# US 10,771,574 B2 · Managing data transmissions based on a user's digital footprint > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,771,574 B2 - Document ID: US10771574B2 - Status: Granted - Kind: B2 - Application number: 16/178,982 - Filed: 2018-11-02 - Published: 2019-05-09 - Granted: 2020-09-08 - Earliest claimed: 2017-11-03 - Assignees: Lotame Solutions Inc. - Applicants: Lotame Solutions Inc. - Inventors: Omar T. Abdala; Hao D. Duong - Web page: https://patentgrep.com/patent/US10771574B2 ## CPC - G06N 20/00: Machine learning - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - H04L 41/16: using machine learning or artificial intelligence - H04L 47/70: Admission control; Resource allocation - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G06N 3/08: Learning methods - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract One exemplary system can determine a digital footprint for a user. The system can determine a first transmission pattern in which first content was transmitted to a first user device based at least in part on the digital footprint. The system can determine training data that includes a relationship between (i) one or more characteristics of the first content and (ii) the first transmission pattern. The system can then train a machine-learning-model using the training data to enable the machine-learning-model to predict a second transmission pattern in which to transmit second content that is different from the first content. The system can provide the second content as input to the machine-learning-model to obtain the second transmission pattern as output from the machine-learning-model. The system can cause the second content to be transmitted to the first user device in accordance with the second transmission pattern, which may conserve computing resources. ## Claims ### Claim 1 A system comprising: a processing device; and a memory device that includes instructions executable by the processing device for causing the processing device to: determine a digital footprint corresponding to a user, wherein the digital footprint indicates Internet activities performed by the user with a first user device and a second user device during a time period; determine, based at least in part on the digital footprint, a first transmission pattern in which first content was transmitted to the first user device during the time period, wherein the first transmission pattern resulted in the second user device performing an Internet activity corresponding to the first content; generate training data that includes a relationship between (i) one or more characteristics of the first content and (ii) the first transmission pattern; train a machine-learning-model using the training data to enable the machine-learning-model to predict a second transmission pattern in which to transmit second content that is different from the first content; provide the second content as input to the machine-learning-model to obtain the second transmission pattern as output from the machine-learning-model; and cause the second content to be transmitted to the first user device in accordance with the second transmission pattern. ### Claim 2 (depends on 1) The system of claim 1, wherein the first user device is an Internet-connected television and the second user device is a mobile phone, tablet, laptop computer, desktop computer, e-reader, or a wearable computer. ### Claim 3 (depends on 1) The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: cause the second content to be transmitted to the first user device in accordance with the second transmission pattern by transmitting data comprising the second transmission pattern to one or more computing devices, the data being configured to cause a content-transmission schedule for the second content to be adjusted to match the second transmission pattern. ### Claim 4 (depends on 1) The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: based on the second transmission pattern, cause a particular combination of computing resources to be allocated to transmitting the second content to the first user device. ### Claim 5 (depends on 1) The system of claim 1, wherein the second transmission pattern includes multiple transmissions of the second content to the first user device over the course of multiple days. ### Claim 6 (depends on 1) The system of claim 1, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to, prior to providing the second content as input to the machine-learning-model: determine, based at least in part on the digital footprint, a plurality of patterns in which a plurality of content was transmitted to the first user device during the time period, wherein the plurality of patterns resulted in the second user device performing a plurality of Internet activities corresponding to the plurality of content; and train the machine-learning-model at least in part by using the plurality of patterns as the training data. ### Claim 7 (depends on 1) The system of claim 1, wherein the Internet activity includes visiting a web site. ### Claim 8 A method comprising: determining, by a processing device, a digital footprint corresponding to a user, wherein the digital footprint indicates Internet activities performed by the user with a first user device and a second user device during a time period; determining, by the processing device and based at least in part on the digital footprint, a first transmission pattern in which first content was transmitted to the first user device during the time period, wherein the first transmission pattern resulted in the second user device performing an Internet activity corresponding to the first content; generating, by the processing device, training data that includes a relationship between (i) one or more characteristics of the first content and (ii) the first transmission pattern; training, by the processing device, a machine-learning-model using the training data to enable the machine-learning-model to predict a second transmission pattern in which to transmit second content that is different from the first content; providing, by the processing device, the second content as input to the machine-learning-model to obtain the second transmission pattern as output from the machine-learning-model; and causing, by the processing device, the second content to be transmitted to the first user device in accordance with the second transmission pattern. ### Claim 9 (depends on 8) The method of claim 8, wherein the first user device is an Internet-connected television and the second user device is a mobile phone, tablet, laptop computer, desktop computer, e-reader, or a wearable computer. ### Claim 10 (depends on 8) The method of claim 8, further comprising: causing the second content to be transmitted to the first user device in accordance with the second transmission pattern by transmitting data comprising the second transmission pattern to one or more computing devices, the data being configured to cause a content-transmission schedule for the second content to be adjusted to match the second transmission pattern. ### Claim 11 (depends on 8) The method of claim 8, further comprising: based on the second transmission pattern, causing a particular combination of computing resources to be allocated to transmitting the second content to the first user device. ### Claim 12 (depends on 8) The method of claim 8, wherein the second transmission pattern includes multiple transmissions of the second content to the first user device over the course of multiple days. ### Claim 13 (depends on 8) The method of claim 8, further comprising, prior to providing the second content as input to the machine-learning-model: determining, based at least in part on the digital footprint, a plurality of patterns in which a plurality of content was transmitted to the first user device during the time period, wherein the plurality of patterns resulted in the second user device performing a plurality of Internet activities corresponding to the plurality of content; and training the machine-learning-model at least in part by using the plurality of patterns as the training data. ### Claim 14 (depends on 8) The method of claim 8, wherein the Internet activity includes visiting a web site. ### Claim 15 A non-transitory computer-readable medium comprising program code that is executable by a processing device for causing the processing device to: determine a digital footprint corresponding to a user, wherein the digital footprint indicates Internet activities performed by the user with a first user device and a second user device during a time period; determine, based at least in part on the digital footprint, a first transmission pattern in which first content was transmitted to the first user device during the time period, wherein the first transmission pattern resulted in the second user device performing an Internet activity corresponding to the first content; generate training data that includes a relationship between (i) one or more characteristics of the first content and (ii) the first transmission pattern; train a machine-learning-model using the training data to enable the machine-learning-model to predict a second transmission pattern in which to transmit second content that is different from the first content; provide the second content as input to the machine-learning-model to obtain the second transmission pattern as output from the machine-learning-model; and cause the second content to be transmitted to the first user device in accordance with the second transmission pattern. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the first user device is an Internet-connected television and the second user device is a mobile phone, tablet, laptop computer, desktop computer, e-reader, or a wearable computer. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising program code that is executable by the processing device for causing the processing device to: cause the second content to be transmitted to the first user device in accordance with the second transmission pattern by transmitting data comprising the second transmission pattern to one or more computing devices, the data being configured to cause a content-transmission schedule for the second content to be adjusted to match the second transmission pattern. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising program code that is executable by the processing device for causing the processing device to: based on the second transmission pattern, cause a particular combination of computing resources to be allocated to transmitting the second content to the first user device. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the second transmission pattern includes multiple transmissions of the second content to the first user device over the course of multiple days. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising program code that is executable by the processing device for causing the processing device to, prior to providing the second content as input to the machine-learning-model: determine, based at least in part on the digital footprint, a plurality of patterns in which a plurality of content was transmitted to the first user device during the time period, wherein the plurality of patterns resulted in the second user device performing a plurality of Internet activities corresponding to the plurality of content; and train the machine-learning-model at least in part by using the plurality of patterns as the training data. ### Claim 21 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the Internet activity includes visiting a web site.