Abstract: Systems and computer-readable media for generating dynamic pricing rules to govern offered price-volume break points. Initial target price-volume break points are offered during a time window. For each transaction of the product, transaction information including the transacted price, quantity, and identifying information of the purchaser is received. Based on a sales trend determined from the transaction information, the time window and the offered price can be updated. After the expiration of the time window, redemptions are generated for each purchaser of the product, based at least in part on the final quantity sold during the time window and the offered price-volume break points. A market price elasticity function is interpolated from observed market price elasticities at each price-volume break point. Using the market price elasticity function, dynamic pricing rules are optimized and updated and then used to calculate updated price-volume break points to be offered for the product.

Abstract: Systems and computer-readable media for generating dynamic pricing rules to govern offered price-volume break points. Initial target price-volume break points are offered during a time window. For each transaction of the product, transaction information including the transacted price, quantity, and identifying information of the purchaser is received. Based on a sales trend determined from the transaction information, the time window and the offered price can be updated. After the expiration of the time window, redemptions are generated for each purchaser of the product, based at least in part on the final quantity sold during the time window and the offered price-volume break points. A market price elasticity function is interpolated from observed market price elasticities at each price-volume break point. Using the market price elasticity function, dynamic pricing rules are optimized and updated and then used to calculate updated price-volume break points to be offered for the product.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Systems and computer-readable media for generating dynamic pricing rules to govern offered price-volume break points. Initial target price-volume break points are offered during a time window. For each transaction of the product, transaction information including the transacted price, quantity, and identifying information of the purchaser is received. Based on a sales trend determined from the transaction information, the time window and the offered price can be updated. After the expiration of the time window, redemptions are generated for each purchaser of the product, based at least in part on the final quantity sold during the time window and the offered price-volume break points. A market price elasticity function is interpolated from observed market price elasticities at each price-volume break point. Using the market price elasticity function, dynamic pricing rules are optimized and updated and then used to calculate updated price-volume break points to be offered for the product.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: Disclosed are system and methods of recommending price premium values of products based on price zones and factors. Retail chains have stores in several price zones of varying demographics and price level. The systems and methods use the pattern of pricing in the price zones to identify outliers where the price of a product in a price zone is not consistent with other products priced in that price zone. The price zone premium values used in evaluating individual-product prices in a price zone are calculated locally for each subclass of products and those values may be aggregated into price zone premium values for the entire enterprise.

Abstract: Business rules can govern a single price or they can define relationships between different products involving two or more decision prices. One problem that retailers can face is that business rules are generally not codified and are rarely followed consistently, and thus it can be difficult to articulate existing business rules or generate new business rules. However, existing price information inherently contains industry knowledge and experience, even if retailers find it difficult to express that knowledge. The disclosed technology generates business rules by reverse engineering rule bounds and coefficients from existing price information. The reverse engineered rule bounds and coefficients can be used in price optimization to generate recommended prices for retail products that optimize revenue and profit while complying with a set of business rules.

Abstract: A method (400) and system (100) for providing a promotion plan (128) for merchandising products (600) receives base data (142) for the products (600) that includes allowable offers (204) and price rules (206) that affect the offers (204). A margin budget (146) is established for the promotion plan (128) that defines an amount of margin an enterprise is willing to forgo for a promotion event implementing the promotion plan (128). A scenario (406) is generated in response to the base data (142). The scenario (406) is optimized to obtain decisions (154) for the promotion plan (128) that are constrained by the margin budget (146). The promotion plan (128), indicating the obtained decisions (154), is presented for implementation by the enterprise during the promotion event. The obtained decisions (154) include a list of the products (600), each of which is associated with one offer (204) and one price rule (206).

