# US 2020/0394697 A1 · Methods and systems for artificial intelligence insights for retail location > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0394697 A1 - Document ID: US20200394697A1 - Status: Published application - Kind: A1 - Application number: 16/439,281 - Filed: 2019-06-12 - Published: 2020-12-17 - Earliest claimed: 2019-06-12 - Assignees: Shoppertrak Rct Corporation - Applicants: Shoppertrak Rct Corporation - Inventors: Michael PAOLELLA; Michelangelo PALELLA - Web page: https://patentgrep.com/patent/US20200394697A1 ## CPC - G06Q 10/0877: {by inventory control or reporting using inventory tracking or counting} - G06Q 30/0623: {by investigating goods or services} - G06N 5/02: Knowledge representation; Symbolic representation - G06N 20/00: Machine learning - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Examples described herein generally relate to a system for managing a retail environment. The system may collect data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system. The system may predict a condition based on a machine-learning model applied to a combination of the collected data from at least two systems of the plurality of retail information systems. The system may push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition. ## Claims ### Claim 1 A method of managing a retail environment, comprising: collecting data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system; predicting a condition based on at least one machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and pushing an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition. ### Claim 2 (depends on 1) The method of claim 1, further comprising: receiving a voice query, from the user having the user persona; interpreting the voice query based on the user persona to generate a machine query for a predicted condition; answering the query using the at least one machine-learning model to generate a predicted condition and a recommended action; and providing a voice response identifying the answer and the recommended action. ### Claim 3 (depends on 1) The method of claim 1, wherein predicting the condition comprises: determining a predicted rate of consumption of a product based on historical inventory levels, a historical traffic level, and a current traffic level; determining a time that a current inventory of the product will be depleted at the predicted rate of consumption; and detecting a low inventory condition if the time is within a threshold time. ### Claim 4 (depends on 1) The method of claim 1, wherein predicting the condition comprises: identifying, by the at least one machine-learning model, a pattern in the collected data; and detecting a deviation of current data collected within a threshold time period from the pattern. ### Claim 5 (depends on 4) The method of claim 4, wherein identifying the pattern comprises, correlating by the at least one machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems. ### Claim 6 (depends on 1) The method of claim 1, wherein the at least one machine-learning model is trained on training sets that are subsets of the data from the plurality of retail information systems that have been labeled with corresponding events. ### Claim 7 (depends on 6) The method of claim 6, further comprising: detecting an occurrence of an event based on business rules applied to the combination of the collected data; labeling a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate one of the training sets; and training the at least one machine learning model to classify a current combination of the collected data into events based on the training sets. ### Claim 8 A non-transitory computer readable medium storing computer executable instructions that when executed by a processor cause the processor to: collect data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system; predict a condition based on at least one machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition. ### Claim 9 (depends on 8) The non-transitory computer readable medium of claim 8, further comprising code to: receive a voice query, from the user having the user persona; interpret the voice query based on the user persona to generate a machine query for a predicted condition; answer the query using the at least one machine-learning model to generate an answer and a recommended action; and provide a voice response identifying the answer and the recommended action. ### Claim 10 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the code to predict the condition comprises code to: determine a predicted rate of consumption of a product based on a historical inventory a historical traffic level, and a current traffic level; determine a time that a current inventory of the product will be depleted at the predicted rate of consumption; and detect a low inventory condition if the time is within a threshold time. ### Claim 11 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the code to predict the condition comprises code to: identify, by the at least one machine-learning model, a pattern in the collected data; and detect a deviation of current data collected within a threshold time period from the pattern. ### Claim 12 (depends on 11) The non-transitory computer readable medium of claim 11, wherein the code to identify the pattern comprises code to correlate, by the at least one machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems. ### Claim 13 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the machine-learning model is trained on training sets that are subsets of the data from the plurality of retail information systems that have been labeled with corresponding events. ### Claim 14 (depends on 13) The non-transitory computer readable medium of claim 13, further comprising code to: detect an occurrence of an event based on business rules applied to the combination of the collected data; label a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate one of the training sets; and train the machine learning model to classify a current combination of the collected data into events based on the training sets. ### Claim 15 A system for managing a retail environment, comprising: a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system; and a computer system comprising a memory storing computer executable instructions and a processor configured to execute the instructions to: collecting data from the plurality of retail information systems; predict a condition based on a machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition. ### Claim 16 (depends on 15) The system of claim 15, wherein the processor is configured to execute the instructions to: receive a voice query, from the user having the user persona; interpret the voice query based on the user persona to generate a machine query for a predicted condition; answer the query using the machine-learning model to generate an answer and a recommended action; and provide a voice response identifying the answer and the recommended action. ### Claim 17 (depends on 15) The system of claim 15, wherein the processor is configured to execute the instructions to: determine a predicted rate of consumption of a product based on a historical inventory a historical traffic level, and a current traffic level; determine a time that a current inventory of the product will be depleted at the predicted rate of consumption; and detect a low inventory condition if the time is within a threshold time. ### Claim 18 (depends on 15) The system of claim 15, wherein the processor is configured to execute the instructions to: identify, by the machine-learning model, a pattern in the collected data; and detect a deviation of current data collected within a threshold time period from the pattern. ### Claim 19 (depends on 15) The system of claim 15, wherein the processor is configured to execute the instructions to correlate, by the machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems. ### Claim 20 (depends on 15) The system of claim 15, wherein the processor is configured to execute the instructions to: detect an occurrence of an event based on business rules applied to the combination of the collected data; label a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate a the training set; and train the machine learning model to classify a current combination of the collected data into events based on the training set.