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Entity Rules

Validating user input before processing goes a long way in improving the bot’s efficiency and user experience. While the entity type does impose some basic restrictions, these are not always sufficient and does not cater to the specific business requirements. Entity rules can be used for additional processing hints and validations.

Entity rules can be added from the Entity Rules section under Instance Properties of the corresponding entity. Add the rule in the JSON editor provided, see here for how.

These rules can also be set in the script for an entity, in such cases, the rule needs to be set before the entity prompt in the dialog flow. Add a script node at the beginning of the dialog or just before the concerned entity node with the following script:

context.entityRules.<entityName> = {
      "ruleName": "value"
   }

Subentity rules can be part of the composite entity rules:

context.entityRules.<compositeEntityName> = {
    <subentityName> : { 
      "ruleName": "value"
     }
   }

Below are the entity rules that can be entered in the JSON editor for the required entity or included as ruleName in the above script. We are continuously improving and updating this list. Any feedback or suggestions, kindly post in the community forum and our developers will accommodate it, if feasible.

Generic rules

RULENAME VALUE DESCRIPTION
allowConfirmation true/false The extracted entity value is presented to the user after each input and the flow would continue only after confirmation from the user. Currently, this rule is applicable only for LoV enumerated entity types.
Example
{
"allowConfirmation": <true/false>
}
confirmYesSynonyms <concept names> Additional words/phrases to be used to confirm an entity value.
Used in conjunction with allowConfirmation rule mentioned above.
Example
{
"confirmYesSynonyms": ["~concept1", "~concept2"]
}
where concept1: ok; concept2: affirmative
confirmNoSynonyms <concept names> Additional words/phrases to be used to cancel a confirmation entity. If selected/uttered, the entity value will be set to null
Used in conjunction with allowConfirmation rule mentioned above.
Example
{
"confirmNoSynonyms": ["~concept2", "~concept3"]
}
where concept1: nope; concept2: wrong
processLatestSentence true/false To restrict checking to the sentences only from the current volley.
Example
{
"processLatestSentence": <true/false>
}
patternsOnly true/false To restrict matching to an entity pattern alone. By default, if the given entity patterns do not result in the extraction of entity value, the platform tries to find a value from user utterance. Setting this rule to true will disable that default processing.
Example
{
"patternsOnly": <true/false>
}
preConditions user defined preconditions To define preconditions for all entity types to process entity extraction if one of the conditions is true. If the condition is invalid the entity extraction is skipped entirely.
Use Case Examples:
  • Example1: A composite entity matches with a set of identification numbers, such as membership ID, provider ID, and RX number. You can set the precondition rule as "preConditions" : ["checkMemberID"].
  • Example 2: A VA has two static list of values (LOV) in a composite entity, you can add precondition to customer specific LOV to enable it.
  • Example 3: If you don’t want to extract entity values for simple utterances, then add preconditions to skip the extraction entirely.
  • Example 4: Add a precondition for each car make type to simplify the extraction of valid car models based on the car make. The car make can be a custom concept entity with an output tag based on the extracted entity value followed by a composite of individual LoVs for each model. Each list has a pre-condition corresponding to the make.
    The pre-condition can be expressed as a context tag or trait (like an intent’s pre-condition) or when enclosed in parentheses, as a pattern. For example:
    "entityRules" : {
    "preConditions" : ["checkMemberID"]
    }
    This will only extract an entity value when the current context has a checkMemberID context tag set or a trait with that name has been found.
Example
JSON
{
"preConditions":
[ "isCustomerX" ]
} 		Entity type: LoV Entity with Premium as one of the choices. But the list is only appropriate for Customer X.
A previous script node has set the “isCustomerX” context tag.
User Utterance “check my premium account” Extracted Value “Premium”
A previous script node has not set the “isCustomerX” context tag.
User Utterance “check my premium account” Extracted Value “none”
JSON
{
"preConditions" :
[ "( buy )" ]
} 		Entity type: Number Entity
User Utterance “buy 10 apples” Extracted Value “10”
User Utterance “search for 10 apples” Extracted Value “none”

String type entity

Works identical to the Description entity type but limited to one sentence.

There won’t be any validations done on the user utterance for string entities unless trained. Hence this entity type is used as a last resort when your requirement is not met with any of the platform supported entity types.

