireless access point in a network environment based on the quantified motion and/or predicted location of the mobile communication device; Watson para. [0061], monitors the respective power level of such communications to determine whether the mobile communication device 110 is moving towards or away from the wireless access point); and a prediction of when the I/O user device will become proximately located to the user.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Watson in order to incorporate the user terminal emulation server is configured to repetitively send the message; and to adjust a rate at which the message is repetitively sent based on a determined indication that the I/O user device will become proximately located to user.
One of ordinary skill in the art would have been motived because it offers the advantage of preparing handoff service based on predicted location (see Watson para. [0009]).
Notes: Waston teaches reducing/adjusting a rate of repetitively sending message based on determined indication that the device will become proximately located to user (Watson fig. 4 and para. [0063]). However, Waston does not explicitly disclose the determined indication is a determined probability (corresponding to a determined probability that the I/O user device will become proximately located to user).
O'Keeffe teaches:
a determined probability that the I/O user device will become proximately located to user (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe for adjusting a rate at which the message is repetitively sent based on a determined probability that the I/O user device will become proximately located to user.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Per claim 17, it does not teach or further define over the limitations in claim 3. As such, claim 17 is rejected for the same reasons as set forth in claim 3.
Claims 6-7 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of Rao et al. (US 2021/0195252, Pub. Date: Jun. 24, 2021).
As per claim 6, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses wherein signaling the I/O user device to prepare for using the I/O user interface to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 16 CLM 10, which when executed by the one or more processors cause the processors to adjust a volume associated with the at least one speaker based at least in part on a distance of the user to the computing device) through the network entity (Soyannwo col. 6 lines 60-65, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to coordinate content transfer between the computing devices 102 and the computing device 104 perform database searches, locate and consume/stream entertainment (e.g., games, music, movies and/or other content, etc.)).
Soyannwo does not explicitly disclose:
communicating device configuration data to the I/O user device which configures the I/O user device for using the I/O user interface to provide the first communication service.
Rao teaches:
communicating device configuration data to the I/O user device (Rao fig. 1&3 and para. [0036], The video format instructions are sent from the video production device 110 to the video capture devices 160 (task 312), via the wireless network and/or wired connections) which configures the I/O user device for using the I/O user interface to provide the first communication service (Rao para. [0037], The individual video format instructions are received and executed at each video capture device (task 314) … the video format instructions are executed by the video capture devices to confirm or adjust the bitrate video resolution settings of the video input streams. In this regard, the video capture device that is providing the current video output stream can be instructed to set a higher video resolution for the current video output stream; Rao para. [0019], The hosting or distribution server system is suitably configured and operated to support or provide various services, such as YOUTUBE, FACEBOOK, USTREAM, TWITCH, MIXER, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Rao in order to incorporate the user terminal emulation server is configured to communicate device configuration data to the I/O user device which configures the I/O user device for using the I/O user interface to provide the first communication service.
One of ordinary skill in the art would have been motived because it offers the advantage of automatically adjusting certain characteristics, parameters, or settings of the video capture devices in a dynamic, on-demand manner (Rao para. [0016]).
As per claim 7, Soyannwo-Chung-Murrells-Rao discloses the server according to claim 6, as set forth above, Rao also discloses the device configuration data communicated to the I/O user device is adapted to configure at least one of the following operations of the I/O user device (Rao fig. 1&3 and para. [0036], The video format instructions are sent from the video production device 110 to the video capture devices 160 (task 312), via the wireless network and/or wired connections):
relative durations of a sleep cycle and an awake cycle, wherein during the sleep cycle the I/O user device is not capable of providing the UI capability to the user and during the awake cycle the I/O user device is capable of providing the UI capability to the user; a paging procedure of the I/O user device;
a frame rate of video to be streamed from a camera of the I/O user device toward the network entity when the first communication service is provided through the I/O user device; a resolution of video to be streamed from the camera of the I/O user device toward the network entity when the first communication service is provided through the I/O user device;
a coding format of video to be streamed from the camera of the I/O user device toward the network entity when the first communication service is provided through the I/O user device;
a coding format of audio to be streamed from a microphone of the I/O user device toward the network entity when the first communication service is provided through the I/O user device;
a frame rate of video to be displayed by a display screen of the I/O user device toward the network entity when a video stream is received from the network entity for the first communication service;
a resolution of video to be displayed by a display screen of the I/O user device (Rao para. [0037], The individual video format instructions are received and executed at each video capture device (task 314) … the video format instructions are executed by the video capture devices to confirm or adjust the bitrate video resolution settings of the video input streams. In this regard, the video capture device that is providing the current video output stream can be instructed to set a higher video resolution for the current video output stream) when a video stream is received from the network entity for the first communication service (Soyannwo col. 6 lines 60-65, The cloud services 202 are implemented by one or more servers, such as servers 206. Additionally, the servers 206 may host any number of cloud based services 202, such as one or more services to coordinate content transfer between the computing devices 102 and the computing device 104 perform database searches, locate and consume/stream entertainment (e.g., games, music, movies and/or other content, etc.)); and
user input interface key assignments used to translate user input via the user input interface into user inputted data which will be sent toward the network entity when the first communication service is provided through the I/O user device.
Similar rationale in claim 6 is applied.
Per claims 20-21, they do not teach or further define over the limitations in claims 6-7 respectively. As such, claims 20-21 are rejected for the same reasons as set forth in claims 6-7 respectively.
Claims 8 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of Meingast et al. (US 11,611,451, Filed: Jun. 5, 2020).
As per claim 8, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo does not explicitly disclose the operations further comprising:
training a machine learning model based on the time ordered sequences of I/O user devices that have been historically.
Meingast teaches:
training a machine learning model (Meingast col. 5 lines 61-65, From the graphical node map and continued machine learning of users' recorded movement paths within the monitored environment 102, routines or common movement paths, whether time-dependent or time-independent, may be learned by the monitoring system 100; Meingast col. 4 lines 10-16, By recording these signals over time and applying machine learning techniques, a common movement path, or a weighted pattern record, may be learned from a user's interactions with the monitored environment and the user's time-based interactions with the monitored environment, as well as the context in which these movement paths are done) based on the time ordered sequences of I/O user devices that have been historically observed to become proximately located to users (Meingast fig. 5-6, a movement path record and col. 12 lines 4-19, The method includes sensing a user presence at a first node 601 by one of an event triggered by a network connected sensor or user input being received at a network connected device, and recording, to a movement path record, 602 first node parameters that may include a node ID, a first node time … The method further includes sensing 604 a user presence at a subsequent node, other than the first node, by another network-connected sensor or user input been received at another network-connected device, and subsequently recording, to the movement path record, 606 subsequent node parameters that may include a subsequent node ID, a subsequent node time).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Meingast for training a machine learning model based on the time ordered sequences of I/O user devices that have been historically observed to become proximately located to users.
One of ordinary skill in the art would have been motived because it offers the advantage of learning a common movement path to provide functions interact with the network-connected devices in the monitored environment (see Meingast col. 4 lines 21-25).
Per claim 22, it does not teach or further define over the limitations in claim 8. As such, claim 22 is rejected for the same reasons as set forth in claim 8.
Claims 9 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of Meingast et al. (US 11,611,451, Filed: Jun. 5, 2020), in view of O'Keeffe (US 2020/0252233, Filed: Apr. 21, 2020).
