Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: A method for targeted heating element control can include: sampling measurements, determining one or more occupied regions based on the measurements, determining one or more food types, controlling appliance operation based on the occupied regions and food types, optionally providing real-time feedback to the user for food placement within the cavity, optionally sampling training measurements, optionally determining a cavity occupancy region classifier, and/or any other suitable elements.

Abstract: A method for targeted heating element control can include: sampling measurements, determining one or more occupied regions based on the measurements, determining one or more food types, controlling appliance operation based on the occupied regions and food types, optionally providing real-time feedback to the user for food placement within the cavity, optionally sampling training measurements, optionally determining a cavity occupancy region classifier, and/or any other suitable elements.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: A method for cavity state determination including: receiving a cook instruction; sampling a set of cavity measurements; determining a cavity state; and selectively operating the cook cavity based on the match.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: A method for timer-based cooking can include: heating a cooking cavity to a cooking temperature, detecting an transition event, determining a food classification, determining a cooking time, tracking a food residence time within the cooking cavity, optionally notifying a user when the residence time satisfies the cooking time, and/or any other suitable element.

Abstract: A method for targeted heating element control can include: sampling measurements, determining one or more occupied regions based on the measurements, determining one or more food types, controlling appliance operation based on the occupied regions and food types, optionally providing real-time feedback to the user for food placement within the cavity, optionally sampling training measurements, optionally determining a cavity occupancy region classifier, and/or any other suitable elements.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: A method for cavity state determination including: receiving a cook instruction; sampling a set of cavity measurements; determining a cavity state; and selectively operating the cook cavity based on the match.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: Computer processes are disclosed for automatically refining the order and/or content of context-specific lists of items (e.g., products) based on monitored actions of users. One such process involves outputting context-specific lists of items to users together with selectable controls for initiating displays of similar items. When users opt to view similar items, the users' actions performed in connection with the similar items are recorded. If the resulting level of user activity recorded for a particular similar item is sufficiently high, the item is automatically added to the associated context-specific list.

Abstract: Computer processes are disclosed for automatically refining the order and/or content of context-specific lists of items (e.g., products) based on monitored actions of users. One such process involves outputting context-specific lists of items to users together with selectable controls for initiating displays of similar items. When users opt to view similar items, the users' actions performed in connection with the similar items are recorded. If the resulting level of user activity recorded for a particular similar item is sufficiently high, the item is automatically added to the associated context-specific list.

Abstract: A computer-implemented system and method are disclosed that are capable of refining the order and/or content of a list of items. In one embodiment, user activity associated with each item displayed to users in a given context is monitored, and is used together with item exposure data to generate context-specific item weights. The weights for the same item in different contexts may be different. The selection of items included in a list presented to users, and/or the order in which the items in the list are displayed, may be adjusted over time based upon the relative weights associated with the items.

Abstract: A computer-implemented system and method are disclosed that are capable of refining the order and/or content of a list of items. In one embodiment, user activity associated with each item displayed to users in a given context is monitored, and is used together with item exposure data to generate context-specific item weights. The weights for the same item in different contexts may be different. The selection of items included in a list presented to users, and/or the order in which the items in the list are displayed, may be adjusted over time based upon the relative weights associated with the items.

Abstract: The invention comprises a computer-implemented system and method for refining the order and/or content of a list of items. In one embodiment, the amount of user activity associated with each item displayed to users in a given context is monitored, used is to generate context-specific item weights. The weights for the same item in different contexts may be different. The selection of items included in a list presented to users, as well as the order in which the items in the list are displayed, may be adjusted over time based upon the relative weights associated with the items. This allows the items generating the greatest user interest in a particular context to be presented to first, or otherwise emphasized.

Abstract: The Viterbi decoder includes a branch metric processor that determines branch metrics for states of an encoder in a time period. The Viterbi decoder includes a survivor processor that selects survivor paths between states of the encoder in consecutive time periods. The Viterbi decoder includes a normalization unit that normalizes state metrics of the states of the encoder in the time period by subtracting a constant.