Messaging system with circumstance configuration framework
Abstract
An example method comprises: receiving, at the server from a first client device, a request for access to a client feature on the first client device; determining, by the server, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the server, device capabilities needed for the determined rule; determining, by the server, nodes that can be executed and nodes that cannot be executed, based on the device capabilities; executing, by the server, nodes that can be executed to reach a partial decision for the applicable rule; pruning the rule to remove executed nodes and generate a pruned rule that includes nodes that cannot be executed; transmitting the pruned rule and partial decision to the device. The pruned rule is executed on the first client device with partial decision to generate a final decision. The client feature is configured based on the decision.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A computer-implemented method of enabling a client feature, comprising:
receiving, by at least one processor, a request to access a client feature on a first client device; determining, by the least one processor, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the at least one processor, nodes that can be executed, the nodes that can be executed including real-time device capabilities; executing, by the at least one processor, the nodes that can be executed to reach a decision for the applicable rule; and configuring, by the at least one processor, the requested client feature based on the decision.
2 . The method of claim 1 , further comprising collecting device capabilities from a plurality of other client devices that communicated with the first client device.
3 . The method of claim 2 , wherein the applicable rule determines an image resolution in an augmented reality application for the first client device.
4 . The method of claim 1 , further comprising pruning the determined applicable rule to remove nodes that cannot be executed due to lack of data.
5 . The method of claim 1 , wherein the applicable rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.
6 . The method of claim 5 , wherein the applicable rule determines that an image resolution is to be reduced and a priority node includes battery level and a non-priority node includes co-processor availability.
7 . The method of claim 1 , wherein the applicable rule determines model input size in a deep neural network and a node includes co-processor availability.
8 . The method of claim 7 , wherein the deep neural network performs human body segmentation and maintains a frames per second level for the client feature.
9 . The method of claim 1 , wherein the applicable rule determines texture level and maintains a frames per second level for the client feature.
10 . A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of a first client device, cause the first client device to perform operations comprising:
receiving a request to access a client feature on the first client device; determining an applicable rule for the access request, the applicable rule having a plurality of nodes; determining nodes that can be executed, the nodes that can be executed including real-time device capabilities; executing the nodes that can be executed to reach a decision for the applicable rule; and configuring the requested client feature based on the decision.
11 . A first client device, comprising:
a memory that stores instructions; and one or more processors configured by the instructions to perform operations comprising:
receiving a request to access a client feature on the first client device;
determining an applicable rule for the access request, the applicable rule having a plurality of nodes;
determining nodes that can be executed, the nodes that can be executed including real-time device capabilities;
executing the nodes that can be executed to reach a decision for the applicable rule; and
configuring the requested client feature based on the decision.
12 . The first client device of claim 11 , wherein the operations further comprise collecting device capability data from a plurality of other client devices that communicated with the first client device.
13 . The first client device of claim 12 , wherein the determined rule determines a common encoding format for the first Client Device and the plurality of other client devices.
14 . The first client device of claim 12 , wherein the operations further comprise scheduling the collecting to avoid interruption of processing on the devices.
15 . The first client device of claim 12 , wherein the determined rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.
16 . The first client device of claim 15 , wherein the determined rule determines if data should be pre-fetched and a priority node of the determined rule includes if sufficient memory is available to store the pre-fetched data.
17 . The first client device of claim 16 , wherein the determined rule includes non-priority nodes that are executed if the priority node returns a positive value, the non-priority nodes including battery level and connection type.
18 . The first client device of claim 11 , wherein the determined rule determines if data should be prefetched based on first Client Device app version, Client Device location and bandwidth.
19 . The first client device of claim 11 , wherein the determined rule determines upload file size and the nodes include bandwidth and connection type.
20 . The first client device of claim 11 , wherein the determined rule determines transcoding and the nodes include historically stable uploads.Join the waitlist — get patent alerts
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