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- Nachgewiesen in: USPTO Patent Applications
- Sprachen: English
- Document Number: 20240078591
- Publication Date: March 7, 2024
- Appl. No: 18/509157
- Application Filed: November 14, 2023
- Claim: 1. A computer-implemented method, comprising: at an online system comprising at least one processor and memory: training a machine-learned availability model for determining availabilities of items in warehouses, wherein training of the machine-learned availability model comprises: applying a plurality of training samples to train the machine-learned availability model, the plurality of training samples comprising previous delivery orders, wherein at least one training sample comprises a training label indicating whether an item was available and a plurality of characteristics associated with the item; receiving a first set of notifications regarding availabilities of a plurality of items, wherein a notification includes whether one or more items were found at a warehouse; determining that a set of items were not found for at least a threshold number of times based on the first set of notifications; retraining the machine-learned availability model based on the set of items, wherein retraining the machine-learned availability model comprises: generating an additional set of training samples using the set of items that were not found for at least the threshold number of times, receiving a second set of notifications indicating one or more items in the set of items were found in one or more warehouses, updating the additional set of training samples based on the second set of notifications, applying the additional set of training samples to the machine-learned availability model, and adjusting parameters of the machine-learned availability model based on the applying of the additional set of training samples; receiving, from a user device, a selection of a particular item that is one of the items in the set; applying the machine-learned availability model to generate a prediction of whether the particular item is available; and causing to display a result based on the prediction generated by the machine-learned availability model regarding the particular item.
- Claim: 2. The computer-implemented method of claim 1, wherein receiving the first set of notifications regarding availabilities of the plurality of items comprises: receiving an indication that a shopper has obtained at least one of the plurality of items; determining that the found one of the plurality of items is a difficult-to-find item that was not found for at least the threshold number of times; updating the training samples for the machine-learned availability model to indicate that the found one of the plurality of items is a difficult-to-find item.
- Claim: 3. The computer-implemented method of claim 1, wherein receiving the first set of notifications regarding availabilities of the plurality of items comprises: receiving an indication that a shopper could not find one of the plurality of items; designating the one of the plurality of items as an unfound item; determining that the unfound item is a difficult-to-find item that was not found for at least the threshold number of times; updating the training samples for the unfound item for the machine-learned availability model to indicate that the found one of the plurality of items is a difficult-to-find item.
- Claim: 4. The computer-implemented method of claim 1, wherein causing to display a result based on the prediction generated by the machine-learned availability model regarding the particular item comprises: prompting a shopper to provide information for finding the particular item in a warehouse; receiving a response from the shopper whether the shopper is able to find the particular item in the warehouse; and causing the user device to display an availability of the particular item in the warehouse.
- Claim: 5. The computer-implemented method of claim 4, wherein the information for finding the particular item in the warehouse comprises one or more of: a picture of a location in the warehouse of the particular item, a text description of a location in the warehouse of the particular item, or any combination thereof.
- Claim: 6. The computer-implemented method of claim 1, wherein applying the machine-learned availability model to generate a prediction of whether the particular item is available comprises: generating a predicted availability score associated with the particular item; comparing the predicted availability score to an availability threshold; and determining the prediction of whether the particular item is available based on the comparison.
- Claim: 7. The computer-implemented method of claim 1, further comprising: receiving information from a first shopper that the first shopper is able to find the particular item at the warehouse; receiving an addition order that includes the particular item; and transmitting the information to a second shopper for finding the particular item at the warehouse.
- Claim: 8. The computer-implemented method of claim 7, wherein receiving information from the first shopper comprises receiving a photo taken by the first shopper locating the particular item.
- Claim: 9. The computer-implemented method of claim 1, further comprising: receiving a threshold number of negative indications that an item is not found at the warehouse; and responsive to receiving the threshold number of negative indications, removing the item from an offering list of the online system with respect to the warehouse.
- Claim: 10. A non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: train a machine-learned availability model for determining availabilities of items in warehouses, wherein training of the machine-learned availability model comprises: applying a plurality of training samples to train the machine-learned availability model, the plurality of training samples comprising previous delivery orders, wherein at least one training sample comprises a training label indicating whether an item was available and a plurality of characteristics associated with the item; receive a first set of notifications regarding availabilities of a plurality of items, wherein a notification includes whether one or more items were found at a warehouse; determine that a set of items were not found for at least a threshold number of times based on the first set of notifications; retrain the machine-learned availability model based on the set of items, wherein retraining the machine-learned availability model comprises: generating an additional set of training samples using the set of items that were not found for at least the threshold number of times, receiving a second set of notifications indicating one or more items in the set of items were found in one or more warehouses, updating the additional set of training samples based on the second set of notifications, applying the additional set of training samples to the machine-learned availability model, and adjusting parameters of the machine-learned availability model based on the applying of the additional set of training samples; receive, from a user device, a selection of a particular item that is one of the items in the set; apply the machine-learned availability model to generate a prediction of whether the particular item is available; and cause to display a result based on the prediction generated by the machine-learned availability model regarding the particular item.