As per claim 9, Soyannwo-Chung-Murrells-Meingast discloses the server according to claim 8, as set forth above, Meingast also discloses the machine learning model that has been trained (Meingast col. 5 lines 61-65, From the graphical node map and continued machine learning of users' recorded movement paths within the monitored environment 102, routines or common movement paths, whether time-dependent or time-independent, may be learned by the monitoring system 100) based on time ordered sequences of I/O user devices that have been historically observed to become proximately located to users (Meingast fig. 5-6, a movement path record and col. 12 lines 4-19, The method includes sensing a user presence at a first node 601 by one of an event triggered by a network connected sensor or user input being received at a network connected device, and recording, to a movement path record, 602 first node parameters that may include a node ID, a first node time … The method further includes sensing 604 a user presence at a subsequent node, other than the first node, by another network-connected sensor or user input been received at another network-connected device, and subsequently recording, to the movement path record, 606 subsequent node parameters that may include a subsequent node ID, a subsequent node time).
Similar rationale in claim 8 is applied.
Soyannwo-Meingast does not explicitly disclose:
processing information indicating present proximity of the user to another I/O user device through the machine learning; and
predicting that the I/O user device will become proximately located to the user based on output of the machine learning model from processing the information.
O'Keeffe teaches:
processing information indicating present proximity of the user to another I/O user device (O'Keeffe para. [0163], In a first embodiment a person 1325 can be sensed by smart fixture 105a and smartphone 130a. Both devices are operable connected to central controller 150 through a wireless router 1440 and internet connection 160. Central controller 150 can aggregate signals from smartphone 130a and smart fixture 105a at data aggregators 1604a and 1604b) through the machine learning (O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms); and
predicting that the I/O user device will become proximately located to the user based on output of the machine learning model from processing the information (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures; O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe for processing information indicating present proximity of the user to another I/O user device through the machine learning that has been trained based on time ordered sequences of I/O user devices that have been historically observed to become proximately located to users; and predicting that the I/O user device will become proximately located to the user based on output of the machine learning model from processing the information.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Per claim 23, it does not teach or further define over the limitations in claim 9. As such, claim 23 is rejected for the same reasons as set forth in claim 9.
Claims 10 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of O'Keeffe (US 2020/0252233, Filed: Apr. 21, 2020), in view of Meingast et al. (US 11,611,451, Filed: Jun. 5, 2020), in view of Watson et al. (US 2020/0359290, Pub. Date: Nov. 12, 2020).
As per claim 10, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses sending a message configured to trigger the I/O user device to prepare the I/O user device for using the I/O user interface to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 16 CLM 10, which when executed by the one or more processors cause the processors to adjust a volume associated with the at least one speaker based at least in part on a distance of the user to the computing device).
Soyannwo does not explicitly disclose:
repetitively sending a message;
processing information indicating present proximity of the user to another I/O user device through a machine learning model that has been trained based on time ordered sequences of I/O user devices among the I/O user devices identified in a repository that have been historically observed to become proximately located to users, to output an indicated probability that the user will be proximately located to the I/O user device; and
adjusting a rate at which the message is repetitively sent based on the indicated probability.
O'Keeffe teaches:
processing information indicating present proximity of the user to another I/O user device (O'Keeffe para. [0163], In a first embodiment a person 1325 can be sensed by smart fixture 105a and smartphone 130a. Both devices are operable connected to central controller 150 through a wireless router 1440 and internet connection 160. Central controller 150 can aggregate signals from smartphone 130a and smart fixture 105a at data aggregators 1604a and 1604b) through a machine learning model (O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms), to output an indicated probability that the user will be proximately located to the I/O user device (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe processing information indicating present proximity of the user to another I/O user device through a machine learning model, to output an indicated probability that the user will be proximately located to the I/O user device.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Soyannwo-O'Keeffe does not explicitly disclose:
repetitively sending a message;
a machine learning model that has been trained based on time ordered sequences of I/O user devices among the I/O user devices identified in a repository that have been historically observed to become proximately located to users; and
adjusting a rate at which the message is repetitively sent based on the indicated probability.
