1. A networked system comprising an autocomplete system, the networked system further comprising:
one or more computer processors and storage devices configured to:
store a record of received user inputs in an autocomplete database as part of an autocomplete model;
associate each user input of the record of received user inputs with one or more categories and with one of a plurality of time segments of a repeated considered time period in order to to build a temporal model capturing temporal demand for the one or more categories, wherein the plurality of time segments comprises a boosted time segment, and wherein during the boosted time segment a first category of the one or more autocomplete categories is associated with a deviation from an average number of user inputs for the repeated considered time period;
receive a partial user input from a first client system during the boosted time segment of the repeated considered time period; and
create a set of autocomplete results using the temporal model, wherein the set of autocomplete results is based at least in part on an identification of the partial user input being received during the boosted time segment.
2. The system of claim 1 wherein the considered time period is one year;
wherein each time segment of the plurality of time segments is one week;
wherein the boosted time segment is identified by:
for each category of the one or more categories, identifying a percentage of all user inputs associated with that category for each time segment of the repeated considered time period;
identifying one or more time segments of the plurality of time segments where the probability of all user inputs associated with a first category deviates from an expected value by more than a threshold amount; and
selecting the one or more time segments where the probability of all user inputs associated with the first autocomplete category deviates from the expected value by more than the threshold amount as the boosted time segment.
3. The system of claim 2 wherein the boosted time segment is further associated with a triggering event.
4. The system of claim 3 wherein the triggering event is a holiday date, wherein the boosted time segment comprises an annual period from a fixed number of weeks prior to the holiday date through the week of the holiday date.
5. The system of claim 1 wherein the boosted time segment is associated with an annual start date and an annual end date.
6. The system of claim 1 wherein the boosted time segment is identified by a weekly start day and time and a weekly end day and time.
7. The system of claim 1 wherein each category of the one or more categories is identified, at least in part, by metadata received with each user input of the record of received user inputs.
8. The system of claim 1 wherein each category of the one or more categories is a lowest level leaf category within a category tree structured with multiple tiers of categories;
wherein each lowest level leaf category within the category tree is associated with one or more search strings within a database; and
wherein associating each user input of the record of received user inputs with the one or more categories comprises accessing the database and comparing each user input with the one or more search strings of each lowest level leaf category to generate the association between the one or more categories and each user input.
9. The system of claim 8 wherein the one or more processors are further configured to:
associate the one or more categories and the partial user input using a first set of match scores ranking an expected relationship between the partial user input and the one or more autocomplete categories; and
wherein the first set of match scores is weighted based on a determination that the first category of the one or more autocomplete categories receives the greater than average number of user inputs during the boosted time segment when compared with the average number of user inputs for the considered time period.
10. The system of claim 9 wherein each user input of the record of received user inputs is associated with the one or more categories based on a match scoring, the match scoring comparing characters of a query string for each user input and metadata associated with each user input with metadata for each lowest level leaf category and the one or more search strings of each lowest level leaf category to generate a set of match scoring results for each user input of the record of received user inputs.
11. A method performed by an autocomplete computing device comprising one or more processors and a memory, the method comprising:
storing a record of received user inputs in an autocomplete database as part of an autocomplete model;
associating each user input of the record of received user inputs with:
one or more categories; and
a time segment of a plurality of time segments of a repeated considered time period to build a temporal model capturing temporal demand;
identifying an autocomplete event time period from the record of received user inputs, wherein the autocomplete event time period comprises a first time period which is within the repeated considered time period, and wherein a first category of the one or more autocomplete categories receives a greater number of user inputs during the autocomplete event time period than an average number of user inputs for the considered time period;
receiving a partial user input from a first client system during the autocomplete event time period; and
associating the one or more categories and the partial user input using a first set of match scores ranking an expected relationship between the partial user input and the one or more autocomplete categories, wherein the first set of match scores is weighted based on the determination that the first category of the one or more autocomplete categories receives the greater number of user inputs than the average number of user inputs for the considered time period.
12. The method of claim 11 further comprising:
creating a set of autocomplete results based at least in part on the identification of the autocomplete event time period in response to the partial user input; and
communicating the set of autocomplete results to the first client device.
13. The method of claim 12 further comprising:
receiving, from the first client device, a complete user input associated with the partial user input following communication of the set of autocomplete results to the first client device;
comparing the complete user input with the set of autocomplete results;
updating the record of received user inputs with the partial user input and the complete user input; and
updating an association of the partial user input and the complete user input with the one or more categories based on the comparison of the complete user input with the set of autocomplete results.