- Claim: 11. The non-transitory computer-readable medium of claim 10, wherein the instruction to receive the first set of notifications regarding availabilities of the plurality of items comprises instructions to: receive an indication that a shopper has obtained at least one of the plurality of items; determine that the found one of the plurality of items is a difficult-to-find item that was not found for at least the threshold number of times; update the training samples for the machine-learned availability model to indicate that the found one of the plurality of items is a difficult-to-find item.
- Claim: 12. The non-transitory computer-readable medium of claim 10, wherein the instruction to receive the first set of notifications regarding availabilities of the plurality of items comprises instructions to: receive an indication that a shopper could not find one of the plurality of items; designate the one of the plurality of items as an unfound item; determine that the unfound item is a difficult-to-find item that was not found for at least the threshold number of times; update the training samples for the unfound item for the machine-learned availability model to indicate that the found one of the plurality of items is a difficult-to-find item.
- Claim: 13. The non-transitory computer-readable medium of claim 10, wherein the instruction to cause to display a result based on the prediction generated by the machine-learned availability model regarding the particular item comprises instructions to: prompt a shopper to provide information for finding the particular item in a warehouse; receive a response from the shopper whether the shopper is able to find the particular item in the warehouse; and cause the user device to display an availability of the particular item in the warehouse.
- Claim: 14. The non-transitory computer-readable medium of claim 13, wherein the information for finding the particular item in the warehouse comprises one or more of: a picture of a location in the warehouse of the particular item, a text description of a location in the warehouse of the particular item, or any combination thereof.
- Claim: 15. The non-transitory computer-readable medium of claim 10, wherein the instruction to applying the machine-learned availability model to generate a prediction of whether the particular item is available comprises instructions to: generate a predicted availability score associated with the particular item; compare the predicted availability score to an availability threshold; and determine the prediction of whether the particular item is available based on the comparison.
- Claim: 16. The non-transitory computer-readable medium of claim 10, wherein the instructions, when executed, further cause the one or more processors to: receive information from a first shopper that the first shopper is able to find the particular item at the warehouse; receive an addition order that includes the particular item; and transmit the information to a second shopper for finding the particular item at the warehouse.
- Claim: 17. The non-transitory computer-readable medium of claim 16, wherein the instruction to receive information from the first shopper comprises instructions to receive a photo taken by the first shopper locating the particular item.
- Claim: 18. The non-transitory computer-readable medium of claim 10, wherein the instructions, when executed, further cause the one or more processors to: receive a threshold number of negative indications that an item is not found at the warehouse; and responsive to receiving the threshold number of negative indications, remove the item from an offering list of the online system with respect to the warehouse.
- Claim: 19. A system comprising: one or more processors; and memory configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: train a machine-learned availability model for determining availabilities of items in warehouses, wherein training of the machine-learned availability model comprises: applying a plurality of training samples to train the machine-learned availability model, the plurality of training samples comprising previous delivery orders, wherein at least one training sample comprises a training label indicating whether an item was available and a plurality of characteristics associated with the item; receive a first set of notifications regarding availabilities of a plurality of items, wherein a notification includes whether one or more items were found at a warehouse; determine that a set of items were not found for at least a threshold number of times based on the first set of notifications; retrain the machine-learned availability model based on the set of items, wherein retraining the machine-learned availability model comprises: generating an additional set of training samples using the set of items that were not found for at least the threshold number of times, receiving a second set of notifications indicating one or more items in the set of items were found in one or more warehouses, updating the additional set of training samples based on the second set of notifications, applying the additional set of training samples to the machine-learned availability model, and adjusting parameters of the machine-learned availability model based on the applying of the additional set of training samples; receive, from a user device, a selection of a particular item that is one of the items in the set; apply the machine-learned availability model to generate a prediction of whether the particular item is available; and cause to display a result based on the prediction generated by the machine-learned availability model regarding the particular item.
- Claim: 20. The system of claim 19, wherein the instruction to receive the first set of notifications regarding availabilities of the plurality of items comprises instructions to: receive an indication that a shopper has obtained at least one of the plurality of items; determine that the found one of the plurality of items is a difficult-to-find item that was not found for at least the threshold number of times; update the training samples for the machine-learned availability model to indicate that the found one of the plurality of items is a difficult-to-find item.
- Current International Class: 06
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