Patents by Inventor Su-Ming Wu
Su-Ming Wu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10579602Abstract: Systems, methods, and other embodiments associated with attribute redundancy removal are described. In one embodiment, a method includes identifying redundant attribute values in a group of attributes that describe two items. The example method also includes generating a pruned group of attributes having the redundant attribute values removed. The similarity of the two items is calculated based, at least in part, on the pruned group of attribute values.Type: GrantFiled: October 31, 2013Date of Patent: March 3, 2020Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Z. Maria Wang, Su-Ming Wu
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Patent number: 10528903Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.Type: GrantFiled: January 7, 2016Date of Patent: January 7, 2020Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Aswin Kannan, Kiran Panchamgam, Su-Ming Wu
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Patent number: 10467654Abstract: Systems, methods, and other embodiments associated with forecasting customer channel choice using cross-channel loyalty are described. In one embodiment, a method includes accessing historical values for each of one or more loyalty variables for respective customers. The method also includes determining respective loyalty variable predictors for each of the one or more loyalty variables for each customer based on the historical values. In response to a trigger event associated with a given customer, the loyalty variable predictors for the customer are used to estimate a present value of each of the one or more loyalty variables for the customer. The present value of each of the loyalty variables is input to a forecast model that calculates, for each channel, a probability that the customer will make a purchase using the channel. The purchase probabilities are provided for use in selecting a marketing message for the customer.Type: GrantFiled: September 4, 2015Date of Patent: November 5, 2019Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Kiran V. Panchamgam, Su-Ming Wu
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Publication number: 20190318410Abstract: Embodiments provide a recommendation for an additional item in response to receiving a basket of goods determine a type for the basket of goods from a set of basket types, receive a set of additional targeted items as target recommendations and receive a history of received types of baskets of goods. Embodiments iteratively perform a clustering into a plurality of clusters of each of the basket types based on the history of received types of baskets of goods, and preference updating for each of the targeted items into each of the plurality of clusters. The iteratively performing, after a plurality of iterations, outputs a sequence of mappings and a sequence of preference parameters. Embodiments generate a frequency of tabulation of mappings from the sequence of mappings and then generate the recommendation based on the sequence of mappings, the sequence of preference parameters and the frequency of tabulation of mappings.Type: ApplicationFiled: April 17, 2018Publication date: October 17, 2019Inventors: Sajad MODARESI, Fernando BERNSTEIN, Denis SAURE, Setareh Borjian BOROUJENI, Su-Ming WU, Robert CORR, Nikos NIKOLAKIS
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Publication number: 20190122176Abstract: Systems, methods, and other embodiments associated with controlling inventory depletion by offering different prices to different customers are described. In one embodiment, a method includes establishing first and second allocations of fulfillment centers to different geographic regions during a markdown phase. Different price schedules are determined for the orders to be fulfilled during the markdown phase based on the first and second allocations. A predicted profit is generated for the orders fulfilled under each of the different price schedules. A price schedule corresponding to the first allocation is selected as resulting in a greater predicted profit than another one of the different price schedules. A sale terminal is controlled to enact the selected price schedule during the markdown phase to cause fulfillment of the incoming orders according to the first allocation of the fulfillment centers.Type: ApplicationFiled: October 23, 2018Publication date: April 25, 2019Inventors: Su-Ming WU, Andrew VAKHUTINSKY
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Publication number: 20170323333Abstract: Systems, methods, and other embodiments associated with computing and generating schedule data structures for items in a display are described. In one embodiment, a method includes accessing a sales data structure corresponding to a store and analyzing sales records for items associated with subcategories to calculate a subcategory profit contribution score for each subcategory. The method may also include selecting a first subcategory from the subcategories as a candidate subcategory of items and analyzing the sales records to calculate an item profit contribution score for each of the items assigned to the candidate subcategory. A first item is selected from the candidate subcategory to be placed on a promotional display space, based upon the item profit contribution score of the first item. A schedule data structure is generated that assigns the first item to the promotional display space.Type: ApplicationFiled: May 1, 2017Publication date: November 9, 2017Inventors: Su-Ming WU, Mark E. FERGUSON, Olga PAK, Olga PERDIKAKI
