IP Library › Granted Patent US 11,875,370

Granted Patent B2

US 11,875,370 · App. 17/126,916 · Granted Jan 16, 2024

Automated replenishment shopping harmonization

Inventors: Thomas W. Furphy (Sammamish, WA); William Justin Leigh (Seattle, WA); Umair Bashir (Issaquah, WA); Terrence Nightingale (Seattle, WA); Reda Ijaz (Seattle, WA)

Assignee: Replenium Inc.

G06Q30/0205 G06F16/26 G06N20/00 G06Q10/067 G06Q10/0838 G06Q10/0875 G06Q30/0641 G06Q10/06315

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Quick Facts

Patent No.: US 11,875,370
App. No.: 17/126,916
Granted: Jan 16, 2024
Kind: B2

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Abstract

An auto-replenishment platform may receive retailer, manufacturer, and 3 rd party consumer data on a regular time interval, via their e-commerce platforms. The auto-replenishment platform, via a harmonization engine, may aggregate all data sets, mine the aggregated data, and then cluster the data. Subsequently, the auto-replenishment platform may generate a consumer model for predicting the consumer demand for a product, factors that influence a consumer's perception of convenience or ease in purchasing that product, and for aggregating a consumer's purchased products for shipment or pickup. The auto-replenishment platform may send the consumer model to the retailer, manufacturer, and 3 rd party e-commerce platforms to integrate the auto-replenishment platform into those platforms. Additionally, the auto-replenishment platform may group a consumer's products for shipment which provides additional efficiencies for the customer and retailer/manufacturer/3 rd party in the form of time savings and/or reduced shipping and handling cost and related logistical advantages.

Claims (67)

1. One or more non-transitory computer readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising:

receiving consumer data from at least one retailer e-commerce platform for a regular time interval;

receiving consumer data from at least one manufacturer e-commerce platform for a regular time interval;

receiving consumer data from at least one 3rd party e-commerce platform for a regular time interval;

comparing the consumer data received from the at least one retailer e-commerce platform to additional consumer data previously received from the at least one retailer e-commerce platform;

comparing the consumer data received from the at least one manufacturer e-commerce platform to additional consumer data previously received from the at least one manufacturer e-commerce platform;

comparing the consumer data received from the at least one 3rd party e-commerce platform to additional consumer data previously received from the at least one 3rd party e-commerce platform;

based on comparing the consumer data received from the at least one retailer e-commerce platform to the additional consumer data previously received from the at least one retailer e-commerce platform, determining that the consumer data received from the at least one retailer e-commerce platform does not match the additional consumer data previously received from the at least one retailer e-commerce platform;

based on comparing the consumer data received from the at least one manufacturer e-commerce platform to the additional consumer data previously received from the at least one manufacturer e-commerce platform, determining that the consumer data received from the at least one manufacturer e-commerce platform does not match the additional consumer data previously received from the at least one manufacturer e-commerce platform;

based on comparing the consumer data received from the at least one 3rd party e-commerce platform to the additional consumer data previously received from the at least one 3rd party e-commerce platform, determining that the consumer data received from the at least one 3rd party e-commerce platform does not match the additional consumer data previously received from the at least one 3rd party e-commerce platform;

based on determining (i) that the consumer data received from the at least one retailer e-commerce platform does not match the additional consumer data previously received from the at least one retailer e-commerce platform, (ii) that the consumer data received from the at least one manufacturer e-commerce platform does not match the additional consumer data previously received from the at least one manufacturer e-commerce platform, and (iii) that the consumer data received from the at least one 3rd party e-commerce platform does not match the additional consumer data previously received from the at least one 3rd party e-commerce platform, aggregating the consumer data, mining the aggregated consumer data, clustering the mined consumer data and generating a consumer model at regular time intervals to determine both consumer demand for a product and factors that influence a consumer's perception of convenience or ease in purchasing the product;

aggregating shipments of a consumer's ordered products that have disparate shipping intervals for delivery or pickup as preferred by the consumer's fulfillment option or as required by a product's replenishment interval;

sending the consumer model to at least one retailer e-commerce platform for the integration of the retailer e-commerce platform and an auto-replenishment platform;

sending the consumer model to at least one manufacturer e-commerce platform for the integration of the manufacturer e-commerce platform and the auto-replenishment platform;

sending the consumer model to at least one 3rd party e-commerce platform for the integration of the 3rd party e-commerce platform and the auto-replenishment platform; and

sending the consumer model to any other party.