Meingast teaches:
a machine learning model that has been trained (Meingast col. 5 lines 61-65, From the graphical node map and continued machine learning of users' recorded movement paths within the monitored environment 102, routines or common movement paths, whether time-dependent or time-independent, may be learned by the monitoring system 100; Meingast col. 4 lines 10-16, By recording these signals over time and applying machine learning techniques, a common movement path, or a weighted pattern record, may be learned from a user's interactions with the monitored environment and the user's time-based interactions with the monitored environment, as well as the context in which these movement paths are done) based on time ordered sequences of I/O user devices among the I/O user devices identified in a repository (Meingast fig. 5, a movement path record and col. 10 lines 49-55, The movement path record 520 may include a unique 50 movement path record identifier 522, a series of movement path node records 524-528, a user ID 530, segment time durations 532 and a total movement path duration time 534. The unique movement path record identifier 522 identifies the movement path record 520 when it may be stored in a repository of stored movement path records of 540) that have been historically observed to become proximately located to users (Meingast fig. 5-6, a movement path record and col. 12 lines 4-19, The method includes sensing a user presence at a first node 601 by one of an event triggered by a network connected sensor or user input being received at a network connected device, and recording, to a movement path record, 602 first node parameters that may include a node ID, a first node time … The method further includes sensing 604 a user presence at a subsequent node, other than the first node, by another network-connected sensor or user input been received at another network-connected device, and subsequently recording, to the movement path record, 606 subsequent node parameters that may include a subsequent node ID, a subsequent node time).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Meingast in order to incorporate a machine learning model that has been trained based on time ordered sequences of I/O user devices among the I/O user devices identified in a repository that have been historically observed to become proximately located to users.
One of ordinary skill in the art would have been motived because it offers the advantage of learning a common movement path to provide functions interact with the network-connected devices in the monitored environment (see Meingast col. 4 lines 21-25).
Soyannwo-O'Keeffe-Meingast does not explicitly disclose:
repetitively sending a message;
adjusting a rate at which the message is repetitively sent based on the indicated probability.
Watson teaches:
repetitively sending a message (Watson fig. 4, waiting 100 milliseconds at 460 before looping back to operation 410 and transmitting a next probe request message);
adjusting a rate at which the message is repetitively sent based on indication that the user will be proximately located to the device (Watson para. [0063], When the wireless access point 121 detects that the mobile communication device 110 moves nearer to the wireless access point 121, such as due to a detected increase in a power level of receiving wireless communications 152 over time, the wireless access point 121 reduces the rate at which probe request communications 151 are communicated to the mobile communication device 110; Watson para. [0009], the communication management resource initiates a handoff of the mobile communication device from the first wireless access point to a second wireless access point in a network environment based on the quantified motion and/or predicted location of the mobile communication device; Watson para. [0061], monitors the respective power level of such communications to determine whether the mobile communication device 110 is moving towards or away from the wireless access point).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Watson for repetitively sending a message and adjusting a rate at which the message is repetitively sent based on the indicated probability.
One of ordinary skill in the art would have been motived because it offers the advantage of preparing handoff service based on predicted location (see Watson para. [0009]).
Per claim 24, it does not teach or further define over the limitations in claim 10. As such, claim 24 is rejected for the same reasons as set forth in claim 10.
Claims 11, 14 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of O'Keeffe (US 2020/0252233, Filed: Apr. 21, 2020), in view of Meingast et al. (US 11,611,451, Filed: Jun. 5, 2020).
As per claim 11, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo does not explicitly disclose wherein predicting that the I/O user device will become proximately located to the user, comprises:
processing a time of day and date through a machine learning model that has been trained based on historically observed proximity between identified users and identified I/O user devices at identified times and dates, to output probability predictions of which of the identified users are predicted to become proximately located to identified I/O user devices; and
predicting whether the I/O user device will become proximately located to the user based on an identity of the user and the probability predictions output by the machine learning model.