Description entity type

RULENAME VALUE DESCRIPTION
discardAsCommand Concept Name: ~bot_commands_override_discard
Synonyms: abort, abort_all, cancel, clear_screen, discard, discard all, exit, exit_all, quit, quit_all, restart, start_over, startover, stop, stop_all 		If the rule is set to true, then the discard commands entered at a string entity node are not considered as valid string inputs, and the task gets discarded.
stripLeading <concept name> To remove the words given in the concept from the start of the extracted string.
The concept can be a single concept name or a space-separated list of concepts or an array of concept names.
Example
JSON
{ "stripLeading": [
"~stringConcept"
]
} 		stringConcept: city
Entity Pattern: I like *
User Utterance “I like city New York” Extracted Value New York
stripTrailing <concept name> To remove words in a concept from the end of the extracted string.
The value can be a single concept name or a space-separated list of concepts or an array of concept names.
Example
JSON
{ "stripTrailing":
"~stringConcept ~stringConcept1"
} 		stringConcept: city; stringConcept1: airport
Entity Pattern: I like *
User Utterance “I like New York city” or “I like New York airport” Extracted Value New York
avoidSingleWord <concept name> To ignore any value that is a member of the concept, unless it is the entire input.
The value can be a single concept name or a space-separated list of concepts or an array of concept names.
Example
JSON
{
"avoidSingleWord":
"~stringConcept"
} 		stringConcept: chess, cricket
Entity Pattern: like to watch * tournament
User Utterance “I like to watch chess tournament” Extracted Value prompt for entity value
User Utterance “I like to watch golf tournament” Extracted Value golf
User Utterance “cricket” Extracted Value cricket, since it is the entire input
avoidSingleVerb true If set to true, then any value that is just a verb is ignored, unless it is the entire input.
Example
JSON
{
"avoidSingleVerb": true
} 		Entity Pattern: I like * music
User Utterance “I like playing music” Extracted Value prompt for entity value
User Utterance “I like rap music” Extracted Value rap
User Utterance “play” Extracted Value play, since it is the entire input
extractOnlyNumbers true if true, the entity will extract only numbers present in the string and set it as the entity value
Example
JSON
{
"extractOnlyNumbers": true
}
letters number
Or
range 		To extract a word of a specific length, or a sequence of individual characters that meet the length criteria.
The rule value is either:
  • A single number, letters=3, where the word(s) matched is composed of that many alphabetic (a-z) letters.
  • A range, letters=5-8, indicates that 5, 6, 7, and 8 letters are valid.
    Words or sequences of letters having only alphabets are matched. The words containing punctuation or numbers are not considered. Also, the return value is converted to all caps.
Example
JSON
{
"letters": 2
} 		Entity Pattern – “letters = 2″
User Utterance 234 XY Extracted Value “XY
User Utterance 234 x y Extracted Value “XY
User Utterance 234 alpha bravo Extracted Value “AB This value is extracted as part of a phonetic alphabet. User Utterance 234 X Y Z Extracted Value “Nothing No alphabet matches with letters value defined.
Example
JSON
{
"letters": 5-8
} 		Entity Pattern – “letters = 5-8″
User Utterance Kore.ai is the best platform in artificial intelligence of recent times. Extracted Value “platform, recent, times

Number type entity

RULENAME VALUE DESCRIPTION
asString true To capture a number as a string, preserving leading zeros
Example
JSON
{
"asString": true
} 		Entity Pattern – by default numeric entries
User Utterance “OTP is 009944” Extracted Value “009944″ without the rule, it would have been “9944”
digits single number
Or
a range of values 		To enforce a number of digits to match in a Number entity. This rule will not ignore leading zeros. The word(s) matched are composed of that many digits after NL processing. When a range is provided for the rule, it passes only if the utterance value matches within that valid range
Example
JSON
{
"digits": "3"
} 		Entity Pattern – Numeric or string entries
User Utterance “CVV is 034” Extracted Value “034″ The value is a match as the digits = 3. If the value of the digits is 2, then it would be a no match in this example.
JSON
{
"digits": "13-17"
} 		Entity Pattern – Numeric or string entries
User Utterance “My credit card number is 4012 8888 8888 1881″ Extracted Value “4012 8888 8888 1881″ It is a match as the number of digits in the credit card number is 16, and within the range.

