# US 12,001,419 B1 · Database system for triggering event notifications based on updates to database records in an electronic file > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,419 B1 - Document ID: US12001419B1 - Status: Granted - Kind: B1 - Application number: 18/178,399 - Filed: 2023-03-03 - Granted: 2024-06-04 - Earliest claimed: 2018-01-11 - Assignees: AnyQpon Inc. - Applicants: AnyQpon Inc. - Inventors: Michael Khoyilar; Piotr Szreder - Web page: https://patentgrep.com/patent/US12001419B1 ## CPC - G06F 16/2358: {Change logging, detection, and notification (replication G06F16/27)} - G06F 9/445: Program loading or initiating (bootstrapping G06F9/4401; security arrangements for program loading or initiating G06F21/57) - G06F 9/542: {Event management; Broadcasting; Multicasting; Notifications} - G06F 16/219: {Managing data history or versioning (querying versioned data G06F16/2474; querying temporal data G06F16/2477)} - G06F 16/2365: {Ensuring data consistency and integrity} - G06F 16/24565: {Triggers; Constraints} - G06F 16/9537: Spatial or temporal dependent retrieval, e.g. spatiotemporal queries - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A database processing system is disclosed for accessing databases and updated data items and triggering event notifications. The data processing system may comprise a records database including a plurality of data records, and a triggers database including a plurality of trigger indicators. The database system may further include a hardware processor that can execute computer-executable instructions in order to: receive an update on a data record, identify a type of change corresponding to change in the data record, cause an update to the records database based on information included with the updated data record, identify a trigger corresponding to the type of update to the data record, and generate an event notification including information included in the update. ## Claims ### Claim 1 A system for providing dynamic pricing based on information from a merchant's point of sale device, comprising: a memory; and one or more servers configured to: connect to a plurality of Point of Sale (POS) merchant devices of a plurality of merchants via an Application Programming Interface (API) specific to the corresponding merchant; connect to a plurality of computing devices of a plurality of users; enable the plurality of merchants to input one or more criteria to provide dynamic pricing specific to users; receive merchant related information from a corresponding POS device, wherein at least one of the one or more criteria relate to the received merchant related information, the merchant related information including a current inventory level indicating an amount of a product stored by the merchant; determine an adjustment to a price for the product associated with the merchant by applying the one or more criteria and the current inventory level indicating the amount of the product stored by the merchant to an artificial intelligence machine learning model, the current inventory level on hand being associated with the inventory level at the time the adjustment to the price is determined, the artificial intelligence machine learning model configured to determine adjustments to prices based on pricing criteria and current inventory levels, the artificial intelligence machine learning model trained to generate a higher adjustment to the price in response to a higher current inventory level; and provide the adjustment of the price to a first computing device. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more servers are configured to display a map to individual merchants of a surrounding location to the corresponding merchant and enable merchant to specify one or more geographical areas for applying the adjustment to the price. ### Claim 3 (depends on 1) The system of claim 1, wherein to determine the adjustment to the price comprises applying the merchant related information and the one or more criteria to the artificial intelligence machine learning model and determining the adjustment to the price based on the output of the artificial intelligence machine learning model. ### Claim 4 (depends on 3) The system of claim 3, wherein the one or more servers are configured to train the artificial intelligence machine learning model using historical merchant related information, historical merchant criteria, and historical conversion rates. ### Claim 5 (depends on 1) The system of claim 1, wherein the merchant related information comprises at least one of: a number of sales for the product over a time interval. ### Claim 6 (depends on 1) The system of claim 1, wherein to determine the adjustment is further based on computing device data received from the plurality of computing devices. ### Claim 7 (depends on 6) The system of claim 6, wherein the adjustment is specific to each user based on the received computing device data. ### Claim 8 (depends on 6) The system of claim 6, wherein the received computing device data comprises Global Positioning System (GPS) data of the corresponding computing device. ### Claim 9 (depends on 6) The system of claim 6, wherein the received computing device data comprises loyalty points. ### Claim 10 (depends on 9) The system of claim 9, wherein the loyalty points are specific to individual users, wherein loyalty points are accumulated based on at least one of: past purchases with the corresponding merchant, referrals to the corresponding merchant, or reviews written for the corresponding merchant. ### Claim 11 (depends on 6) The system of claim 6, wherein the received computing device data comprises user product search history or distance to a corresponding merchant. ### Claim 12 (depends on 1) The system of claim 1, wherein the merchant related information including historical price adjustments provided to a first consumer