O'Keeffe teaches:
processing a time of day and date (O'Keeffe Abstract, A central controller for building automation can use historical user profiles to select output devices (e.g. automated lights and speakers); O'Keeffe para. [0109], Historical user profile 455 can include data regarding one or more routes 459 traveled by the profile owner. Routes 459 can include a list of regions and may contain the sequence in which regions are visited. Routes 459 can have an associated time of day or day of week (e.g. profile #1 with identity=Dad can contain morning sequence={Bedroom, Bathroom, Bedroom, Kitchen, Garage} and evening sequence={ Garage, Kitchen, Home Office})) through a machine learning model (O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms) to output probability predictions of which of the identified users are predicted to become proximately located to identified I/O user devices (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures); and
predicting whether the I/O user device will become proximately located to the user (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures) based on an identity of the user (O'Keeffe para. [0109], Historical user profile 455 can include data regarding one or more routes 459 traveled by the profile owner. Routes 459 can include a list of regions and may contain the sequence in which regions are visited. Routes 459 can have an associated time of day or day of week (e.g. profile #1 with identity=Dad can contain morning sequence={Bedroom, Bathroom, Bedroom, Kitchen, Garage} and evening sequence={ Garage, Kitchen, Home Office})) and the probability predictions output by the machine learning model ( O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe for processing a time of day and date through a machine learning model to output probability predictions of which of the identified users are predicted to become proximately located to identified I/O user devices; and predicting whether the I/O user device will become proximately located to the user based on an identity of the user and the probability predictions output by the machine learning model.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Soyannwo-O'Keeffe does not explicitly disclose:
a machine learning model that has been trained based on historically observed proximity between identified users and identified I/O user devices at identified times and dates.
Meingast teaches:
a machine learning model that has been trained (Meingast col. 5 lines 61-65, From the graphical node map and continued machine learning of users' recorded movement paths within the monitored environment 102, routines or common movement paths, whether time-dependent or time-independent, may be learned by the monitoring system 100) based on historically observed proximity between identified users (Meingast fig. 5-6, movement path record and col. 11 lines 31-35, The user ID 530 may include user ID parameter information detected or input at the network-connected nodes 300 or network-connected devices 350 that includes information either identifying a registered user or identifying an unregistered user with the movement path record 520) and identified I/O user devices at identified times and dates (Meingast fig. 5-6, movement path record and col. 12 lines 4-19, The method includes sensing a user presence at a first node 601 by one of an event triggered by a network connected sensor or user input being received at a network connected device, and recording, to a movement path record, 602 first node parameters that may include a node ID, a first node time … The method further includes sensing 604 a user presence at a subsequent node, other than the first node, by another network-connected sensor or user input been received at another network-connected device, and subsequently recording, to the movement path record, 606 subsequent node parameters that may include a subsequent node ID, a subsequent node time; Meingast col. 9 lines 26-32, The time parameter recorded at each event triggered by a network-connected sensor or at each input by a user on a network-connected device may include a time of day, a day of the month, a day of the week, a week of the month, a week of the year, and/or a type of day, namely, whether the day may be a holiday or a non-holiday, and/or a weekday or a weekend).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Meingast for a machine learning model that has been trained based on historically observed proximity between identified users and identified I/O user devices at identified times and dates.
One of ordinary skill in the art would have been motived because it offers the advantage of learning a common movement path to provide functions interact with the network-connected devices in the monitored environment (see Meingast col. 4 lines 21-25).