2. The one or more non-transitory computer readable media of claim 1 , wherein the consumer data is a record of consumer purchased goods and services for a regular time interval, wherein the consumer data includes a list of products and services, a quantity of products and services, and associated pricing paid for the products and services.

3. The one or more non-transitory computer readable media of claim 1 , wherein the consumer model is based on the consumer data received from at least one retailer e-commerce platform, at least one manufacturer e-commerce platform, and at least one 3rd party e-commerce platform.

4. The one or more non-transitory computer readable media of claim 1 , wherein the aggregating includes combining retailer, manufacturer, 3rd party data by product category, pricing or geographic location.

5. The one or more non-transitory computer readable media of claim 1 , wherein the mining includes applying a machine learning algorithm that includes the aggregated data of a retailer consumer data, a manufacturer consumer data, and a 3rd party consumer data.

6. The one or more non-transitory computer readable media of claim 1 , wherein the clustering includes grouping data by characteristics that determine the consumer demand for a product.

7. The one or more non-transitory computer readable media of claim 1 , wherein the generating a consumer model is a determination of the consumer demand for a product and factors that influence a consumer's perception of convenience or ease in purchasing that product.

8. The one or more non-transitory computer readable media of claim 1 , wherein the ease of purchasing includes grouping together products ordered by a consumer for delivery or pickup at specified intervals.

9. A system, comprising:

one or more processors; and

memory having instructions stored therein, the instructions, when executed by the one or more processors, cause the one or more processors to perform acts comprising:

receiving consumer data from at least one retailer e-commerce platform for a regular time interval;

receiving consumer data from at least one manufacturer e-commerce platform for a regular time interval;

receiving consumer data from at least one 3rd party e-commerce platform for a regular time interval;

comparing the consumer data received from the at least one retailer e-commerce platform to additional consumer data previously received from the at least one retailer e-commerce platform;

comparing the consumer data received from the at least one manufacturer e-commerce platform to additional consumer data previously received from the at least one manufacturer e-commerce platform;

comparing the consumer data received from the at least one 3rd party e-commerce platform to additional consumer data previously received from the at least one 3rd party e-commerce platform;

based on determining (i) that the consumer data received from the at least one retailer e-commerce platform does not match the additional consumer data previously received from the at least one retailer e-commerce platform, (ii) that the consumer data received from the at least one manufacturer e-commerce platform does not match the additional consumer data previously received from the at least one manufacturer e-commerce platform, and (iii) that the consumer data received from the at least one 3rd party e-commerce platform does not match the additional consumer data previously received from the at least one 3rd party e-commerce platform, aggregating the consumer data, mining the consumer data, clustering the consumer data and generating a consumer model at regular time intervals to determine both consumer demand for a product and factors that influence a consumer's perception of convenience or ease in purchasing the product;

sending the consumer model to at least one retailer e-commerce platform for the integration of a retailer e-commerce platform and an auto-replenishment platform;

sending the consumer model to at least one manufacturer e-commerce platform for the integration of the manufacturer e-commerce platform and the auto-replenishment platform;

sending the consumer model to at least one 3rd party e-commerce platform for the integration of the 3rd party e-commerce platform and the auto-replenishment platform; and

sending the consumer model to any other party.

10. The system of claim 9 , wherein the consumer data is a record of consumer purchased goods and services for a regular time interval, wherein the consumer data includes a list of products and services, a quantity of products and services, and associated pricing paid for the products and services.

11. The system of claim 9 , wherein the consumer model is based on the consumer data of at least one retailer e-commerce platform, at least one manufacturer e-commerce platform, and at least one 3rd party e-commerce platform.

12. The system of claim 9 , wherein the aggregating includes combining retailer, manufacturer, 3rd party data by product category, pricing or geographic location.

13. The system of claim 9 , wherein the mining includes applying a machine learning algorithm that includes the aggregated data of a retailer consumer data file, a manufacturer consumer data file, and a 3rd party consumer data file.

14. The system of claim 9 , wherein the clustering includes grouping data by characteristics that determine the consumer demand for a product.

15. The system of claim 9 , wherein the generating a consumer model is a determination of the consumer demand for a product and factors that influence a consumer's perception of convenience or ease in purchasing that product.

16. The system of claim 9 , wherein the ease of purchasing includes grouping together products ordered by a consumer for delivery or pickup at specified intervals.

17. A computer implemented method, comprising: