GENERATING WORK PRODUCTS USING WORK PRODUCT METRICS AND PREDICTED CONSTITUENT AVAILABILITY

• US 20160048853A1
• Filed: 08/14/2014
• Published: 02/18/2016
• Est. Priority Date: 08/14/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bhattacharjya, Debarun, Varshney, Kush R., Varshney, Lav R.
• Granted Patent: US 10,467,638 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A method includes predicting availability of a plurality of constituents for one or more future epochs, obtaining one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent, and generating at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products. The metrics for the existing work products may include quality metrics and novelty metrics.

Claims

1. A method comprising:

predicting availability of a plurality of constituents for one or more future epochs;

obtaining one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent; and

generating at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products;

wherein the predicting, obtaining and generating are performed by at least one processing device comprising a processor coupled to a memory.

2. The method of claim 1, wherein the constituents comprise raw materials for food dishes and the existing work products and generated work products comprise menus of one or more food dishes.

3. The method of claim 1, wherein the constituents comprise events and the existing work products and generated work products comprise itineraries.

4. The method of claim 1, wherein the constituents comprise raw materials for clothing and the existing work products and generated work products comprise catalogs of clothing items.

5. The method of claim 1, wherein predicting availability comprises:

receiving information on current availability of the constituents for a current epoch;

receiving information on historical availability of the constituents for one or more previous epochs;

creating a probability distribution of availability for each constituent for each of the one or more future epochs based on the received information on current availability and historical availability of the constituents.

6. The method of claim 5, wherein creating the probability distribution is further based on a supply chain configuration for the constituents.

7. The method of claim 5, wherein creating the probability distribution is further based on one or more of delivery times for the constituents, seasonal constraints relating to availability of the constituents and climate and weather constraints relating to availability of the constituents.

8. The method of claim 5, wherein the probability distribution comprises a stochastic forecast of availability for each constituent for each of the one or more future epochs.

9. The method of claim 5, wherein creating the probability distribution uses at least one of a time series model and a regression forecast.

10. The method of claim 1, wherein the one or more metrics comprises quality metrics and novelty metrics.

11. The method of claim 10, wherein the quality metrics are obtained using human hedonic perception scoring metrics based on historical data regarding the constituents and the existing work products.

12. The method of claim 10, wherein the novelty metrics are obtained based on a temporal Bayesian surprise metric.

13. The method of claim 10, wherein the novelty metrics evolve over respective ones of the future epochs.

14. The method of claim 10, wherein obtaining the one or more metrics further comprises determining a creativity metric using a computational creativity model that utilizes the quality metrics and novelty metrics.

15. The method of claim 1, wherein generating the at least one work product comprises:

building an optimization model, the optimization model comprising a sequential decision problem for two or more future epochs; and

utilizing a backward recursion over the two or more future epochs to solve the optimization model;

wherein the optimization model is formulated to maximize total expected profits over time subject to two or more constraints, the two or more constraints including an availability-based constraint determined using the predicted availability of the constituents and a creativity-based constraint determined using the obtained metrics.

16. The method of claim 15, wherein the creativity-based constraint specifies that creativity of the generated work product exceeds a user-defined threshold.

17. The method of claim 15, wherein the availability-based constraint specifies that the amounts of constituents used by the generated work product in a given epoch does not exceed predicted availability of the constituents for the given epoch.

18. The method of claim 1, wherein generating the at least one work product for each of the one or more future epochs further comprises generating corresponding inventory orders, a given inventory order specifying respective amounts of constituents to order in a given epoch to satisfy the generated work product associated with the given epoch.

19. An article of manufacture comprising a computer readable storage medium for storing computer readable program code which, when executed, causes a computer to:

predict availability of a plurality of constituents for one or more future epochs;

obtain one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent; and

generate at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products.

20. An apparatus comprising:

a memory; and

a processing device operatively coupled to the memory and configured to implement;

a forecasting module configured to predict availability of a plurality of constituents for one or more future epochs;

an assessment module configured to obtain one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent; and

a generation module configured to generate at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products.