As per claim 14, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo-Chung also discloses the operations further comprising:
processing a predicted next location of the user (Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office; Soyannwo col. 2 lines 10-12, the transfer of content between the computing devices may be coordinated by a central computing system or cloud service; Soyannwo col. 12 lines 47-48, a user detection module 506 is provided to detect the presence of a user near one or more computing devices);
selecting a first set of the I/O user devices, from among the candidate sets of the I/O user devices, which have a set of I/O user interfaces that satisfy the capability criterion to enable the user to use the first one of the communication services (Chung col. 7 lines 59-64, In instances where no active device has desired functionality, such as microphone functionality, the user device may determine whether any connected devices in an inactive state has microphone functionality, and in certain instances, may attempt to activate such devices (e.g., by sending a wakeup command, etc.); and
before the user is predicted to become proximately located (Soyannwo col. 2 lines 30-33, In response to the user movement, the first computer may detect that the user is moving away and attempt to locate other devices, such as the second computing device in the office) to the first set of the I/O user devices (Chung col. 7 lines 59-64, In instances where no active device has desired functionality, such as microphone functionality, the user device may determine whether any connected devices in an inactive state has microphone functionality, and in certain instances, may attempt to activate such devices (e.g., by sending a wakeup command, etc.), signaling the first set of the I/O user devices to prepare for using the set of I/O user interfaces to provide the first communication service for the user (Soyannwo col. 7 lines 40-42 & 46-49, the cloud services 116 may activate or wake the computing device 104 and begin to stream the music to computing device 104 … the user 112 hears the music output from both the computing devices 102 and 104 as the user 112 is crossing the threshold between room 106 and 108; Soyannwo col. 16 CLM 10, which when executed by the one or more processors cause the processors to adjust a volume associated with the at least one speaker based at least in part on a distance of the user to the computing device) through the network entity (Soyannwo fig. 2, servers 206 comprise one server for coordinating content transfer between the computing devices and different server [network entity] for streaming service).
Similar rationale in claim 1 is applied.
Soyannwo does not explicitly disclose:
processing a predicted next location of the user through a machine learning model that has been trained based on historically observed sets of the I/O user devices that have been selected for use by users who were proximately located to the next location when using the communication service, to output candidate sets of the I/O user devices.
O'Keeffe teaches:
processing a predicted next location of the user (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420) through a machine learning model (O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms) to output candidate sets of the I/O user devices (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures; O'Keeffe fig. 12, Central controller 1200 comprises proximity estimator and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe for processing a predicted next location of the user through a machine learning model to output candidate sets of the I/O user devices.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Soyannwo-O'Keeffe does not explicitly disclose:
a machine learning model that has been trained based on historically observed sets of the I/O user devices that have been selected for use by users who were proximately located to the next location when using the communication service.
Meingast teaches:
a machine learning model that has been trained (Meingast col. 5 lines 61-65, From the graphical node map and continued machine learning of users' recorded movement paths within the monitored environment 102, routines or common movement paths, whether time-dependent or time-independent, may be learned by the monitoring system 100; Meingast col. 4 lines 10-16, By recording these signals over time and applying machine learning techniques, a common movement path, or a weighted pattern record, may be learned from a user's interactions with the monitored environment and the user's time-based interactions with the monitored environment, as well as the context in which these movement paths are done) based on historically observed sets of the I/O user devices that have been selected for use by users who were proximately located to the next location when using service (Meingast col. 5 lines 56-59, a graphical node map may be constructed to identify how and when a user or multiple users traverse and operate devices within the monitored environment 102; Meingast fig. 5-6, a movement path record and col. 12 lines 4-19, The method includes sensing a user presence at a first node 601 by one of an event triggered by a network connected sensor or user input being received at a network connected device, and recording, to a movement path record, 602 first node parameters that may include a node ID, a first node time … The method further includes sensing 604 a user presence at a subsequent node, other than the first node, by another network-connected sensor or user input been received at another network-connected device, and subsequently recording, to the movement path record, 606 subsequent node parameters that may include a subsequent node ID, a subsequent node time).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Meingast in order to incorporate a machine learning model that has been trained based on historically observed sets of the I/O user devices that have been selected for use by users who were proximately located to the next location when using the communication service.
One of ordinary skill in the art would have been motived because it offers the advantage of learning a common movement path to provide functions interact with the network-connected devices in the monitored environment (see Meingast col. 4 lines 21-25).
Per claim 25, it does not teach or further define over the limitations in claim 11. As such, claim 25 is rejected for the same reasons as set forth in claim 11.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of O'Keeffe (US 2020/0252233, Filed: Apr. 21, 2020), in view of Susel et al. (US 10,251,018, Date of Patent: Apr. 2, 2019).
