Example embodiments of systems and methods for telephone call regulation include the use of a predictive model. An example method includes receiving, by a predictive model, a telephone number from a user application comprising instructions for execution on a user device and determining, by the predictive model, a spam factor for the received telephone number. The example method further includes determining, by the predictive model, a classification of the received telephone number based on the spam factor and transmitting, by the predictive model, the classification of the received telephone number to the user application. The example method further includes receiving, by the predictive model, a feedback from the user application, the feedback indicative of a corrective classification of the received telephone number and modifying, by the predictive model, the spam factor for the received telephone number based on the corrective classification.

The present disclosure describes receiving a trigger operation indication that content has been selected by a user device, and determining whether the content offers a recurring audio content data. The operation may also include retrieving a first audio content and transmitting the first audio content to the user device.

Implementations are directed to using an assistant to initiate automated telephone calls with entities. Some implementations identify an item of interest, identify a group of entities associated with the item, and initiate the calls with the entities. During a given call with a given entity, the assistant can request a status update regarding the item, and determine a temporal delay before initiating another call with the given entity to request a further status update regarding the item based on information received responsive to the request. Other implementations receive a request to perform an action on behalf of a user, identify a group of entities that can perform the action, and initiate a given call with a given entity. During the given call, the assistant can initiate an additional call with an additional entity, and generate notification(s), for the user, based on result(s) of the given call and/or the additional call.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting spoof communications are disclosed. In one aspect, a method includes the actions of receiving data identifying a first computing device and a phone number of the first computing device. The actions further include generating an NFT that includes metadata that includes the data identifying the first computing device and the phone number of the first computing device. The actions further include receiving data indicating that a second computing device with the phone number is placing a telephone call and data identifying the second computing device. The actions further include accessing the NFT. The actions further include comparing the data identifying the second computing device to the data identifying the first computing device included in the metadata of the NFT. The actions further include determining whether the phone number is likely being spoofed.

System and method for handling a transaction between a waiting party and queuing party include an independent communication system (ICS) managing calls between the waiting party and calling party for handling sensitive data as well as call-attached data. The ICS manages the transaction in different stages and with different levels of sensitivity. Either party is allowed to modify the call or call preferences during the transaction. The ICS works independently from the queuing party calling system.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing a phone number list are disclosed. In one aspect, a method includes the actions of receiving, by a computing device, telephone call data that reflects characteristics of telephone calls placed and received by a first user. Based on the telephone call data, the actions further include generating, by the computing device, a first telephone number whitelist for the first user. The actions further include determining, by the computing device, whether to combine the first telephone number whitelist and a second telephone number whitelist for a second user. The actions further include storing, by the computing device, the first telephone number whitelist or the combined telephone number whitelist in association with the first user.

Telephone calls can be validated using blockchains. In one example, a computing node can generate a distributed ledger indicating relationships between telephone numbers and unique identifiers assigned to the telephone numbers. The distributed ledger can be accessible to a telecommunication system for validating telephone calls.

The disclosed techniques enable selective forwarding and blocking of messages directed to an alias email address based on a whitelist, as well as email alerts triggered by emails from unauthorized senders. More generally, the disclosed techniques enable an enterprise system to store contact emails for users (i.e., alias email addresses) while avoiding storing and managing personal email addresses for the user. For example, the enterprise system may forward personal email addresses to an aliasing server configured to generate alias email addresses based on the personal email addresses. The aliasing server may operate as a “middle man” that receives emails directed to the alias email addresses and that forwards the emails to the personal email addresses (when appropriate). The enterprise system may store and maintain the alias email addresses in lieu of the personal email addresses.

Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

One example method of operation may include identifying a call from a calling device destined for a mobile device, responsive to identifying a calling device number assigned to the calling device, accessing a call content application programming interface (API), operated by a content delivery device, configured to deliver to the mobile device a first caller identification name (CNAM) or a second CNAM, assigned to the calling device number, determining a context assigned to a mobile device number assigned to the mobile device, selecting one of the first CNAM and the second CNAM based on the context, and assigning the selected CNAM to the call.