Abstract: A method for analyzing hidden dynamics, includes acquiring discrete observations, each discrete observation having an observed value selected from two or more allowed discrete values. A factorial hidden Markov model (FHMM) relating the discrete observations with a plurality of hidden dynamics is constructed. A contribution of the state of each hidden dynamic to the discrete observation may be represented in the FHMM as a parameter of a nominal distribution which is scaled by a function of the state of the hidden dynamic. States of the hidden dynamics are inferred from the discrete observations based on the FHMM. Information corresponding to at least one inferred state of at least one of the hidden dynamics is output. The parameters of the contribution of each dynamic to the hidden states may be learnt from a large number of observations. An example of a networked printing system is used to demonstrate the applicability of the method.

Abstract: An apparatus operating on a time sequence of events comprises an event handling module configured to generate a predicted label for a current observed event of the time sequence of events, and a true label handling module configured to process a true label revealed for an observed event of the time sequence of events. The event handling module and the true label handling module cooperatively model stochastic dependence of a true label for the current observed event based on observed events of the time sequence of events and revealed true labels for past observed events of the time sequence of events. The event handling module and the true label handling module operate asynchronously. The event handling module and the true response handling module are suitably embodied by one or more digital processors.

Abstract: A system and method of localizing elements (shared devices and/or their users) in a device infrastructure, such as a printing network, are provided. The method includes mapping a structure in which the elements of a device infrastructure are located, the elements comprising shared devices and users of the shared devices. Probable locations of fewer than all of the elements in the structure are mapped, with at least some of the elements being initially assigned to an unknown location. Usage logs for a plurality of the shared devices are acquired. The acquired usage log for each device includes a user identifier for each of a set of uses of the device, each of the uses being initiated from a respective location within the mapped structure by one of the users. Based on the acquired usage logs and the input probable locations of some of the elements, locations of at least some of the elements initially assigned to an unknown location are predicted.

Abstract: In a monitoring method, a time sequence of information pertaining to a monitored device, network, or system is recorded, comprising observations of the monitored device, network, or system and known prior correct action recommendations for the monitored device, network, or system. A hidden Markov model (HMM) operating on the time sequence of information is maintained. The HMM comprises a hidden state of the monitored device, network, or system. A current state of the monitored device, network, or system is classified using a classification value comprising an emission of the HMM that depends on an estimate of the distribution of the hidden state and on a selected portion of the time sequence of information. An action recommendation is generated for the current state of the monitored device, network, or system based on the classification value.

Abstract: In a method for monitoring hardware devices of the hardware devices network to detect failures, past logged jobs are analyzed to determine a pattern of past usage of hardware devices by users of the hardware devices network. A failure of a hardware device is inferred based on a deviation in usage of the hardware devices from the pattern of past usage. In some embodiments, a state model is constructed that assigns one of two or more available operational states to each hardware device of the hardware devices network based on a recent load of jobs logged to the hardware device and a stored history of logged jobs indicative of typical loading of the hardware devices. One or more failing hardware devices are identified based on the assigned operational states.

Abstract: A constraint solver is encapsulated in a service adapted to participate in a negotiation. In operation, the system constructs a graph in its memory for negotiating a set of actions to be performed by selected ones of a plurality of participants. Each node of the graph defines a negotiation context that has associated therewith a constraint store with at least a set of domain constraints stored therein that limit during the negotiation a set of semantic constraints of the service on variables of the constraint solver.

Abstract: An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k? in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process.

Abstract: An events analysis method comprises: optimizing respective to a set of training data a set of branching transition likelihood parameters associating parent events of type k with child events of type k? in branching processes; inferring a most probable branching process for a set of input data comprising events based on the optimized set of branching transition likelihood parameters; and identifying rare or unusual events of the set of input data based on the inferred most probable branching process. An events analysis apparatus includes a probabilistic branching process learning engine configured to optimize the set of branching transition likelihood parameters, and a probabilistic branching process inference engine configured to infer the most probable branching process.

Abstract: Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.

Abstract: A method for analyzing hidden dynamics, includes acquiring discrete observations, each discrete observation having an observed value selected from two or more allowed discrete values. A factorial hidden Markov model (FHMM) relating the discrete observations with a plurality of hidden dynamics is constructed. A contribution of the state of each hidden dynamic to the discrete observation may be represented in the FHMM as a parameter of a nominal distribution which is scaled by a function of the state of the hidden dynamic. States of the hidden dynamics are inferred from the discrete observations based on the FHMM. Information corresponding to at least one inferred state of at least one of the hidden dynamics is output. The parameters of the contribution of each dynamic to the hidden states may be learnt from a large number of observations. An example of a networked printing system is used to demonstrate the applicability of the method.

Abstract: A plurality of components are specified to perform a negotiation, where each component is a participant, a coordinator, or both. Each component is provided with a conversion table that maps a set of parameters between invocation patterns instantiated by the participants. Each coordinator is provided with a negotiation graph that it modifies to coordinate its neighborhood of negotiation graphs, and each participant is provided with a negotiation graph that it modifies for each invocation pattern it instantiates. Two message types are sent between components that communicate directly with each other in each neighborhood of negotiation graphs. The two message types are sent between the components to collaboratively mirror their negotiation graphs using their conversion tables in each neighborhood of negotiation graphs so that each participant only views information concerning those aspects in its negotiation graph that relate to the parameters of the invocation patterns it instantiated.

