Granted Patent B2

US 12,645,871 · App. 18/474,962 · Granted Jun 2, 2026

Inventor: Ben Mackey (San Francisco, CA)

G06F40/20G06F16/335G06F40/131G06F40/166G06F40/279H04L9/3239

Abstract

Claims (75)

1 . A computer-implemented method comprising:

creating, by a computing device, a first record associated with a user identifier, the first record containing a plurality of hash values, each of the plurality of hash values corresponding to a separate one or more units of text, each of the one or more units of text having been previously input by a user;

applying, by a computing device, a similarity function to the plurality of hash values in the first record to determine a level of similarity between each pair of hash values in the plurality of hash values in the first record;

determining, by a computing device and based on a result of the applying the similarity function, a top N similar hash values among the plurality of hash values in the first record;

creating, by the computing device, a second record that contains the top N similar hash values in association with the user identifier but not all of the plurality of hash values in the first record, wherein the second record is smaller than the first record;

receiving, by the computing device, a text change in a text entry input by the user, the text change including one or more text units;

accessing, using a data store, the second record associated with a user identifier;

determining one or more hash values associated with the text change by applying a hash function over the one or more text units of the text change;

comparing each of the one or more hash values associated with the text change to the top N similar hash values in the second record associated with the user identifier; and

in response to determining a match between at least one of the one or more hash values associated with the text change and at least one of the top N similar hash values in the second record associated with the user identifier, determining a phrase suggestion for the user to add the text change to the data store for subsequent reuse and visually presenting the phrase suggestion on the computing device.

2 . The computer-implemented method of claim 1 , further comprising:

determining a plurality of levels of similarity by periodically applying the similarity function to all pairs of hash values associated with the user identifier in the data store to determine periodically an updated top N similar hash values; and

updating the second record or creating a new record in the data store, that associates the updated top N similar hash values with the user identifier.

3 . The computer-implemented method of claim 2 , further comprising ranking the plurality of levels of similarity to select the updated top N similar hash values associated with the user identifier that represent text that the user most frequently uses.

4 . The computer-implemented method of claim 2 , further comprising determining a plurality of distances for all pairs of the hash values associated with the user identifier in the data store.

5 . The computer-implemented method of claim 4 , further comprising determining that two hash values are similar when a distance is less than a predefined threshold.

6 . The computer-implemented method of claim 5 , wherein the distance includes one of a cosine distance, a Euclidean distance, or a Hamming distance.

7 . The computer-implemented method of claim 1 , further comprising:

receiving a signal to accept the phrase suggestion from the computing device; and

in response to determining the phrase suggestion is accepted, updating the data store by using the phrase suggestion.

8 . The computer-implemented method of claim 1 , further comprising calculating the one or more hash values associated with the text change by using a locality-sensitive hash (LSH) algorithm.

9 . The computer-implemented method of claim 1 , wherein the suggestion is a snippet suggestion to recommend that the computing device creates a snippet based on the phrase suggestion, the method further comprising:

sending a snippet suggestion instruction corresponding to the snippet suggestion to the computing device to present to the computing device via a user interface.

10 . A system comprising:

one or more processors; and

a memory coupled to the one or more processors comprising instructions, execution of which by the one or more processors causes the system to:

create a first record associated with a user identifier, the first record containing a plurality of hash values, each of the plurality of hash values corresponding to a separate one or more units of text, each of the one or more units of text having been previously input by a user;

apply a similarity function to the plurality of hash values in the first record to determine a level of similarity between each pair of hash values in the plurality of hash values in the first record;

determine, based on a result of applying the similarity function, a top N similar hash values among the plurality of hash values in the first record;

create a second record that contains the top N similar hash values in association with the user identifier but not all of the plurality of hash values in the first record, wherein the second record is smaller than the first record;

receive, by a computing device, a text change in a text entry input by the user, the text change including one or more text units;

access, using a data store, the second record associated with a user identifier;

determine one or more hash values associated with the text change by applying a hash function over the one or more text units of the text change;

compare each of the one or more hash values associated with the text change to the top N similar hash values in the second record associated with the user identifier; and

in response to determining a match between at least one of the one or more hash values associated with the text change and at least one of the top N similar hash values in the second record associated with the user identifier, determine a phrase suggestion for the user to add the text change to the data store for subsequent reuse and visually presenting the phrase suggestion on the computing device.

11 . The system of claim 10 , wherein execution of the instructions by the one or more processors further causes the system to:

determine a plurality of levels of similarity by periodically applying the similarity function to all pairs of hash values associated with the user identifier in the data store to determine periodically an updated top N similar hash values; and

update the second record or creating a new record in the data store, that associates the updated top N similar hash values with the user identifier.

