Reducing bandwidth requirements for peer-to-peer gaming based on importance of remote objects to a local player

• US 7,627,632 B2
• Filed: 11/13/2006
• Issued: 12/01/2009
• Est. Priority Date: 11/13/2006
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Pang, Jeffrey Anson, Douceur, John R., Uyeda, Frank Christopher, Lorch, Jacob R.
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): Rifkin; Ben M
• Application Number:
  US11/559,338
• Publication Number:
  US 20080115121A1
• Time in Patent Office: 1,114 Days
• Field of Search: 706/1, 463/2, 463/42, 709/205, 709/240
• US Class Current: 709/205
• CPC Class Codes: A63F 13/12 involving interaction between a plurality of game devices, e.g. transmisison or distribution systems protocols for networked virtual reality, networked games H04L29/06034
• CPC Class Codes: A63F 13/30 Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
• CPC Class Codes: A63F 13/34 using peer-to-peer connections
• CPC Class Codes: A63F 13/67 adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• CPC Class Codes: A63F 2300/407 Data transfer via internet
• CPC Class Codes: A63F 2300/408 Peer to peer connection
• CPC Class Codes: A63F 2300/6027 using adaptive systems learning from user actions, e.g. for skill level adjustment
• CPC Class Codes: H04L 67/104 Peer-to-peer [P2P] networks
• CPC Class Codes: H04L 67/1059 Inter-group management mechanisms, e.g. splitting, merging or interconnection of groups
• CPC Class Codes: H04L 67/131 Protocols for games, networked simulations or virtual reality
• CPC Class Codes: H04L 67/30 Profiles

Abstract

Techniques enable the reduction of bandwidth requirements for peer-to-peer gaming architectures. In some embodiments, these techniques allow differentiation among players to decide which players should receive continuous updates and which should receive periodic updates. For those gaming systems receiving periodic updates, guided artificial intelligence is employed to simulate activity of a game object based on guidance provided by the periodic updates. Conversely, for those gaming systems receiving continuous updates, the continuous updates may be employed to update the activity of the game object rather than simulating the activity.

Claims

1. In a peer-to-peer multiplayer game architecture where game objects are partitioned across participating peers, a method comprising:

differentiating among multiple remote players to determine whether a remote player should receive continuous updates or periodic updates;

wherein the differentiating of the multiple players is based, at least in part, on at least one of the following;

a distance between a local avatar and a remote avatar, an angular difference between an aiming position of a local avatar and a direction toward a remote avatar, or an amount of time elapsed since an interaction between a local avatar and a game object owned by a remote player;

sending one of continuous updates or periodic updates to each remote player according to the determination; and

receiving, from the remote players, continuous and periodic updates pertaining to game objects;

responsive to the continuous updates, updating activity of the corresponding game objects controlled by the remote players;

responsive to the periodic updates, using guided artificial intelligence to simulate activity of the corresponding game objects controlled by the remote players.

2. A method as described in claim 1, further comprising:

receiving a request from a remote player to modify a state of a game object controlled by a local player in response to an interaction initiated by a game object controlled by the remote player; and

prioritizing the request over both the continuous and periodic updates.

3. A method as described in claim 1, further comprising repeating the differentiating of the multiple players for each set of at least one frame.

4. A method as described in claim 1, further comprising converging when moving from periodic updates to continuous updates.

5. A method as described in claim 1, wherein the periodic updates comprise guidance usable by the guided artificial intelligence to simulate the activity of the game objects controlled by the remote players, and wherein the guidance comprises at least one of:

an estimate of when a next update may be sent by the remote player, a prediction of a future location of a game object, behavioral parameters of a game object, or an aiming angle of a game object.

6. A method as described in claim 1, wherein the differentiating of the multiple players comprises assigning importance values to each remote player indicative of at least one of:

how much attention is being paid by a local player to game objects controlled by each remote player or how much attention is being paid by each remote player to game objects controlled by the local player.

7. A computer-readable medium comprising a plurality of instructions that, when executed by at least one processor, causes the at least one processor to perform acts comprising:

prioritizing multiple remote peers based on importance of a remote peer to a local peer;

wherein the prioritizing of the multiple remote peers based on importance of a remote peer to a local peer is determined by, at least in part, on at least one of the following;

a distance between a local avatar and a remote avatar, an angular difference between an aiming position of a local avatar and a direction toward a remote avatar, or an amount of time elapsed since an interaction between a local avatar and a game object owned by a remote player;

separating the multiple remote peers into at least first and second groups based on the prioritization; and

sending object state information at a first rate to at least one remote peer in the first group and at a second rate to at least one remote peer in the second group.

8. A computer-readable medium as described in claim 7, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform an act comprising simulating activity of a remote peer using artificial intelligence and received guidance.

9. A computer-readable medium as described in claim 7, wherein the object state information sent at a first rate comprises frame-to-frame positional updates and the object state information sent at a second rate comprises predictive information.

10. A computer-readable medium as described in claim 7, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform an act comprising apportioning bandwidth of the local peer, wherein the separating of the multiple remote peers is also based in part on the apportioning of the bandwidth.

11. A computer-readable medium as described in claim 7, wherein the prioritizing and separating of the multiple remote peers occurs on at least a frame-to-frame basis.

12. A computer-readable medium as described in claim 7, wherein the first rate of object state information comprises an update every frame and the second rate of object state information comprises sending guidance to as many second group remote peers as a bandwidth of the local peer will allow.