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Publication number: 20170212921Abstract: Systems, methods, and other embodiments associated with extracting attributes from electronic data structures are described. In one embodiment, a method includes correlating tokens from description strings with defined attributes in an electronic inventory database by identifying which of the defined attributes match the tokens to link the tokens with columns of the database associated with the defined attributes. The method includes iteratively updating annotation strings for unidentified ones of the tokens by generating suggested matches for the unidentified tokens according to known correlations between identified tokens and the defined attributes using a conditional random fields model. The method also includes populating the database using the identified tokens from the description strings according to the annotation strings by automatically storing the tokens from the description strings into the columns as identified by the annotation strings.Type: ApplicationFiled: January 27, 2016Publication date: July 27, 2017Inventors: Su-Ming WU, Setareh BORJIAN BOROUJENI
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Publication number: 20170200104Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.Type: ApplicationFiled: January 7, 2016Publication date: July 13, 2017Inventors: Aswin KANNAN, Kiran PANCHAMGAM, Su-Ming WU
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Publication number: 20170200172Abstract: A system that generates a consumer decision tree receives retail item transactional sales data. The system aggregates the sales data to an item/store/time duration level and aggregates the sales data to an attribute-value/store/time duration level. The system determines sales shares for the time duration and determines similarities for attribute-value pairs based on correlations between attribute-value pairs. The system then determines a most significant attribute based on the determined similarities.Type: ApplicationFiled: January 8, 2016Publication date: July 13, 2017Inventors: Su-Ming WU, John SHIN, Kiran Venkata PANCHAMGAM
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Publication number: 20170068962Abstract: Systems, methods, and other embodiments associated with forecasting customer channel choice using cross-channel loyalty are described. In one embodiment, a method includes accessing historical values for each of one or more loyalty variables for respective customers. The method also includes determining respective loyalty variable predictors for each of the one or more loyalty variables for each customer based on the historical values. In response to a trigger event associated with a given customer, the loyalty variable predictors for the customer are used to estimate a present value of each of the one or more loyalty variables for the customer. The present value of each of the loyalty variables is input to a forecast model that calculates, for each channel, a probability that the customer will make a purchase using the channel. The purchase probabilities are provided for use in selecting a marketing message for the customer.Type: ApplicationFiled: September 4, 2015Publication date: March 9, 2017Inventors: Kiran V. PANCHAMGAM, Su-Ming WU
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Publication number: 20160210640Abstract: Systems, methods, and other embodiments associated with incrementally swapping items in an assortment are described. In one embodiment, a computing system includes demand logic configured to read data from an electronic data structure that defines an assortment. The assortment defines a subset of items from a product category. The demand logic is configured to generate forecasted changes to an associated metric value by generating demand transference values for (i) individually removing each item presently in the assortment and (ii) individually adding each item of a set of available items of the product category. The computing system includes assortment logic configured to transform the electronic data structure that defines the assortment according to the forecasted changes by incrementally swapping items in the assortment for new items in the available set of items until the forecasted changes between items in the assortment and new items in the set of available items satisfy a predefined condition.Type: ApplicationFiled: January 20, 2015Publication date: July 21, 2016Inventors: Su-Ming WU, Saraswati YAGNAVAJHALA
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Publication number: 20150127419Abstract: A system that generates an item-to-item similarity for a category that includes a plurality of products receives attribute values for each product in the category and product-store-week sales units for each product in the category. The system estimates attribute weights. The system then determines the item-to-item similarity as a weighted attribute match score.Type: ApplicationFiled: November 4, 2013Publication date: May 7, 2015Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Sandeep TIWARI, Su-Ming WU
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Publication number: 20150100554Abstract: Systems, methods, and other embodiments associated with attribute redundancy removal are described. In one embodiment, a method includes identifying redundant attribute values in a group of attributes that describe two items. The example method also includes generating a pruned group of attributes having the redundant attribute values removed. The similarity of the two items is calculated based, at least in part, on the pruned group of attribute values.Type: ApplicationFiled: October 31, 2013Publication date: April 9, 2015Inventors: Z. Maria WANG, Su-Ming WU
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Publication number: 20140358633Abstract: A demand transference forecast system receives for a category of merchandise de-promoted sales data for each of a plurality of stock keeping units (“SKUs”), similarities between each pair of SKUs in the category, and SKU-store ranging information. The system determines a sales indices of all SKUs in the category across the de-promoted sales data for the category. The system determines Total Assortment Effect (“TAE”) variable quantities for the SKUs across share intervals in the de-promoted sales data based on the sales indices and the similarities. The system then generates a single parameter based demand transference model based on the similarities, the sales indices, and ratios of the share intervals.Type: ApplicationFiled: May 31, 2013Publication date: December 4, 2014Inventors: Su-Ming WU, Sandeep TIWARI
