A computer-implemented method for generating a machine-learning model can include receiving, at a computing device having one or more processors, a plurality of reported phone numbers from telephone users, a plurality of posted phone numbers from one or more websites, and transcriptions of messages associated with a plurality of calling phone numbers. The machine-learning model is generated based on these various inputs and stored at the computing device. The model is configured to determine a probability that an unknown phone message is unwanted based on a phone number from which the unknown phone message originated.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: receiving, at a computing device having one or more processors, a plurality of reported phone numbers from telephone users, each of the plurality of reported phone numbers being identified by one of the telephone users as being a source of unwanted phone messages; receiving, at the computing device, a plurality of posted phone numbers from one or more websites, each of the websites being identified as a directory of sources of unwanted phone messages; receiving, at the computing device, transcriptions of messages associated with a plurality of calling phone numbers, each transcription including textual data content of a message originating from one of the plurality of calling phone numbers; identifying, at the computing device, one or more of the calling phone numbers as potential sources of unwanted phone messages based on the transcriptions; generating, at the computing device, a machine-learning model based on: (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers identified as potential sources of unwanted phone messages, the machine-learning model being configured to determine a probability that an unknown phone message is unwanted based on a phone number from which the unknown phone message originated; and storing, at the computing device, the machine learning model. 2. The computer-implemented method of claim 1, wherein at least one of the transcriptions is generated from a voicemail message via speech-to-text. 3. The computer-implemented method of claim 1, further comprising: receiving, at the computing device, one or more call logs associated with a plurality of logged phone numbers, each call log including an indication that the logged phone numbers generated a phone message during a period of time; and identifying, at the computing device, one or more of the logged phone numbers as potential sources of unwanted phone messages based on the call logs, wherein the machine-learning model is generated based on the one or more logged phone numbers identified as potential sources of unwanted phone messages. 4. The computer-implemented method of claim 1, further comprising identifying, at the computing device, the one or more websites as a directory of sources of unwanted phone messages. 5. The computer-implemented method of claim 1, further comprising receiving, at the computing device, a reliability metric for each of the one or more websites; and generating, at the computing device, a weight for each of the plurality of posted phone numbers based on the reliability metrics, wherein the machine-learning model is further based on the weight for each of the plurality of posted phone numbers. 6. The computer-implemented method of claim 1, wherein the model is configured to determine the probability that the unknown phone message is unwanted based on the phone number from which the unknown phone message originated for phone numbers that are absent from (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers. 7. The computer-implemented method of claim 1, further comprising: generating, at the computing device, a database of phone numbers based on the model, the database including a plurality of phone numbers associated with a plurality of probabilities, each of the plurality of phone numbers being associated with one of the plurality of probabilities, each of the plurality of probabilities representing a likelihood that a phone message originating from its associated phone number is unwanted; and providing, from the computing device, the database of phone numbers to a plurality of computing devices for use. 8. The computer-implemented method of claim 1, further comprising: receiving, at the computing device, an indication of an unknown phone message to a user, the indication including an originating telephone number; determining, at the computing device, a classification of the originating telephone number based on the model, the classification being one of a probable source of unwanted phone messages and an improbable source of unwanted phone messages; and routing, from the computing device, the unknown phone message to the user based on the classification of the originating telephone number and a message routing policy of the user. 9. A computer system, comprising: one or more computing devices, each of the computing devices including one or more processors; and a non-transitory, computer readable medium storing instructions that, when executed by the one or more processors, cause the computer system to perform operations comprising: receiving a plurality of reported phone numbers from telephone users, each of the plurality of reported phone numbers being identified by one of the telephone users as being a source of unwanted phone messages; receiving a plurality of posted phone numbers from one or more websites, each of the websites being identified as a directory of sources of unwanted phone messages; receiving transcriptions of messages associated with a plurality of calling phone numbers, each transcription including textual data content of a message originating from one of the plurality of calling phone numbers; identifying one or more of the calling phone numbers as potential sources of unwanted phone messages based on the transcriptions; generating a machine-learning model based on: (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers identified as potential sources of unwanted phone messages, the machine-learning model being configured to determine a probability that an unknown phone message is unwanted based on a phone number from which the unknown phone message originated; and storing the machine learning model. 