Note

The presence of the digits rule is an implicit asString=true and integerOnly=true, which means the entity value is always returned as a string even if the number is short and does not have a leading zero. Floating point/decimal numbers and ordinals (For example, "first", "twenty-third") are not matched.
The rule does not split a word or number. For example, if the utterance is "two thousand and twenty three" and the rule is "digits": 3, then it is a no match. The utterance "twenty twenty three" would also be a no match. The rule should be "digits": 4 for all the inputs to match.

Currency type entity

RULENAME VALUE DESCRIPTION
defaultCode <currency code>
or
<country code> 		If no code is mentioned in user input, this value will be picked as code.
The value needs to be a 3 letter currency code or two-letter country alpha-2 codes.
Example
JSON
{
"defaultCode": "NZD"
} 		Entity Pattern pay *
User Utterance “Pay 30” Extracted Value “NZD30”
User Utterance “Pay USD30” Extracted Value “USD30“
maxDigits <number> To restrict the amount length. If the amount length exceeds the value it will be discarded.
Example
JSON
{
"maxDigits": 3
} 		Entity Pattern pay *
User Utterance “Pay USD30” Extracted Value “USD30”
User Utterance “Pay USD3000” Extracted Value prompt for value
currencyCodes [<currency code>,<currency code>]
or
[<country code>,<country code>] 		To restrict the currency codes. If the user entered code is not in the given list then the value will be discarded.
Example
JSON
{
"currencyCodes": [
"USD",
"INR",
"NZD"
]
} 		Entity Pattern pay *
User Utterance “Pay USD30” Extracted Value “USD30”
User Utterance “Pay AUD30” Extracted Value prompt for a value
ignoreOnlyCode <true/false> If the value is true, the currency entity requires both the currency code/symbol AND the amount. Otherwise, the entered value is unaccepted, resulting in an error message displayed to the user.
If the value is false, the currency entity accepts the currency code or symbol even without the amount.
Example
JSON
{
"ignoreOnlyCode": true
} 		In a scenario where the rule is set to true:
Entity Pattern pay *
User Utterance “Pay USD30” Extracted Value “USD30”
User Utterance “Pay AUD” VA Response: I apologize, I did not recognize the currency you entered. Please enter the currency you would like to use. An example of currency is $ 100.

Model Number for Composite entity type

RULENAME VALUE DESCRIPTION
modelNumber <true>
or
<a character> 		To capture a model number that is composed of letters and numbers such as membership number, social security number, country of origin code, and other ticket numbers.
The rule is especially useful to capture structured data on a voice channel where a custom regex pattern is not flexible or is too cumbersome to account for every variation.
For example:
  • “My id is six twenty seven dash forty four eighty nine
  • “My seat is 20 3 sea”
  • “The account number is alpha bravo slash six two one oh seven”
  • “I was in row 35 seat d”
    Each part of the structured data is a subentity, and the composite entity patterns define the order of those elements. The modelNumber rule means that the value stored in context.entities will be a concatenated string of the matched subentities instead of a JSON object.
Example
JSON
{
"modelNumber": true
} 		Entity Pattern – @AccountNumbers @AccountSuffix
@AccountNumbers is a Number entity with “digits”:8 entity rule.
@AccountSuffix is a String entity with “letters”:2 entity ruleUser Utterance ““Account Number is 3 3 1 3 4 2 1 2 X Y”
Extracted Value ““33 13 42 12 X Y”User Utterance ““Account Number is 33134212 – XY””
Extracted Value ““33134212XY”“User Utterance ““Account Number is 33 13 42 12 alpha bravo”
Extracted Value “ “33134212AB”“
User Utterance ““Account Number is thirty three thirteen forty two twelve a like apple b as in boy” Extracted Value “ “33134212AB”” Composite Patterns – {row} @RowNumber {seat} @Seat
@RowNumber is a Number entity with “digits”:2 and “range”: (“from”:1,”to”,35} entity rules.
@Seat is a Custom concept entity using a concept ~seatletters ( A B C D E F )
User Utterance ““I am sitting in 33 A” Extracted Value “ “33A”“
User Utterance “““I am in row thirty three seat sea” Extracted Value “ “33C”“
composite number <single number> or <a range of values> To extract a composite number using the Letters and Number entities. This rule will extract a number composed of subgroups with a specific number of digits and number of letters. For example:
  • A provider ID could be 8 digits and 1 or 2 letters
  • An SSN is 3 digits then 2 digits then 4 digits
  • A Jira ticket is a project id plus a number.
  • An airline seat is 1 or 2 digits and 1 letter (though not all 2 digits or all letters).