Abstract: A negotiation is carried out between a plurality of participants. Each participant defines an invocation pattern. A set of coordinator parameters are attached to each invocation pattern, where the set of coordinator parameters define interdependencies between parameters of the plurality of participants that are shared across their invocation patterns. A graph is constructed to carry out the negotiation of a set of actions to be performed by selected ones of the plurality of participants, with nodes of a first node type that represents a negotiating context or a second node type that represents a decision point. The participants construct the graph with primitives instantiated by the invocation patterns. At least one node in the graph is a decision point that merges two or more negotiation contexts into a single decision point. The single decision point combines values of the parameters from the two or more negotiation contexts.

Abstract: In a method for monitoring hardware devices of the hardware devices network to detect failures, past logged jobs are analyzed to determine a pattern of past usage of hardware devices by users of the hardware devices network. A failure of a hardware device is inferred based on a deviation in usage of the hardware devices from the pattern of past usage. In some embodiments, a state model is constructed that assigns one of two or more available operational states to each hardware device of the hardware devices network based on a recent load of jobs logged to the hardware device and a stored history of logged jobs indicative of typical loading of the hardware devices. One or more failing hardware devices are identified based on the assigned operational states.

Abstract: Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.

Abstract: Document constraint descriptors are obtained from user signals. User signals are received indicating a set of attribute-value relations that can apply to documents. The user signals are used to obtain equivalent logical relations, and the logical relations are used to obtain the document constraint descriptor defining a set of one or more constraints equivalent to the logical relations. The user signals can be received through user interface circuitry of various types, including display/select user interfaces or a paper user interface in which a user marks a form that is scanned. The machine can be a portable computing device, a fixed computing device, or a multifunction device. The machine can also solve the set of constraints and can use the solution to obtain document references, such as Web URLs, indicating documents that satisfy the set of constraints. The user can then request more detail, or can obtain a printed version of a document.

Abstract: A constraint solver is encapsulated in a service adapted to participate in a negotiation. In operation, the system constructs a graph in its memory for negotiating a set of actions to be performed by selected ones of a plurality of participants. Each node of the graph defines a negotiation context that has associated therewith a constraint store with at least a set of domain constraints stored therein that limit during the negotiation a set of semantic constraints of the service on variables of the constraint solver.

Abstract: Information about documents is transferred between machines in the form of constraint descriptors. A first machine can obtain a constraint descriptor that includes information about a set of constraints that documents can satisfy. The first machine can transfer the constraint descriptor to a second machine that is capable of using the constraint descriptor to access documents that satisfy the set of constraints. Similarly, the second machine can receive a data packet from the first machine, where the data packet includes an encoding of the constraint descriptor. The second machine can decode the data packet to obtain the constraint descriptor. Each of the machines can be a portable computing device or a fixed computing device. The first machine can receive user signals through its user interface, defining an attribute-value relation, and the first machine can compile a constraint using the user signals. Or the first machine can receive user signals selecting a stored constraint descriptor.

Abstract: In obtaining performance of a combination of actions, such as in a coordination or scheduling application, combination data indicating a combination of two or more action types can be obtained. The combination data could, for each action type, identify a service that can be performed by a networked server to provide an instance of the action type. Combinations of offers can be generated, with the offers in each combination together offering the combination of action types indicated by the combination data. In generating the combinations, inquiries can be provided to the servers or other sources of actions, with an inquiry indicating an action type and requesting offers offering to perform an action and indicating an action identifier for the offered action. The action identifiers from the offers in any of the generated combinations can be used to obtain performance of that combination of actions.

Abstract: A plurality of components are specified to perform a negotiation, where each component is a participant, a coordinator, or both. Each component is provided with a conversion table that maps a set of parameters between invocation patterns instantiated by the participants. Each coordinator is provided with a negotiation graph that it modifies to coordinate its neighborhood of negotiation graphs, and each participant is provided with a negotiation graph that it modifies for each invocation pattern it instantiates. Two message types are sent between components that communicate directly with each other in each neighborhood of negotiation graphs. The two message types are sent between the components to collaboratively mirror their negotiation graphs using their conversion tables in each neighborhood of negotiation graphs so that each participant only views information concerning those aspects in its negotiation graph that relate to the parameters of the invocation patterns it instantiated.

Abstract: A negotiation is carried out between a plurality of participants. Each participant defines an invocation pattern. A set of coordinator parameters are attached to each invocation pattern, where the set of coordinator parameters define interdependencies between parameters of the plurality of participants that are shared across their invocation patterns. A graph is constructed to carry out the negotiation of a set of actions to be performed by selected ones of the plurality of participants, with nodes of a first node type that represents a negotiating context or a second node type that represents a decision point. The participants construct the graph with primitives instantiated by the invocation patterns. At least one node in the graph is a decision point that merges two or more negotiation contexts into a single decision point. The single decision point combines values of the parameters from the two or more negotiation contexts.