10. The computer system of claim 9, wherein at least one of the transcriptions is generated from a voicemail message via speech-to-text. 11. The computer system of claim 9, wherein the operations further comprise: receiving one or more call logs associated with a plurality of logged phone numbers, each call log including an indication that the logged phone numbers generated a phone message during a period of time; and identifying one or more of the logged phone numbers as potential sources of unwanted phone messages based on the call logs, wherein the machine-learning model is generated based on the one or more logged phone numbers identified as potential sources of unwanted phone messages. 12. The computer system of claim 9, wherein the operations further comprise identifying the one or more websites as a directory of sources of unwanted phone messages: 13. The computer system of claim 9, wherein the operations further comprise: receiving a reliability metric for each of the one or more websites; and generating a weight for each of the plurality of posted phone numbers based on the reliability metrics, wherein the machine-learning model is further based on the weight for each of the plurality of posted phone numbers. 14. The computer system of claim 9, wherein the model is configured to determine the probability that the unknown phone message is unwanted based on the phone number from which the unknown phone message originated for phone numbers that are absent from (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers. 15. The computer system of claim 9, wherein the operations further comprise: generating a database of phone numbers based on the model, the database including a plurality of phone numbers associated with a plurality of probabilities, each of the plurality of phone numbers being associated with one of the plurality of probabilities, each of the plurality of probabilities representing a likelihood that a phone message originating from its associated phone number is unwanted; and providing the database of phone numbers to a plurality of computing devices for use. 16. The computer system of claim 8, wherein the operations further comprise: receiving an indication of an unknown phone message to a user, the indication including an originating telephone number; determining a classification of the originating telephone number based on the model, the classification being one of a probable source of unwanted phone messages and an improbable source of unwanted phone messages; and routing the unknown phone message to the user based on the classification of the originating telephone number and a message routing policy of the user. 17. A computer-implemented method, comprising: receiving, at a computing device having one or more processors, a plurality of reported phone numbers from telephone users, each of the plurality of reported phone numbers being identified by one of the telephone users as being a source of unwanted phone messages; receiving, at the computing device, a plurality of posted phone numbers from one or more websites, each of the websites being identified as a directory of sources of unwanted phone messages; receiving, at the computing device, transcriptions of messages associated with a plurality of calling phone numbers, each transcription including textual data content of a message originating from one of the plurality of calling phone numbers; identifying, at the computing device, one or more of the calling phone numbers as potential sources of unwanted phone messages based on the transcriptions; generating, at the computing device, a machine-learning model based on: (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers identified as potential sources of unwanted phone messages, the machine-learning model being configured to determine a probability that an unknown phone message is unwanted based on a phone number from which the unknown phone message originated; generating, at the computing device, a database of phone numbers based on the model, the database including a plurality of phone numbers associated with a plurality of probabilities, each of the plurality of phone numbers being associated with one of the plurality of probabilities, each of the plurality of probabilities representing a likelihood that a phone message originating from its associated phone number is unwanted; receiving, at the computing device, an indication of an unknown phone message to a user, the indication including an originating telephone number; determining, at the computing device, a classification of the originating telephone number based on the database of phone numbers, the classification being one of a probable source of unwanted phone messages and an improbable source of unwanted phone messages; and routing, from the computing device, the unknown phone message to the user based on the classification of the originating telephone number and a message routing policy of the user. 18. The computer-implemented method of claim 17, wherein the database of phone numbers includes phone numbers that are absent from (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers. 19. The computer-implemented method of claim 18, further comprising: determining, at the computing device, a classification of each phone number in the database of phone numbers based on the model, the classification being one of a probable source of unwanted phone messages and an improbable source of unwanted phone messages, wherein at least one of the phone numbers that are absent from (i) the plurality of reported phone numbers, (ii) the plurality of posted phone numbers, and (iii) the one or more calling phone numbers is classified as a probable source of unwanted phone messages. 20. The computer-implemented method of claim 17, further comprising: receiving, at the computing device, a reliability metric for each of the one or more websites; and generating, at the computing device, a weight for each of the plurality of posted phone numbers based on the reliability metric, wherein the machine-learning model is further based on the weight for each of the plurality of posted phone numbers.