As per claim 12, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo also discloses wherein to predicting that the I/O user device will become proximately located to the user comprises:
processing a present geographic location of the user through a machine learning model that has been trained based on geographic locations of the I/O user devices; and
predicting whether the I/O user device will become proximately located to the user based on an output of the machine learning model from processing the present geographic location of the user.
O’Keeffe teaches:
processing a present geographic location of the user (O’Keeffe para. [0084], Within the person location system 100, the overall function of the central controller 150 can be to select one or more automated electronic devices 110 to operate, based on fixed wireless sensors data indicative of a person's location, augmented with relevant mobile device locations; O’Keeffe para. [0102], Output device selector 434 can select one or more electronic devices 110 based on factors such as the geographic location relative to the region with highest proximity-weighted occupancy estimate 409 … In another example, output device selector 434 can select a light at the end of a hallway as a output device and can signal the light to illuminate at a higher intensity if the person is far away (e.g. greater than 10 ft) to provide sufficient light intensity at a region associated with a high occupancy estimate 409) through a machine learning model (O'Keeffe fig. 12, Central controller 1200 and para. [0118], the weight generator and proximity estimators can be fixed algorithms or machine learning algorithms); and
predicting whether the I/O user device will become proximately located to the user (O'Keeffe para. [0164], Central controller 150 can thereby identify that person 1325 meets an activity threshold in first data and that historical location models indicate that there is a high probability (for example based on the time of night) that person 1325 is intending to travel to the kitchen 1420. In the example illustrated in FIG. 14 central controller 150 then selects a set of smart fixture (e.g. at smart fixture subset selector 1634 in FIG. 16) and sends second signals to the set of smart fixtures including 1305c, 1305d, 1305e, 1305f, 1305g, 1305h instructing smart fixtures in the set to turn on secondary lights contained in the smart fixtures) based on an output of the machine learning model from processing the present geographic location of the user (O’Keeffe para. [0102], Output device selector 434 can select one or more electronic devices 110 based on factors such as the geographic location relative to the region with highest proximity-weighted occupancy estimate 409 … In another example, output device selector 434 can select a light at the end of a hallway as a output device and can signal the light to illuminate at a higher intensity if the person is far away (e.g. greater than 10 ft) to provide sufficient light intensity at a region associated with a high occupancy estimate 409).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of O'Keeffe for the user terminal emulation server is configured to process a present geographic location of the user through a machine learning model; and predict whether the I/O user device will become proximately located to the user based on an output of the machine learning model from processing the present geographic location of the user.
One of ordinary skill in the art would have been motived because it offers the advantage of providing the most accurate localization estimate (O’Keeffe para. [0076]).
Soyannwo-O'Keeffe does not explicitly disclose:
a machine learning model that has been trained based on geographic locations of the I/O user devices.
Susel teaches:
a machine learning model that has been trained based on geographic locations of the I/O user devices (Susel col. 10 lines 8-12 and lines 18-21, As part of the generation of the machine-learning model, the location prediction module 250 forms a training set of data by identifying a positive training set of instances in which user client devices 110 are determined to be present at the physical location … The location prediction module 250 uses supervised machine learning to train the machine-learning model, with the collected signals of the positive training set; Susel col. 12 lines 26-30, The location context module 245 collects 345 context information comprising signals from the user client device 110 to generate training data. In one embodiment, the context information includes the location of the user client device 110).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Susel for a machine learning model that has been trained based on geographic locations of the I/O user devices.
One of ordinary skill in the art would have been motived because it offers the advantage of improving predictive performance (Susel col. 3 line 2).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Soyannwo et al. (US 9,491,033, Date of Patent: Nov. 8, 2016), in view of Chung (US 11,026,177, Date of Patent: Jun. 1, 2021), in view of Murrells et al. (US 2017/0078305, Pub. Date: Mar. 16, 2017), in view of Barzuza et al. (US 2021/0329524, Filed: Mar. 19, 2021), in view of Garner, IV et al. (US 11,803,416, Filed: Dec. 16, 2020, hereinafter Garner).