Note

A modelNumber composite entity is a voice-compatible replacement for a Custom regex entity.
A regex is a strict character-matching expression, the input must be an exact match that generally works on text-based channels though those expressions can become complicated when optional elements like punctuation are involved – users don't necessarily know if they have to provide them or not.
On voice channels, an ASR can (mis)transcribe speech, for example "C" becomes "sea" or "see". A user could pause so "23" becomes "20 3". The Number and String entity types, along with the digits and letters entity rules, can handle all of these variations.
The example above of ( @AccountNumbers @AccountSuffix ) can replace a regex liked{8}-?[a-zA-Z]{2}

PersonName type entity

RULENAME VALUE DESCRIPTION
disablePatterns An array of person name patterns to ignore – currently supporting only “possessive“ To disable patterns in extracting a person’s name when not applicable in specific scenarios.
Example
JSON
{
"disablePatterns": [
"possessive"
]
} 		Entity Pattern – by default capitalized words
User Utterance “schedule Bob’s review at 9 am” Extracted Value “Bob“
ignoreWords <concept name> Words in the concept are not to be considered as names even when capitalized.
The concept name could be a space-separated list of concepts or an array of concepts.
Example
JSON
{
"ignoreWords": [
"review",
"~prepositionList"
]
} 		Entity Pattern – by definition capitalized words
User Utterance “meeting for Bob Review” Extracted Value “Bob” would have been “Bob Review” without the rule
negativePatterns array of patterns Person names can be general capitalized words, or a name can be used in a sense where it is not meant to be taken as a person.
Example
JSON
{
"negativePatterns": [
"about *"
]
} 		Entity Pattern – by definition capitalized words
User Utterance “schedule a meeting about Philip with Fred” Extracted Value “Fred” would have been “Philip” without the rule

Company type entity

RULENAME VALUE DESCRIPTION
ignoreWords <concept name> Words in the concept are not to be considered as companies even when capitalized.
The concept name could be a space-separated list of concepts or an array of concepts.
Example
JSON
{
"ignoreWords": [
"atm"
]
} 		Entity Pattern – by definition capitalized words
User Utterance “find ATM” Extracted Value none would have been “ATM” (an Italian company) without the rule
negativePatterns An array of company name patterns to ignore To disable patterns in extracting a company name when not applicable in specific scenarios.

Date type entity

RULENAME VALUE DESCRIPTION
range { “from” : <from-date>, “to” : <to-date> } To extract dates only in the specified range. Either endpoint is optional.
Values can be a date, YYYY-MM-DD, or a keyword: today, tomorrow, yesterday. The dates are inclusive.
Example
JSON
{
"range": {
"from": "2020-01-01",
"to": "today"
}
} 		Entity Pattern – by default date patterns
User Utterance “show schedule for 2019-02-03” Extracted Value prompt for a value
User Utterance “show schedule for tomorrow” Extracted Value prompt for a value
User Utterance “show schedule for 2020-02-03” Extracted Value “2020-02-03”
referenceDate <date> Sets the reference date to <date> and any date calculation to set the current date entity value is based on that date.
Values should be a date, YYYY-MM-DD, or a keyword: today, tomorrow, yesterday.
Example
JSON
{
"referenceDate": "2020-07-09"
} 		Entity Pattern – by default date patterns
User Utterance “schedule after two days” Extracted Value “2020-07-11” would have been two days from the current date without the reference date
preferredDateFormat “yyyy-mm-dd” In case of ambiguity in date, it uses the preferred date format to resolve ambiguity.
Values can be:
  • “yyyy-mm-dd”
  • “yyyy-dd-mm”
  • “dd-mm-yyyy”
  • “mm-dd-yyyy”
Example
JSON
{
"preferredDateFormat": "mm-dd-yyyy"
} 		User Utterance “03-04-2021”
Extracted Value “2021-03-04”
NOTE: This rule only applies when there is an ambiguity in the user utterance. If the user’s preference is already set as part of a previous conversation, then the user selected format would take precedence over the defined rule
For example, a per the previous use case, if the user had selected the preferred format to be “dd-mm-yyy” previously in the conversation then the date would be taken as “2021-04-03”
returnOnlyMonthYear <true/false> When set to true, the platform would capture the month and year alone from the user input and update the context object accordingly. Even if the user gives a full input (like 20 Oct 2019), the system would only take Oct 2019 from that input. The user will not be prompted for the date if not provided
Example
JSON
{
"returnOnlyMonthYear": true
} 		User Utterance “03-04-2021”
Extracted Value “04-2021”User Utterance “Apr 2021”
Extracted Value “04-2021”