12 . The system of claim 11 , wherein the processors are further operable when executing the instructions to rank the plurality of levels of similarity to select the updated top N similar hash values associated with the user identifier that represent text that a user associated with the user most frequently uses.

13 . The system of claim 11 , wherein execution of the instructions by the one or more processors further causes the system to implement the similarity function to calculate a plurality of distances for all pairs of the hash values associated with the user identifier in the data store.

14 . The system of claim 13 , wherein execution of the instructions by the one or more processors further causes the system to apply the similarity function to determine that two hash values are similar when a distance is less than a predefined threshold.

15 . The system of claim 14 , wherein the distance includes one of a cosine distance, a Euclidean distance, or a Hamming distance.

16 . The system of claim 10 , wherein execution of the instructions by the one or more processors further causes the system to:

receive a signal to accept the phrase suggestion from the computing device; and

in response to determining the phrase suggestion is accepted, update the data store by using the phrase suggestion.

17 . The system of claim 10 , wherein execution of the instructions by the one or more processors further causes the system to calculate the one or more hash values associated with the text change by using a locality-sensitive hash (LSH) algorithm.

18 . The system of claim 10 , wherein the suggestion is a snippet suggestion to recommend that the computing device creates a snippet based on the phrase suggestion, and wherein execution of the instructions by the one or more processors further causes the system to:

send a snippet suggestion instruction corresponding to the snippet suggestion to the computing device to present to the computing device via a user interface.

19 . One or more non-transitory computer-readable storage media storing one or more sequences of instructions, execution of which by one or more processors causes the one or more processors to execute:

creating a first record associated with a user identifier, the first record containing a plurality of hash values, each of the plurality of hash values corresponding to a separate one or more units of text, each of the one or more units of text having been previously input by a user;

applying a similarity function to the plurality of hash values in the first record to determine a level of similarity between each pair of hash values in the plurality of hash values in the first record;

determining, based on a result of the applying the similarity function, a top N similar hash values among the plurality of hash values in the first record;

creating a second record that contains the top N similar hash values in association with the user identifier but not all of the plurality of hash values in the first record, wherein the second record is smaller than the first record;

receiving a text change in a text entry input by the user, the text change including one or more text units;

accessing, using a data store, the second record associated with a user identifier,

determining one or more hash values associated with the text change by applying a hash function over the one or more text units of the text change;

comparing each of the one or more hash values associated with the text change to the top N similar hash values in the second record associated with the user identifier; and

in response to determining a match between at least one of the one or more hash values associated with the text change and at least one of the top N similar hash values in the second record associated with the user identifier, determining a phrase suggestion for the user to add the text change to the data store for subsequent reuse and visually presenting the phrase suggestion on a computing device.

20 . The one or more non-transitory computer-readable storage media of claim 19 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

determining a plurality of levels of similarity by periodically applying the similarity function to all pairs of hash values associated with the user identifier in the data store to determine periodically an updated top N similar hash values; and

updating the second record or creating a new record in the data store, that associates the updated top N similar hash values with the user identifier.

21 . The one or more non-transitory computer-readable storage media of claim 20 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

ranking the plurality of levels of similarity to select the updated top N similar hash values associated with the user identifier that represent text that the user most frequently uses.

22 . The one or more non-transitory computer-readable storage media of claim 20 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

implementing the similarity function to calculate a plurality of distances for all pairs of the hash values associated with the user identifier in the data store.

23 . The one or more non-transitory computer-readable storage media of claim 22 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

applying the similarity function to determine that two hash values are similar when a distance is less than a predefined threshold.

24 . The one or more non-transitory computer-readable storage media of claim 23 , wherein the distance includes one of a cosine distance, a Euclidean distance, or a Hamming distance.

25 . The one or more non-transitory computer-readable storage media of claim 19 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

receiving a signal to accept the phrase suggestion from the computing device; and

in response to determining the phrase suggestion is accepted, updating the data store by using the phrase suggestion.

26 . The one or more non-transitory computer-readable storage media of claim 19 , wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

calculating the one or more hash values associated with the text change by using a locality-sensitive hash (LSH) algorithm.

27 . The one or more non-transitory computer-readable storage media of claim 19 , wherein the suggestion is a snippet suggestion to recommend that the computing device creates a snippet based on the phrase suggestion, and wherein execution of the instructions by the one or more processors further causes the one or more processors to execute:

sending a snippet suggestion instruction corresponding to the snippet suggestion to the computing device to present to the computing device via a user interface.

Assignments (2)

Continuity (2)

References Cited (15)