As per claim 13, Soyannwo-Chung-Murrells discloses the server according to claim 1, as set forth above, Soyannwo does not explicitly disclose wherein to determining that the I/O user interface of the I/O user device satisfies the capability criterion comprises:
processing a user interface capability of the I/O user interface of the I/O user device and an indication of the first one of the communication services through a machine learning model that has been trained based on user interface capabilities that are required to satisfy different types of communication services; and
determining whether the capability criterion is satisfied based on an output of the machine learning model from processing the user interface capability of the I/O user interface and the indication of the first one of the communication services.
Barzuza teaches:
processing a user interface capability of the I/O user interface of the I/O user device and an indication of the first one of the communication services through a machine learning model (Barzuza para. [0179], In response to determining that the particular communication device 116 is incapable of receiving the video content, the method 1700 may proceed by the distributed communication controller 112 determining alternative devices that are capable of receiving the video content of the call …. Upon determining one or more alternative devices, the distributed communication controller 112 may then access the hardware capabilities field 712 of the device information data structure 700 for each of the one or more alternative devices to determine a suitable alternative communication device 116 that has video hardware (e.g., a display, screen, etc.); Barzuza para. [0280], methods described or claimed herein can be performed using AI, machine learning); and
determining whether the capability criterion is satisfied based on an output of the machine learning model from processing the user interface capability of the I/O user interface and the indication of the first one of the communication services (Barzuza para. [0179], In response to determining that the particular communication device 116 is incapable of receiving the video content, the method 1700 may proceed by the distributed communication controller 112 determining alternative devices that are capable of receiving the video content of the call; Barzuza para. [0220], When the required capabilities are not available at the wearable device 304, the method 2300 may proceed by reserving an alternative device (e.g., conferencing device/ communication device 116, etc.) in the enterprise facility 108 that has the capabilities required, as determined in step 2324 (step 2332); Barzuza para. [0280], methods described or claimed herein can be performed using AI, machine learning)).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Barzuza in order to incorporate the user terminal emulation server is configured to process a user interface capability of the I/O user interface of the I/O user device and an indication of the first one of the communication services through a machine learning model; and determine whether the capability criterion is satisfied based on an output of the machine learning model from processing the user interface capability of the I/O user interface and the indication of the first one of the communication services.
One of ordinary skill in the art would have been motived because it offers the advantage of ensuring the device is able to providing the service.
Soyannwo does not explicitly disclose:
a machine learning model that has been trained based on user interface capabilities that are required to satisfy different types of communication services.
Garner teaches:
a machine learning model that has been trained based on device capabilities that are required to satisfy different types of communication services (Garner col. 15 lines 34-40, These machine learning modules may also be configured to continuously learn which criteria are more important, or have higher weight, based on task/capability information 113, device information 114, and/or performance information 115, and may correspondingly update respective machine learning models maintained by management systems 108; Garner col. 35-39, Task/capability information 113 may include a library or catalog of existing capabilities for devices 106 in networks 104. Task/capability information 113 may also include information regarding which tasks/sub-tasks, or types of tasks/sub-tasks, are supported by devices 106).
It would have beenobvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Soyannwo in view of Garner for a machine learning model that has been trained based on user interface capabilities that are required to satisfy different types of communication services.
One of ordinary skill in the art would have been motived because it offers the advantage of identifying certain devices for performing certain tasks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wilson et al. (US 20160072797) Mobile Virtual Communication And Entertainment Service;
Luna (US 20140370817) Determining Proximity For Devices Interacting With Media Devices;
Fullam (US 20140342660) Media Devices For Audio And Video Projection Of Media Presentations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VINH NGUYEN/Examiner, Art Unit 2453
/DHAIRYA A PATEL/Primary Examiner, Art Unit 2453
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