and historical purchases of a product or service with the merchant by the first consumer, wherein to determine the adjustment is further based on based on a historical conversion rate for the first consumer, the historical conversation rate for the first consumer being based on the historical purchases of the product or service with the merchant by the first consumer in response to historical price adjustments provided to the first consumer. ### Claim 13 A method comprising: connecting to a plurality of Point of Sale (POS) merchant devices of a plurality of merchants via an Application Programming Interface (API) specific to the corresponding merchant; connecting to a plurality of computing devices of a plurality of users; enabling the plurality of merchants to input one or more criteria to provide dynamic pricing specific to users; receiving merchant related information from a corresponding POS device, wherein at least one of the one or more criteria relate to the received merchant related information, the merchant related information including a current inventory level indicating an amount of a product stored by the merchant; determine an adjustment to a price for the product associated with the merchant by applying the one or more criteria and the current inventory level indicating the amount of the product stored by the merchant to an artificial intelligence machine learning model, the current inventory level on hand being associated with the inventory level at the time the adjustment to the price is determined, the artificial intelligence machine learning model configured to determine adjustments to prices based on pricing criteria and current inventory levels, the artificial intelligence machine learning model trained to generate a higher adjustment to the price in response to a higher current inventory level; and providing the adjustment of the price to a first computing device. ### Claim 14 (depends on 13) The method of claim 13, wherein the one or more criteria comprises specifying a time period. ### Claim 15 (depends on 14) The method of claim 14, wherein the time period comprises at least one of: peak hours, weekends, or holidays. ### Claim 16 (depends on 13) The method of claim 13, wherein the one or more criteria comprises a distance or proximity of a user's location to the corresponding merchant. ### Claim 17 (depends on 13) The method of claim 13, wherein the one or more criteria comprises a distance or proximity of a user's searched location to the corresponding merchant. ### Claim 18 (depends on 13) The method of claim 13, wherein the method further comprises enabling a plurality of users to join a group, wherein to determine the adjustment to the price for the product is applied to the group. ### Claim 19 (depends on 18) The method of claim 18, wherein the adjustment to the price for the product is also based on a number of users in the group. ### Claim 20 (depends on 18) The method of claim 18, wherein the method further comprises further adjusting the price for individual users in the group based on characteristics of individual user profiles. ### Claim 21 A non-transitory computer storage medium storing computer-executable instructions that, when executed by a processor of a computing system, cause the processor to perform operations comprising: connecting to a plurality of Point of Sale (POS) merchant devices of a plurality of merchants via an Application Programming Interface (API) specific to the corresponding merchant; connecting to a plurality of computing devices of a plurality of users; enabling the plurality of merchants to input one or more criteria to provide dynamic pricing specific to users; receiving merchant related information from a corresponding POS device, wherein at least one of the one or more criteria relate to the received merchant related information, the merchant related information including a current inventory level indicating an amount of a product stored by the merchant; determine an adjustment to a price for the product associated with the merchant by applying the one or more criteria and the current inventory level indicating the amount of the product stored by the merchant to an artificial intelligence machine learning model, the current inventory level on hand being associated with the inventory level at the time the adjustment to the price is determined, the artificial intelligence machine learning model configured to determine adjustments to prices based on pricing criteria and current inventory levels, the artificial intelligence machine learning model trained to generate a higher adjustment to the price in response to a higher current inventory level; and providing the adjustment of the price to a first computing device. ### Claim 22 (depends on 21) The non-transitory computer storage medium of claim 21, wherein to determine the adjustment is further based on a current date or time. ### Claim 23 (depends on 21) The non-transitory computer storage medium of claim 21, wherein to determine the adjustment is further based on external data from a database external to the merchants and users. ### Claim 24 (depends on 23) The non-transitory computer storage medium of claim 23, wherein the external data comprises weather data for an area surrounding the corresponding merchant. ### Claim 25 (depends on 23) The non-transitory computer storage medium of claim 23, wherein the external data comprises traffic data for an area surrounding the corresponding merchant. ### Claim 26 (depends on 23) The non-transitory computer storage medium of claim 23, wherein the external data comprises traffic data for an area between the user and the merchant. ### Claim 27 (depends on 21) The non-transitory computer storage medium of claim 21, wherein the one or more processors are further configured to provide merchants with a projected assessment for the adjustment to the price.