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Patent number: 8874499Abstract: A system generates a consumer decision tree (“CDT”). The system receives customer purchasing data that includes transactions of a plurality of products each having at least one product attribute. For a product category, the system identifies a plurality of similar products from the purchasing data and one or more attributes corresponding to each similar product. The system assigns the product category as a current level of the CDT, and determines a most significant attribute of the plurality of attributes for the current level. The system forms a next level of the CDT by dividing the most significant attribute into a plurality of sub-sections, where each sub-section corresponds to an attribute value of the most significant attribute. The system then forms a next level of the CDT for each sub-section until a terminal node is identified.Type: GrantFiled: June 21, 2012Date of Patent: October 28, 2014Assignee: Oracle International CorporationInventors: Sandeep Tiwari, Peter Gaidarev, Su-Ming Wu
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Patent number: 8751289Abstract: Systems, methods, and other embodiments associated with scalable regression for retail panel are described. In one embodiment, a method includes performing a regression that estimates elasticity of demand for a retail item, wherein the regression is performed on a transformation of a demand model that does not include variables associated with base demand or seasonality. In a subsequent processing step, the method includes estimating a base demand and seasonality for the retail item based, at least in part, on the estimated elasticity of demand. The method may be performed in a database that stores retail panel data for the retail item and other retail items.Type: GrantFiled: May 5, 2011Date of Patent: June 10, 2014Assignee: Oracle International CorporationInventors: Yevgeniy Popkov, Su-Ming Wu, Manish Gupte
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Patent number: 8645188Abstract: One embodiment is directed generally to a computer system, and in particular to a system for providing automatic estimating of demand parameters. According to certain embodiments, a computer readable medium has instructions stored thereon that, when executed by a processor, cause the processor to determine a reliable demand parameter for a level within a sales hierarchy. The instructions include estimating a demand parameter for a first pool. The estimating is based on blending and comparing with respect to an enlarged pool comprising the first pool as a subset of the enlarged pool to obtain an estimated demand parameter.Type: GrantFiled: January 12, 2012Date of Patent: February 4, 2014Assignee: Oracle International CorporationInventors: Su-Ming Wu, Yevgeniy Popkov
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Publication number: 20130346352Abstract: A system generates a consumer decision tree (“CDT”). The system receives customer purchasing data that includes transactions of a plurality of products each having at least one product attribute. For a product category, the system identifies a plurality of similar products from the purchasing data and one or more attributes corresponding to each similar product. The system assigns the product category as a current level of the CDT, and determines a most significant attribute of the plurality of attributes for the current level. The system forms a next level of the CDT by dividing the most significant attribute into a plurality of sub-sections, where each sub-section corresponds to an attribute value of the most significant attribute. The system then forms a next level of the CDT for each sub-section until a terminal node is identified.Type: ApplicationFiled: June 21, 2012Publication date: December 26, 2013Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Sandeep TIWARI, Peter GAIDAREV, Su-Ming WU
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Publication number: 20130185116Abstract: A system provides automatic escalation of demand parameters to determine a reliable demand parameter for a level within a sales hierarchy. The system measures difference in demand parameters between a level of interest within the sales hierarchy and a plurality of other levels within the sales hierarchy. The system also compares the differences in demand parameters of the other levels. The system further determines an escalation path for a demand parameter based on the comparison.Type: ApplicationFiled: January 12, 2012Publication date: July 18, 2013Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Yevgeniy Popkov, Su-Ming Wu
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Publication number: 20130185117Abstract: One embodiment is directed generally to a computer system, and in particular to a system for providing automatic estimating of demand parameters. According to certain embodiments, a computer readable medium has instructions stored thereon that, when executed by a processor, cause the processor to determine a reliable demand parameter for a level within a sales hierarchy. The instructions include estimating a demand parameter for a first pool. The estimating is based on blending and comparing with respect to an enlarged pool comprising the first pool as a subset of the enlarged pool to obtain an estimated demand parameter.Type: ApplicationFiled: January 12, 2012Publication date: July 18, 2013Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Su-Ming WU, Yevgeniy POPKOV