# US 11,909,745 B2 · Systems and methods for establishing sender-level trust in communications using sender-recipient pair data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,909,745 B2 - Document ID: US11909745B2 - Status: Granted - Kind: B2 - Application number: 17/937,563 - Filed: 2022-10-03 - Published: 2023-01-26 - Granted: 2024-02-20 - Earliest claimed: 2018-09-28 - Assignees: Yahoo Assets LLC - Applicants: Yahoo Assets LLC - Inventors: Lachlan A. Maxwell; Donald J. Mcqueen; Paul S. Rock - Web page: https://patentgrep.com/patent/US11909745B2 ## CPC - G06F 7/24: Sorting, i.e. extracting data from one or more carriers, rearranging the data in numerical or other ordered sequence, and rerecording the sorted data on the original carrier or on a different carrier or set of carriers {sorting methods in general}(G06F7/36 takes precedence) - H04L 63/126: {the source of the received data} - G06F 7/08: Sorting, i.e. grouping record carriers in numerical or other ordered sequence according to the classification of at least some of the information they carry (by merging two or more sets of carriers in ordered sequence G06F7/16) - G06N 20/00: Machine learning - H04L 63/0236: {Filtering by address, protocol, port number or service, e.g. IP-address or URL} - H04L 63/101: {Access control lists [ACL]} - H04L 63/1433: {Vulnerability analysis} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned. ## Claims ### Claim 1 A computer-implemented method for establishing sender-level trust in future electronic communication, comprising: receiving, over a network at a server, communication data between a sender and a recipient; determining, using a trained machine learning database, whether the received communication data is associated with an expected communication and an expected communication pair data score; labeling the expected communication pair data score and the sender associated with the expected communication pair data score as trusted based on a determination that the expected communication pair data score meets or exceeds a threshold; and updating the trained machine learning database with sender and recipient communication pair data corresponding to the trusted expected communication pair data score for future communication. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: screening for trusted expected communication data by filtering future expected communication data against the communication data filters. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: generating a list of untrusted senders associated with an expected communication pair data score that does not meet or exceed the threshold. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, further comprising: analyzing the list of untrusted senders for various identifying data unique to the sender; and storing the list of untrusted senders in the trained machine learning database. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein generating an expected communication pair data score based on the expected communication data comprises: analyzing the expected communication data for known sender and recipient communication pair data, communication delivery data, feedback data, and interaction data, wherein the interaction data comprises at least information corresponding to whether the recipient replied to the sender. ### Claim 6 (depends on 4) The computer-implemented method of claim 4, further comprising: determining whether the communication transmitted by the senders corresponding with the list of untrusted senders was well-received by the recipient, by analyzing recipient feedback information. ### Claim 7 (depends on 4) The computer implemented method of claim 4, further comprising: identifying recipients that are at risk of being communicated by untrusted senders by analyzing the list of untrusted senders for sender recipient pair data. ### Claim 8 A system for establishing sender-level trust in future electronic communication, the system including a computing processor configured to execute instructions to perform a method including: receiving, over a network at a server, communication data between a sender and a recipient; determining, using a trained machine learning database, whether the received communication data is associated with an expected communication and an expected communication pair data score; labeling the expected communication pair data score and the sender associated with the expected communication pair data score as trusted based on a determination that the expected communication pair data score meets or exceeds a threshold; and updating the trained machine learning database with sender and recipient communication pair data corresponding to the trusted expected communication pair data score for future communication. ### Claim 9 (depends on 8) The system of claim 8, wherein the method further includes: screening for trusted expected communication data by filtering future expected communication data against the communication data filters. ### Claim 10 (depends on 8) The system of claim 8, wherein the method further includes: generating a list of untrusted senders associated with an expected communication pair data score that does not meet or exceed the threshold. ### Claim 11 (depends on 10) The system of claim 10, wherein the method further includes: analyzing the list of untrusted senders various identifying data unique to the sender; and storing the list of untrusted senders in the trained machine learning database. ### Claim 12 (depends on 8) The system of claim 8, wherein generating an expected communication pair data score based on the expected communication data comprises: analyzing the expected communication data for known sender and recipient communication pair data, communication delivery data, feedback data, and interaction data, wherein the interaction data comprises at least information corresponding to whether the recipient replied to the sender. ### Claim 13 (depends on 11) The system of claim 11, wherein the method further includes: determining whether the communication transmitted by the senders corresponding with the list of untrusted senders was well-received by the recipient, by analyzing recipient feedback information. ### Claim 14 (depends on 11) The system of claim 11, wherein the method further includes: identifying recipients that are at risk of being communicated by untrusted senders by analyzing the list of untrusted senders for sender recipient pair data. ### Claim 15 A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for establishing sender-level trust in future electronic communication, the method including: receiving, over a network at a server, communication data between a sender and a recipient; determining, using a trained machine learning database, whether the received communication data is associated with an expected communication and an expected communication pair data score; labeling the expected communication pair data score and the sender associated with the expected communication pair data score as trusted based on a determination that the expected communication pair data score meets or exceeds a threshold; and updating the trained machine learning database with sender and recipient communication pair data corresponding to the trusted expected communication pair data score for future communication. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the method further includes: screening for trusted expected communication data by filtering future expected communication data against the communication data filters. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the method further includes: generating a list of untrusted senders associated with an expected communication pair data score that does not meet or exceed the threshold. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the method further includes: analyzing the list of untrusted senders various identifying data unique to the sender; storing the list of untrusted senders in the trained machine learning database. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein generating an expected communication pair data score based on the expected communication data comprises: analyzing the expected communication data for known sender and recipient communication pair data, communication delivery data, feedback data, and interaction data, wherein the interaction data comprises at least information corresponding to whether the recipient replied to the sender. ### Claim 20 (depends on 18) The non-transitory computer-readable medium of claim 18, the method further comprising: determining whether the communication transmitted by the senders corresponding with the list of untrusted senders was well-received by the recipient, by analyzing recipient feedback information.