14. The system of claim 13 wherein each category of the one or more categories is a lowest level leaf category within a category tree structured with multiple tiers of categories;
wherein each lowest level leaf category within the category tree is associated with one or more search strings within a database; and
wherein associating each user input of the record of received user inputs with the one or more categories comprises accessing the database and comparing each user input with the one or more search strings of each lowest level leaf category to generate the association between the one or more categories and each user input.
15. The system of claim 14 wherein each user input of the record of received user inputs is associated with the one or more categories based on a match scoring, the match scoring comparing characters of a query string for each user input and metadata associated with each user input with metadata for each lowest level leaf category and the one or more search strings of each lowest level leaf category to generate a set of match scoring results for each user input of the record of received user inputs.
16. A non-transitory computer readable medium comprising computer readable instructions that, when executed by one or more processors, causes a device to:
store a record of received user inputs in an autocomplete database as part of an autocomplete model;
associate each user input of the record of received user inputs with:
metadata associated with one or more categories; and
a time segment of a plurality of time segments of a repeated considered time period to build a temporal model capturing temporal demand for the metadata;
identifying, with the temporal model, an autocomplete event time period from the record of received user inputs, wherein the autocomplete event time period comprises a first time period which is within the repeated considered time period, and wherein a first category of the one or more autocomplete categories receives a greater number of user inputs during the autocomplete event time period than an average number of user inputs for the repeated considered time period;
receive a partial user input from a first client system during an autocomplete event time period of the repeated considered time period;
compare the partial user input with one or more historical search query strings associated with each category of the one or more categories;
select a first category of the one or more categories based on:
the comparison of the partial user input with the one or more historical search query strings associated with each category; and
an identification that the autocomplete event is within the repeated considered time period using the temporal model;
create a set of autocomplete results based on the selection of the first category using the temporal model.
17. The non-transitory computer readable medium of claim 16 wherein the instructions further cause the device to:
receive, from the first client device, a complete user input associated with the partial user input following communication of the set of autocomplete results to the first client device;
compare the complete user input with the set of autocomplete results; and
update the record of received user inputs with the partial user input and the complete user input; and
update an association of the partial user input and the complete user input with the one or more categories based on the comparison of the complete user input with the set of autocomplete results.
18. The non-transitory computer readable medium of claim 17 wherein the instructions further cause the device to:
receive a second partial user input prior to receiving the complete user input;
compare the second partial user input with the one or more historical search query strings associated with each category of the one or more categories;
update the selection of the first category of the one or more categories based on the comparison of the second partial user input with the one or more historical search query strings associated with each category; and
create a second set of autocomplete results based on the update of the selection of the first category using the temporal model.
19. The non-transitory computer readable medium of claim 16 wherein the second partial user input is a prefix string of the complete user input, comprising a character match to a first portion of the complete user input; and
wherein the partial user input is a second prefix string of the second partial user input.
20. The non-transitory computer readable medium of claim 16 wherein the autocomplete time period is further associated with a triggering event comprising a holiday date, wherein the autocomplete time period comprises an annual period from a fixed number of weeks prior to the holiday date through the week of the holiday date.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of displaying timing perturbation analysis information comprising the steps of:
determining frequency offset and frequency drift rate values by low pass filtering an input signal below a demarcation frequency; and
providing a Cartesian plot of frequency offset versus frequency drift rate, the Cartesian plot including limits for the frequency offset and frequency drift rate values that define a central bounding box.
2. The method as recited in claim 1 further comprising the step of highlighting values for the frequency offsetdrift rate when the values are outside the central bounding box.
3. The method as recited in claim 1 further comprising the step of recursively updating the values in the Cartesian plot with a set integration time.
4. The method as recited in claim 1 further comprising the step of accumulating the values in the Cartesian plot until cleared to provide infinite persistence for program quality control testing.
5. A method of displaying frequency offset and frequency drift rate data produced by a wander measurement instrument comprising the steps of:
calculating the frequency offset and frequency drift rate data by low pass filtering an input signal below a demarcation frequency; and
displaying the frequency offset and frequency drift rate data on a Cartesian plot of frequency offset versus frequency drift rate, the Cartesian plot including limits for the frequency offset and frequency drift rate values that define a central bounding box.
6. A wander measurement instrument having a display according to the method of claim 1.