Systems and methods to identify products from verbal utterances
Some embodiments provide retail product ordering systems comprising: a user computing device comprising an application executed by a device control circuit to: receive an audible utterance; controls a product identifier application interface to: apply a tokenizer model and obtain a set of individual search words; apply a series of featurizer models to the search words to generate features; and apply a classifier and extractor model based on the features and generate multiple requested product entities each comprising a respective sub-set of the position labeled product terms; wherein the device control circuit is further configured to access a purchase history database, confirm an accuracy of each of requested product entities relative to a purchase history, generate a listing of determined product identifiers corresponding to the confirmed set of the multiple requested product entities, and control a display system of the user computing device to render the listing of determined product identifiers.
1 . A retail product ordering system comprising:
a user computing device comprising a transceiver, an audio detecting system, and an application stored in memory that, when executed by a device control circuit of the user computing device, is configured to:
receive an audible utterance of a request comprising a series of request words;
obtain a plurality of search words corresponding to the series of request words by applying a tokenizer model to the series of request words, the tokenizer model trained based on training utterance words;
generate features of the plurality of search words by applying a series of featurizer models to the set of individual search words, the series of featurizer models trained based on sets of predefined product identifiers corresponding to a respective single product;
obtain a plurality of requested product entities based on the features of the plurality of search words by applying a classifier and extractor model, the classifier and extractor model trained based on the sets of predefined product identifiers comprising product terms, each product term having position labels, wherein each requested product entity of the plurality of requested product entities comprises a respective sub-set of position labeled product terms ordered in accordance with corresponding position labels; and
generate a recommendation listing comprising recommended products, the recommended products corresponding to the plurality of requested product entities.
2 . The retail product ordering system of claim 1 , wherein generating the recommendation listing comprises:
accessing a product inventory database;
for each of the plurality of requested product entities, identifying a category of recommended products in the product inventory database; and
generating the recommendation listing, wherein the recommendation listing comprises the categories of the recommended products.
3 . The retail product ordering system of claim 2 , wherein the product inventory database corresponds to one or more retail entities.
4 . The retail product ordering system of claim 1 , wherein generating the recommendation listing comprises:
accessing purchase history data associated with a plurality of users;
for each of the plurality of requested product entities, identifying a common actual product purchased by the plurality of users from the purchase history data; and
generating the recommendation listing, wherein the recommendation listing comprises the common actual products.
5 . The retail product ordering system of claim 4 , wherein the plurality of users have one or more threshold correlations with a user submitting the audible utterance.
6 . The retail product ordering system of claim 1 , wherein the device control circuit is further configured to:
control a display system of the user computing device to render the recommendation listing of the recommended products.
7 . The retail product ordering system of claim 6 , wherein the displayed recommendation listing of the recommended products comprises a selectable option that is configured to cause, in response to selection of the selectable option by a user, product information to be displayed for each product associated with the recommended products.
8 . A method of enabling retail product ordering comprising:
receiving an audible utterance of a request comprising a series of request words;
obtaining a plurality of search words corresponding to the series of request words by applying a tokenizer model to the series of request words, the tokenizer model trained based on training utterance words;
generating features of the plurality of search words by applying a series of featurizer models to the set of individual search words, the series of featurizer models trained based on set of predefined product identifiers corresponding to a respective single product;
obtaining a plurality of requested product entities based on the features of the plurality of search words by applying a classifier and extractor model, the classifier and extractor model trained based on the sets of predefined product identifiers comprising product terms, each product term having position labels, wherein each requested product entity of the plurality of requested product entities comprises a respective sub-set of position labeled product terms ordered in accordance with corresponding position labels; and
generating a recommendation listing comprising recommended products, the recommended products corresponding to the plurality of requested product entities.
9 . The method of claim 8 , wherein generating the recommendation listing comprises:
accessing a product inventory database;
for each of the plurality of requested product entities, identifying a category of recommended products in the product inventory database; and
generating the recommendation listing, wherein the recommendation listing comprises the categories of the recommended products.
10 . The method of claim 9 , wherein the product inventory database corresponds t one or more retail entities.
11 . The method of claim 8 , wherein generating the recommendation listing comprises:
accessing purchase history data associated with a plurality of users;
for each of the plurality of requested product entities, identifying a common actual product purchased by the plurality of users from the purchase history data; and
generating the recommendation listing, wherein the recommendation listing comprises the common actual products.
12 . The method of claim 11 , wherein the plurality of users have one or more threshold correlations with a user submitting the audible utterance.
13 . The method of claim 8 , further comprising:
displaying the recommendation listing of the recommended products.
14 . The method of claim 13 , wherein the displayed recommendation listing of the recommended products comprises a selectable option that is configured to cause, in response to selection of the selectable option by a user, product information to be displayed for each product associated with the recommended products.
15 . A non-transitory computer-readable media storing a plurality of instructions, which, when executed by a processor, cause the processor to perform operations comprising:
receiving an audible utterance of a request comprising a series of request words;
obtaining a plurality of search words corresponding to the series of request words by applying a tokenizer model to the series of request words, the tokenizer model trained based on training utterance words;
generating features of the plurality of search words by applying a series of featurizer models to the set of individual search words, the series of featurizer models trained based on sets of predefined product identifiers corresponding to a respective single product;
obtaining a plurality of requested product entities based on the features of the plurality of search words by applying a classifier and extractor model, the classifier and extractor model trained based on the sets of predefined product identifiers comprising product terms, each product term having position labels, wherein each requested product entity of the plurality of requested product entities comprises a respective sub-set of position labeled product terms ordered in accordance with corresponding position labels; and
generating a recommendation listing comprising recommended products, the recommended products corresponding to the plurality of requested product entities.
16 . The non-transitory computer-readable media of claim 15 , wherein generating the recommendation listing comprises:
accessing a product inventory database;
for each of the plurality of requested product entities, identifying a category of recommended products in the product inventory database; and
generating the recommendation listing, wherein the recommendation listing comprises the categories of the recommended products.
17 . The non-transitory computer-readable media of claim 16 , wherein the product inventory database corresponds to one or more retail entities.
18 . The non-transitory computer-readable media of claim 15 , wherein the operations further comprise:
accessing purchase history data associated with a plurality of users, wherein the plurality of users have one or more threshold correlations with a user submitting the audible utterance;
for each of the plurality of requested product entities, identifying a common actual product purchased by the plurality of users from the purchase history data; and
generating the recommendation listing, wherein the recommendation listing comprises the common actual products.
19 . The non-transitory computer-readable media of claim 15 , wherein generating the recommendation listing comprises:
displaying the recommendation listing of the recommended products.
20 . The non-transitory computer-readable media of claim 19 , wherein the displayed recommendation listing of the recommended products comprises a selectable option that is configured to cause, in response to selection of the selectable option by a user, product information to be displayed for each product associated with the recommended products.