# US 2019/0318304 A1 · Cognitive fashion-ability score driven fashion merchandising acquisition

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- Number: US 2019/0318304 A1
- Document ID: US20190318304A1
- Status: Published application
- Kind: A1
- Application number: 16/438,822
- Filed: 2019-06-12
- Published: 2019-10-17
- Earliest claimed: 2018-04-11
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Mohit SEWAK
- Web page: https://patentgrep.com/patent/US20190318304A1

## CPC

- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 3/096: Transfer learning
- G06Q 10/08726: {for replenishment processing, procedures, or recommendations using forecasting or optimisation}
- G06Q 30/0631: {Recommending goods or services}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A first plurality of f-scores for a first plurality of products in an inventory are determined. The first plurality of f-scores are discretized to generate a plurality of groups, and a forecast is generated for each of the groups based on historical data associated with the first plurality of products. Projected gaps in the inventory are identified based on the forecasts. A second plurality of f-scores are determined for a second plurality of products, where each of the second plurality of products is not in the inventory. For each of the second plurality of products, a corresponding group in the plurality of groups is identified based on the second plurality of f-scores, an at least one product in the second plurality of products is selected to order, based on determining that the identified corresponding group aligns with at least one of the one or more projected gaps in inventory.

## Claims

### Claim 1

A method comprising:
determining a first plurality of f-scores for a first plurality of products based on processing a first plurality of images using one or more convolutional neural networks (CNNs), wherein each of the first plurality of images corresponds to one of the first plurality of products, and wherein each of the first plurality of products is in an inventory;
discretizing the first plurality of f-scores to generate a plurality of groups;
generating, for each of the plurality of groups, a forecast based on historical data associated with each of the first plurality of products in each respective group;
identifying one or more projected gaps in the inventory, based on the generated forecasts;
determining a second plurality of f-scores for a second plurality of products based on processing a second plurality of images with the one or more CNNs, wherein each of the second plurality of products is not in the inventory;
identifying, for each of the second plurality of products, a corresponding group in the plurality of groups based at least in part on the second plurality of f-scores; and
selecting at least one product in the second plurality of products to order, based at least in part on determining that the identified corresponding group aligns with at least one of the one or more projected gaps in inventory.

### Claim 2 (depends on 1)

The method of claim 1, wherein selecting at least one product in the second plurality of products is further based on one or more determined interaction effects between the first plurality of products, wherein the interaction effects are determined based on the associated historical data.

### Claim 3 (depends on 1)

The method of claim 1, wherein selecting at least one product in the second plurality of products is further based on a determined price elasticity of the identified corresponding group of the at least one product, wherein the price elasticity of the corresponding group is determined based on the associated historical data.

### Claim 4 (depends on 1)

The method of claim 1, wherein selecting at least one product in the second plurality of products is further based on a determined fulfillment time for the at least one product.

### Claim 5 (depends on 1)

The method of claim 1, the method further comprising:
determining that at least one of the one or more projected gaps cannot be satisfactorily filled by the second plurality of products; and
generating a recommendation to design and develop a new product that satisfies the at least one projected gap.

### Claim 6 (depends on 1)

The method of claim 1, the method further comprising:
determining, based on the generated forecast, that at least one of the plurality of groups is a target for marketing efforts; and
generating a recommendation to promote the determined group using a marketing campaign.

### Claim 7 (depends on 1)

The method of claim 1, the method further comprising:
determining that a public event has occurred, wherein the public event involves a third plurality of products;
determining an f-score for each of the third plurality of products, based on processing a third plurality of images, wherein each of the third plurality of images corresponds to one of the third plurality of products;
identifying, for each of the third plurality of products, a corresponding group in the plurality of groups based on the third plurality of f-scores; and
generating, for each of the plurality of groups, a revised forecast based at least in part on each identified corresponding group in the plurality of groups and further based on historical trends from one or more previous public events.

### Claim 8 (depends on 1)

The method of claim 1, wherein selecting at least one product in the second plurality of products further comprises determining a quantity of the at least one product to order.