Multi-scale temporal processing system with context integration
A system for processing and integrating contextual information across multiple time scales simultaneously analyzes immediate and historical data. The system comprises three main components working together to maintain temporal coherence. A temporal processing engine processes input data at different time scales using multiple processors, each dedicated to a specific time horizon. A context integration engine extracts and organizes contextual features from the input data, generating representations for different types of context while maintaining state information across these context types. A fusion engine combines the outputs from the temporal processors and integrates them with the context representations to generate coherent, unified outputs. The three engines operate cooperatively to ensure temporal consistency while processing context across multiple time scales. This architecture enables robust handling of complex temporal and contextual relationships, supporting applications in fields such as customer service, healthcare monitoring, and interactive systems where maintaining coherence across time scales is crucial.
1 . A computer system comprising:
a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that implement:
a temporal processing engine configured to:
receive input data comprising immediate context data and historical context data;
process the input data in parallel at multiple time scales using a plurality of temporal processors, wherein each temporal processor operates at a different time scale, and wherein the plurality of temporal processors comprises a short-term processor configured to process immediate context data, a medium-term processor configured to process recent historical data, and a long-term processor configured to process extended historical data, each processor maintaining processor-specific state information;
maintain temporal coherence between the plurality of temporal processors by synchronizing the processor-specific state information across the multiple time scales;
identify patterns across the multiple time scales and generate pattern information by correlating the patterns detected at the multiple time scales using the processor-specific state information; and
a context integration engine configured to:
extract context features from the input data;
generate context representations from the context features for different context types; and
maintain state information across the different context types concurrently and independently with respect to the plurality of temporal processors;
a fusion engine configured to:
combine a plurality of temporal outputs from the plurality of temporal processors into a unified temporal output;
integrate the unified temporal output with the context representations to produce integrated temporal and context information by implementing sequential fusion layers;
validate the consistency of the integrated temporal and context information; and
generate a coherent output based on the integrated temporal and context information.
2 . The computer system of claim 1 , wherein each temporal processor implements pattern identification and state tracking specific to its respective time scale while maintaining state information within that time scale.
3 . The computer system of claim 1 , wherein the computer system is further configured to process domain-specific information through domain context, entity relationships through entity context, and interaction patterns through interaction context.
4 . The computer system of claim 1 , wherein the computer system is further configured to dynamically assign and adjust weights to the multiple temporal scales based on the identified patterns within the input data to generate the coherent output.
5 . The computer system of claim 1 , wherein maintaining temporal coherence comprises identifying and reconciling temporal dependencies while tracking state transitions between the multiple time scales.
6 . The computer system of claim 1 , wherein the context integration engine maintains consistency between the context representations while tracking relationships and state changes across the different context types.
7 . The computer system of claim 1 , wherein the computer system is further configured to maintain consistent state information and track state transitions across the multiple time scales during temporal processing.
8 . The computer system of claim 1 , wherein the computer system is further configured to identify and track pattern evolution across the multiple time scales while integrating the pattern information into the context representations.
9 . The computer system of claim 1 , wherein the computer system is further configured to implement sequential fusion layers for temporal information, context types, and cross-integration of temporal and context information.
10 . The computer system of claim 1 , wherein the computer system is further configured to adjust processing parameters and update the context representations based on identified pattern changes across the multiple time scales.
11 . The computer system of claim 1 , wherein the computer system is further configured to validate temporal, context, and state consistency throughout the integration process when generating the coherent output.
12 . A computer-implemented method comprising the steps of:
receiving input data comprising immediate context data and historical context data;
processing the input data in parallel at multiple time scales using a plurality of temporal processors, wherein each temporal processor operates at a different time scale, and wherein the plurality of temporal processors comprises a short-term processor configured to process immediate context data, a medium-term processor configured to process recent historical data, and a long-term processor configured to process extended historical data, each processor maintaining processor-specific state information;
maintaining temporal coherence between the plurality of temporal processors by synchronizing the processor-specific state information across the multiple time scales;
identifying patterns across the multiple time scales and generate pattern information by correlating the patterns detected at the multiple time scales using the processor-specific state information;
extracting context features from the input data;
generating context representations from the context features for different context types;
maintaining state information across the different context types concurrently and independently with respect to the plurality of temporal processors;
combining a plurality of temporal outputs from the plurality of temporal processors into a unified temporal output;
integrating the unified temporal output with the context representations to produce integrated temporal and context information by implementing sequential fusion layers;
validating consistency of the integrated temporal and context information; and
generating a coherent output based on the integrated temporal and context information.
13 . The method of claim 12 , wherein processing at each time scale comprises identifying patterns and tracking state transitions specific to the respective time scale while maintaining corresponding state information.
14 . The method of claim 12 , wherein generating context representations comprises processing domain-specific information as domain context, entity relationships as entity context, and interaction patterns as interaction context.
15 . The method of claim 12 , wherein combining the plurality of temporal outputs comprises dynamically assigning and adjusting weights to the multiple temporal scales based on the identified patterns within the input data.
16 . The method of claim 12 , wherein maintaining temporal coherence comprises identifying and reconciling temporal dependencies while tracking state transitions between the multiple time scales.
17 . The method of claim 12 , wherein maintaining state information across the different context types comprises tracking relationships and state changes across the different context types while ensuring consistency between the context representations.
18 . The method of claim 12 further comprising maintaining consistent state information and tracking state transitions across the multiple time scales during temporal processing.
19 . The method of claim 12 further comprising identifying and tracking pattern evolution across the multiple time scales while integrating resulting pattern information into the context representations.
20 . The method of claim 12 , wherein integrating the unified temporal output with the context representations comprises performing sequential fusion operations for temporal information, context types, and cross-integration of temporal and context information.
21 . The method of claim 12 further comprising adjusting processing parameters and updating the context representations based on identified pattern changes across the multiple time scales.
22 . The method of claim 12 , wherein generating the coherent output comprises validating temporal, context, and state consistency throughout the integration process.