AD GENERATION WITH NEURAL NETWORKS
Ads are generated based on product info and consumer profiles. A discriminator evaluates probabilities of ads being effective at causing consumer engagement. A decoder extracts product info from generated ads. Based on the probabilities of ads being effective and similarity of extracted and source product info, generated ads are labeled as examples. The examples are used in training an improved ad generator. Ads may be visual and/or audio containing speech. Ads may even contain humor, as recognized by mismatches between source and decoded product info.
1 . A system comprising:
an ad generator that generates ads based on input info about products;
a discriminator that predicts probabilities that generated ads could be effective;
a decoder that extracts, from the generated ads, info about products in the generated ads; and
a database of examples of ads that have a probability of being effective above a threshold and have an acceptable match between the input info and the extracted info;
wherein the examples could be used to train an improved ad generator.
2 . The system of claim 1 wherein the probabilities that generated ads could be effective is with respect to probabilities that consumers would engage if presented with the generated ads.
3 . The system of claim 1 further comprising a database of example ads that do not have a probability of being effective above the threshold.
4 . The system of claim 1 further comprising a database of example ads that do not have an acceptable match between the input info and the extracted info.
5 . The system of claim 1 wherein the ads are visual and extracting info about products comprises object recognition.
6 . The system of claim 1 wherein the ads are audible and extracting info about products comprises automatic speech recognition.
7 . The system of claim 1 wherein a match between the input info and the extracted info is acceptable even if there is a humorous mismatch between parameters of the product info.
8 . The system of claim 1 wherein the ad generator customizes the generated ads based on individual consumer profiles.
9 . A method comprising:
generating ads based on input info about products;
predicting probabilities that the generated ads could be effective;
extracting info about products from the generated ads;
labeling the generated ads as examples based on the probabilities that the generated ads could be effective and similarity between the input info and extracted info about products; and
providing the examples as training data for an improved ad generator.
10 . The method of claim 9 wherein the probabilities that generated ads could be effective is with respect to probabilities that consumers would engage if presented with the generated ads.
11 . The method of claim 9 wherein labels are non-Boolean, the method further comprising training an improved ad generator using a regression algorithm.
12 . The method of claim 9 wherein the ads are visual and extracting info about products comprises object recognition.
13 . The method of claim 9 wherein the ads are audible and extracting info about products comprises automatic speech recognition.
14 . The method of claim 9 wherein the similarity between the input info and the extracted info is acceptable even if there is a humorous mismatch between parameters of the product info.
15 . The method of claim 9 further comprising training an improved ad generator, wherein training includes applying consumer profiles and wherein predicting probabilities that the generated ads could be effective is based on the consumer profiles.