Date Period type entity

RULENAME VALUE DESCRIPTION
range { “from” : <from-date>, “to” : <to-date> } To extract dates in the specified range. Either endpoint is optional.
Values can be a date, YYYY-MM-DD, or a keyword: today, tomorrow, yesterday. The dates are inclusive.
Example
JSON
{
"range": {
"from": "2020-01-01",
"to": "today"
}
} 		Entity Pattern – by default date patterns
User Utterance “show schedule for 2019-02-03” Extracted Value prompt for a value
User Utterance “show schedule for tomorrow” Extracted Value prompt for a value
User Utterance “show schedule for 2020-02-03” Extracted Value “2020-02-03”
range with strict { “from” : <from-date>, “to” : <to-date>,
“strict”: <true/false> } 		If the value is true, the Date Period entity accepts values that are strictly in the defined date range.
If the value is false, the Date Period entity accepts values that are in the range or overlapping with the range. 		Examples JSON
{
"range": {
"from": "2022-01-01",
"to": "2023-03-05",
"strict" = "true"
}
} 		User Utterance: “2022-05-05 – 2022-06-05”
Expected Value: “Selected date range”
(The user utterance is within the defined date range. As strict is true, any value strictly in the defined date range is a valid input and is accepted.)User Utterance: “2021-05-05 – 2022-05-05”
Extracted Value: “I’m sorry, I am unable to recognize the string. Please enter again.”
(The user utterance is not strictly in the defined date range, it’s overlapping. As strict is true, any overlapping input is an invalid input and is rejected with an error.) 		JSON
{
"range": {
"from": "2022-01-01",
"to": "2023-03-05",
"strict" = "false"
}
} 		User Utterance: “2021-05-05 – 2022-05-05”
Expected Value: “Selected date range”
(The user utterance is overlapping with the defined date range. As strict is false, any overlapping input is a valid input and is accepted.) 		referenceDate <date> Sets the reference date to <date> and any date calculation to set the current date entity value is based on that date.
Values should be a date, YYYY-MM-DD, or a keyword: today, tomorrow, yesterday. 		Example JSON
{
"referenceDate": "2020-07-09"
} 		Entity Pattern – by default date patterns
User Utterance “schedule after two days” Extracted Value “2020-07-11” 		tense past/future To adjust the date period based on the tense when the year is not present in the user utterance.
Without this rule, the year is set as the current year if the month/day falls within 90 days from the current date else it would be set to the previous year. With this rule, you can force the year to current or past. 		Example JSON
{
"tense": "past"
} 		Entity Pattern – by default date patterns
User Utterance “get the statement for Jan” Extracted Value “2020-01-01” if the current date is “2020-15-12” without the rule, it would have been “2021-01-01”
preferredDateFormat “yyyy-mm-dd” In case of ambiguity in date, it uses the preferred date format to resolve ambiguity.
Values can be:
  • “yyyy-mm-dd”
  • “yyyy-dd-mm”
  • “ddmmyyyy”
  • “mmddyyyy”
Example JSON
{
"preferredDateFormat": "mm-dd-yyyy"
} 		User Utterance “03-04-2021”
Extracted Value “2021-03-04”
NOTE: This rule only applies when there is an ambiguity in the user utterance. If the user’s preference is already set as part of a previous conversation, then the user selected format would take precedence over the defined rule
For example, a per the previous use case, if the user had selected the preferred format to be “dd-mm-yyy” previously in the conversation then the date would be taken as “2021-04-03”

