Abstract: A system and method directs promotional information, such as coupons of possible interest to a user, when they release their cloud-stored print job from a multifunction peripheral at their selected location. The user is identified, along with their location, and promotional information of possible relevance to the user is displayed on the multifunction peripheral touchscreen. Information selected by the user will be printed out along with their released print job. Businesses register and store their promotional information with the cloud print server, and may be assessed a fee when their literature is printed.

Abstract: A system and method for improved predictive maintenance for multifunction peripherals includes machine learning trained with more accurate problem and resolution coding and categorization. A training set is made from past maintenance records that include accurate problem codes, thorough natural language problem descriptions, accurate resolution codes, thorough problem resolution descriptions and problem/solution categorization. When a service call is received, an operator provides record with a natural language problem description and to which they assign a problem code for categorization. Natural language in the record is compared with records in the training set. When there is a sufficient language match, the problem code and categorization from the training set record is used instead of that which was operator assigned. The corrected record can then be fed to improve predictive maintenance machine learning.

Abstract: A system and method for improved predictive maintenance for multifunction peripherals includes machine learning trained with more accurate problem and resolution coding and categorization. A training set is made from past maintenance records that include accurate problem codes, thorough natural language problem descriptions, accurate resolution codes, thorough problem resolution descriptions and problem/solution categorization. When a service call is received, an operator provides record with a natural language problem description and to which they assign a problem code for categorization. Natural language in the record is compared with records in the training set. When there is a sufficient language match, the problem code and categorization from the training set record is used instead of that which was operator assigned. The corrected record can then be fed to improve predictive maintenance machine learning.

Abstract: A system and method provides automated prediction of filenames and storage locations for scan files generated from a user's scan of their financial documents. A prediction model is generated for the user based on weighted values derived from preexisting filenames and preexisting storage locations for their stored electronic files. Text or images from each scan document is analyzed and weighted and compared to the user's prediction model and a suggested name and storage location is automatically generated. The suggestions are confirmed or corrected by the user prior to storage, and any corrections are used to update the user's prediction model.

Abstract: A system and method provides automated prediction of filenames and storage locations for scan files generated from a user's scan of their financial documents. A prediction model is generated for the user based on weighted values derived from preexisting filenames and preexisting storage locations for their stored electronic files. Text or images from each scan document is analyzed and weighted and compared to the user's prediction model and a suggested name and storage location is automatically generated. The suggestions are confirmed or corrected by the user prior to storage, and any corrections are used to update the user's prediction model.

Abstract: A system and method for predicting device failures and generating proposed resolutions for such an error when occurs includes receiving device status data from each of a plurality of identified multifunction peripherals into a memory. Service history data for each of the multifunction peripherals is stored, the service history data including data corresponding to a plurality of data patterns associated with prior device failures associatively with resolutions implemented to address such failures. Patterns are detected in received device status data. Device failure is predicted for at least one identified multifunction peripheral in accordance with detected patterns and service history data. The predicted device failure is reported along with at least one proposed resolution to address a device error predicted by the predictive device failure data.

Abstract: A system and method for predicting device failures and generating proposed resolutions for such an error when occurs includes receiving device status data from each of a plurality of identified multifunction peripherals into a memory. Service history data for each of the multifunction peripherals is stored, the service history data including data corresponding to a plurality of data patterns associated with prior device failures associatively with resolutions implemented to address such failures. Patterns are detected in received device status data. Device failure is predicted for at least one identified multifunction peripheral in accordance with detected patterns and service history data. The predicted device failure is reported along with at least one proposed resolution to address a device error predicted by the predictive device failure data.

Abstract: A system and method for predictive inventory management includes a processor and memory. A network interface receives device status data from each of a plurality of multifunction peripherals into the memory. The memory stores service history data and service call history data for the multifunction peripherals, in addition to replacement parts data corresponding to replacement parts used in prior device repairs. The processor detects patterns in the service history data, the service call history data and the replacement parts data and generates predictive replacement part data for future replacement parts that will be needed based on the device status data and the detected patterns. The processor then outputs the predictive replacement part data.

Abstract: A system and method for paper jam prediction includes a processor, memory and a network interface. Ongoing paper jam data is received from an identified, networked multifunction peripheral. Service call data for the multifunction peripheral indicative of prior service calls is stored in the memory. A sampling window of the paper jam data prior to a service call date is defined and a point in the sampling window when no symptoms of a forthcoming paper jam were present is determined so as to define a prediction window. A relationship between paper jam data in the prediction window of the sampling window and paper jam data outside the prediction window in the sampling window is determined and incoming paper jam data is monitored relative to the relationship data. A paper jam warning is generated when monitored incoming paper jam data indicates a forthcoming paper jam on the multifunction peripheral.

Abstract: A system and method for paper jam prediction includes a processor, memory and a network interface. Ongoing paper jam data is received from an identified, networked multifunction peripheral. Service call data for the multifunction peripheral indicative of prior service calls is stored in the memory. A sampling window of the paper jam data prior to a service call date is defined and a point in the sampling window when no symptoms of a forthcoming paper jam were present is determined so as to define a prediction window. A relationship between paper jam data in the prediction window of the sampling window and paper jam data outside the prediction window in the sampling window is determined and incoming paper jam data is monitored relative to the relationship data. A paper jam warning is generated when monitored incoming paper jam data indicates a forthcoming paper jam on the multifunction peripheral.

Abstract: A system and method for multifunction peripheral device failure prediction includes a processor, memory and a network interface. The system receives device status data from each of a plurality of identified multifunction peripherals. Service history data for each of the multifunction peripherals is stored in memory. The processor detects anomalies in received device status data and generates predictive device failure data for at least one identified multifunction peripheral in accordance with detected anomalies and service history data. Predictive device failure data can be used to schedule technician visits or add device maintenance to previously scheduled visits. Such scheduling can include scheduling of service to geographically clustered devices.

Abstract: A system and method for multifunction peripheral device failure prediction includes a processor, memory and a network interface. The system receives device status data from each of a plurality of identified multifunction peripherals. Service history data for each of the multifunction peripherals is stored in memory. The processor detects anomalies in received device status data and generates predictive device failure data for at least one identified multifunction peripheral in accordance with detected anomalies and service history data. Predictive device failure data can be used to schedule technician visits or add device maintenance to previously scheduled visits. Such scheduling can include scheduling of service to geographically clustered devices.

Abstract: A system and method for predicting device failures and generating proposed resolutions for such an error when occurs includes receiving device status data from each of a plurality of identified multifunction peripherals into a memory. Service history data for each of the multifunction peripherals is stored, the service history data including data corresponding to a plurality of data patterns associated with prior device failures associatively with resolutions implemented to address such failures. Patterns are detected in received device status data. Device failure is predicted for at least one identified multifunction peripheral in accordance with detected patterns and service history data. The predicted device failure is reported along with at least one proposed resolution to address a device error predicted by the predictive device failure data.