System and method of integrating social trends in assortment planning

1. A trend aggregation system, comprising:

computer comprising a processor and a memory, the computer configured to:

receive an initial set of images as image data from an imaging sensor;

transform analog image values from each image of the initial set of images via a RGB model to digital image values wherein each pixel of each image is identified as a value corresponding to a red channel, a value corresponding to a green channel and a value corresponding to a blue channel;

store each of the transformed initial set of images as digital RGB data in a three-dimensional matrix;

learn by train a neural network model using unsupervised learning to identify one or more product categories and one or more product attributes based on at least the digital image values;

identify one or more attribute values from the digital image values using the learned neural network model;

quantify a social affinity score of one or more items based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment; and

update the learning of the neural network model to identify at least one additional product attribute based, at least in part, on one or more additional images indicating an updated trend on a social media feed.

2. The trend aggregation system of claim 1 , wherein the computer is further configured to:

receive an image of at least one additional item;

identify a product attribute from the image of the at least one additional item; and

assign an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

3. The trend aggregation system of claim 2 , wherein the computer is further configured to:

access attribute values associated with the at least one additional item from a database;

access a uniform attribute coding for the supply chain entity; and

modify the attribute values associated with the at least one additional item based, at least in part, on the assigned attribute value and the uniform attribute coding for the supply chain entity.

4. The trend aggregation system of claim 3 , wherein the computer is further configured to:

identify one or more image features from the initial set of images using a convolutional neural network; and

assign each of the one or more image features with the one or more attribute values identified from the initial set of images.

5. The trend aggregation system of claim 4 , wherein the computer identifies the product attribute from the image of the at least one additional item using the one or more image features identified from the initial set of images.

6. The trend aggregation system of claim 5 , wherein the computer is further configured to:

assign the social affinity score to a combination of one or more product attribute values.

7. The trend aggregation system of claim 6 , wherein the computer is further configured to:

receive a combination of one or more product attributes planned for inclusion in a product assortment; and

calculate the social affinity score for a combination of one or more product attribute values for the one or more planned product attributes.

8. A computer-implemented method, comprising:

receiving, using a computer comprising a processor and a memory, an initial set of images as image data from an imaging sensor;

transforming analog image values from each image of the initial set of images via a RGB model to digital image values wherein each pixel of each image is identified as a value corresponding to a red channel, a value corresponding to a green channel and a value corresponding to a blue channel;

storing each of the transformed initial set of images as digital RGB data in a three-dimensional matrix;

learning by a neural network model using unsupervised learning to identify one or more product categories and one or more product attributes based on at least the digital image values;

identifying, using the computer, one or more attribute values from the digital image values using the learned neural network model;

quantifying, using the computer, a social affinity score of one or more items based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment; and

updating, using the computer, the learning of the neural network model to identify at least one additional product attribute based, at least in part, on one or more additional images indicating an updated trend on a social media feed.

9. The computer-implemented method of claim 8 , further comprising:

receiving an image of at least one additional item;

identifying a product attribute from the image of the at least one additional item; and

assigning an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

10. The computer-implemented method of claim 9 , further comprising:

accessing attribute values associated with the at least one additional item from a database;

accessing a uniform attribute coding for a supply chain entity; and

modifying the attribute values associated with the at least one additional item based, at least in part, on the assigned attribute value and the uniform attribute coding for the supply chain entity.

11. The computer-implemented method of claim 10 , further comprising:

identifying one or more image features from the initial set of images using a convolutional neural network; and

assigning each of the one or more image features with the one or more attribute values identified from the initial set of images.

12. The computer-implemented method of claim 11 , wherein identifying the product attribute from the image of the at least one additional item comprises using the one or more image features identified from the initial set of images.

13. The computer-implemented method of claim 12 , further comprising:

assigning the social affinity score to a combination of one or more product attribute values.

14. The computer-implemented method of claim 13 , further comprising:

receiving a combination of one or more product attributes planned for inclusion in a product assortment; and

calculating the social affinity score for a combination of one or more product attribute values for the one or more planned product attributes.

15. A non-transitory computer-readable medium embodied with software, the software when executed by a processor, cause the processor:

receives an initial set of images as image data from an imaging sensor;

transforms analog image values from each image of the initial set of images via a RGB model to digital image values wherein each pixel of each image is identified as a value corresponding to a red channel, a value corresponding to a green channel and a value corresponding to a blue channel;

stores each of the transformed initial set of images as digital RGB data in a three-dimensional matrix;

learns by a neural network model using unsupervised learning based on at least the digital image values;

identifies one or more attribute values from the digital image values using the learned neural network model;

quantifies a social affinity score of one or more items based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment; and

updates the learning of the neural network model to identify at least one additional product attribute based, at least in part, on one or more additional images indicating an updated trend on a social media feed.

16. The non-transitory computer-readable medium of claim 15 , the processor further:

receives an image of at least one additional item;

identifies a product attribute from the image of the at least one additional item; and

assigns an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

17. The non-transitory computer-readable medium of claim 16 , the processor further:

accesses attribute values associated with the at least one additional item from a database;

accesses a uniform attribute coding for a supply chain entity; and

modifies the attribute values associated with the at least one additional item based, at least in part, on the assigned attribute value and the uniform attribute coding for the supply chain entity.

18. The non-transitory computer-readable medium of claim 17 , the processor further:

identifies one or more image features from the initial set of images using a convolutional neural network; and

assigns each of the one or more image features with the one or more attribute values identified from the initial set of images.

19. The non-transitory computer-readable medium of claim 18 , wherein identifies the product attribute from the image of the at least one additional item comprises using the one or more image features identified from the initial set of images.

20. The non-transitory computer-readable medium of claim 19 , the processor further:

assigns the social affinity score to a combination of one or more product attribute values.