Abstract: A method (400) and system (100) for providing a promotion plan (128) for merchandising products (600) receives base data (142) for the products (600) that includes allowable offers (204) and price rules (206) that affect the offers (204). A margin budget (146) is established for the promotion plan (128) that defines an amount of margin an enterprise is willing to forgo for a promotion event implementing the promotion plan (128). A scenario (406) is generated in response to the base data (142). The scenario (406) is optimized to obtain decisions (154) for the promotion plan (128) that are constrained by the margin budget (146). The promotion plan (128), indicating the obtained decisions (154), is presented for implementation by the enterprise during the promotion event. The obtained decisions (154) include a list of the products (600), each of which is associated with one offer (204) and one price rule (206).

Abstract: A transmitter for a digital transmission signal includes a pre-distorter to improve linearity of a power amplifier. An amplified transmission signal is conditioned into a narrowband feedback signal that is responsive to a logarithm of the power appearing in out-of-band components of the amplified transmission signal. The feedback signal is processed in a pre-distortion processor that implements a genetic algorithm to adapt pre-distortion functions implemented in the pre-distorter and improve linearity over time. The genetic algorithm tests a population of randomly-generated pre-distortion functions for fitness. A baseline component of the coefficients from pre-distortion functions used in a subsequent population tracks the best-fit pre-distortion function from the current population, allowing the use of a limited search space. New populations are generated from old populations using an elitism process, and randomized crossover, and mutation processes.

Abstract: A constrained-envelope digital communications transmitter (10) places constraints on the envelope of spectrally constrained, digitally modulated communication signals (46) converted into diverse frequency channels (54) and combined into a composite signal (58). The envelope constraints achieve lower peak-to-average power ratio without allowing significant spectral regrowth. The composite signal (58) is applied to a plurality of cascade-coupled constrained-envelope generators (64). Each constrained-envelope generator (64) detects overpeak events (66) and configures corrective impulses (68) for the overpeak events (66). The corrective impulses (68) are distributed to the respective frequency channels (54) in accordance with a predetermined distribution profile (102) and are filtered into allocated shaped pulses (120) that exhibit a constrained spectrum.

Abstract: A power amplifier linearizer (12) includes an on-chip portion (60) and an external controller (22). The on-chip portion (60) implements two predistortion circuits (86, 88) desirably configured as look-up tables. One predistortion circuit (88) is programmed by the external controller (22) to apply a hotter linearizing translation function (54) and the other predistortion circuit (86) is programmed by the external controller (22) to apply a colder linearizing translation function (56). One or more temperature signals (24, 38) are correlated with the temperatures experienced by a power amplifier (34) and drive a power amplifier thermal modeler (50) implemented in the controller (22). As a result of running the thermal modeler (50), the controller (22) generates an interpolation signal (26) that indicates how far to interpolate between the hotter and colder translation functions (54, 56).

Abstract: A signal recognizer capable of classifying any of all commonly used communications signals. The recognizer has a number of modules that may operate in parallel, each module associated with a different signal type. Each module determines signal parameters by first estimating one or more parameters of a detected signal of interest. The estimated parameter(s) is then used as the basis for demodulating the input signal. The demodulated symbols are used for hypothesis testing, during which the module decides on a candidate signal type for that module. Each module subjects its candidate signal type to a “false alarm” test, which evaluates the likelihood that the signal is not the signal type associated with the module. The resulting confidence data is collected and analyzed to determine a best candidate signal type from among all the modules, together with its signal parameters.

Abstract: A transmitter (20) for a digital transmission signal (26) includes a pre-distorter (22) to improve linearity of a power amplifier (44). An amplified transmission signal (46) is conditioned into a narrowband feedback signal (58) that is responsive to a logarithm of the power appearing in out-of-band components (42) of the amplified transmission signal (46). The feedback signal (58) is processed in a pre-distortion processor (80) that implements a genetic algorithm (82) to adapt pre-distortion functions implemented in the pre-distorter (22) and improve linearity over time. The genetic algorithm (82) tests a population of randomly-generated pre-distortion functions for fitness (100). A baseline component of the coefficients from pre-distortion functions used in a subsequent population tracks the best-fit pre-distortion function from the current population (126), allowing the use of a limited search space.