DateTime type entity

RULENAME VALUE DESCRIPTION
range { “from” : <from-datetime>, “to” : <to-datetime> } To extract date-times only in the specified range. Either endpoint is optional.
Values can be a date, YYYY-MM-DD, or a DateTime, YYYY-MM-DDTHH:MM:SS (user’s timezone is assumed if none specified) or a keyword: today, tomorrow, yesterday, now. The dates are inclusive.
Example
JSON
{
"range": {
"from": "2020-01-01T00:00:00+05:30",
"to": "2020-10-01T00:00:00+05:30"
}
} 		Entity Pattern – by default date time patterns
User Utterance “set alarm for 2019-02-03T10:00:00” Extracted Value prompt for a value
User Utterance “show schedule for 2021-12-20T10:00:00” Extracted Value prompt for a value
User Utterance “show schedule for 2020-02-03 T10:00:00” Extracted Value “2020-02-03T10:00:00”
preferredTimes { “from” : <from-time>,
“to” : <to-time> } 		To set the preferred time which can be used to interpret times when they are ambiguous. These times would be applicable to all days of the week.
For example, “3” will become “3 pm” if the preferredTimes are 9 am to 6 pm.
If the time cannot be placed inside the range then it will pick am/pm based on which is closest. If there are two possibilities or equal distance then the “daylight/standard awake” hours will be used. The times should be ISO 8601 format, THH:MM
{ “from” : [],
“to” : [] } 		This option is for setting preferred times different for different days of the week. The “from” and “to” keys can be arrays with 7 members representing the time for each day, from Sunday to Saturday. Each value should be in the in Thh:MM format or an empty string to indicate there is no preference.
{ “favor” : <keyword> } The “favor” keyword can be used to set the preference in terms of “future”, “past”, “am”, or “pm”.
Example
JSON
"preferredTimes": {
"from": "T12:00:00",
"to": "T18:00:00"
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”
JSON
"preferredTimes": {
"from": [
"", "T09:00",
"T09:00", "T21:00",
"T21:00", "T07:00", ""
],
"to": [
"", "T18:00",
"T18:00", "T06:00",
"T06:00", "T16:00", ""
]
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”, if the current day is Monday/Tuesday/Friday; “T03:00:00”, if the current day is Wednesday/Thursday
JSON
"preferredTimes": {
"favor": "pm"
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”
timeRangePossible true/false If true then the dialog will be looking for time range in the user input.
Influences how “10 to 4” is interpreted. By default, this will be “3:50” but if there is the possibility of a range then will be decoded as 2 times “10:00” and “16:00”
Example
JSON
{
"timeRangePossible": "true"
}
preferredDateFormat “yyyy-mm-dd” In case of ambiguity in date, it uses the preferred date format to resolve ambiguity.
Values can be:
  • “yyyy-mm-dd”
  • “yyyy-dd-mm”
  • “ddmmyyyy”
  • “mmddyyyy”
Example
JSON
{
"preferredDateFormat": "mm-dd-yyyy"
} 		User Utterance “03-04-2021”
Extracted Value “2021-03-04”
NOTE: This rule only applies when there is an ambiguity in the user utterance. If the user’s preference is already set as part of a previous conversation, then the user selected format would take precedence over the defined rule
For example, a per the previous use case, if the user had selected the preferred format to be “dd-mm-yyy” previously in the conversation then the date would be taken as “2021-04-03”

Time type entity

RULENAME VALUE DESCRIPTION
preferredTimes { “from” : <from-time>,
“to” : <to-time> } 		To set the preferred time which can be used to interpret times when they are ambiguous. These times would be applicable to all days of the week.
For example, “3” will become “3 pm” if the preferredTimes are 9 am to 6 pm.
If the time cannot be placed inside the range then it will pick am/pm based on which is closest. If there are two possibilities or equal distance then the “daylight/standard awake” hours will be used. The times should be ISO 8601 format, THH:MM
{ “from” : [],
“to” : [] } 		This option is for setting preferred times different for different days of the week. The “from” and “to” keys can be arrays with 7 members representing the time for each day, from Sunday to Saturday. Each value should be in the in Thh:MM format or an empty string to indicate there is no preference.
{ “favor” : <keyword> } The “favor” keyword can be used to set the preference in terms of “future”, “past”, “am”, or “pm”.
Example
JSON
"preferredTimes": {
"from": "T12:00:00",
"to": "T18:00:00"
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”
JSON
"preferredTimes": {
"from": [
"", "T09:00",
"T09:00", "T21:00",
"T21:00", "T07:00", ""
],
"to": [
"", "T18:00",
"T18:00", "T06:00",
"T06:00", "T16:00", ""
]
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”, if the current day is Monday/Tuesday/Friday; “T03:00:00”, if the current day is Wednesday/Thursday
JSON
"preferredTimes": {
"favor": "pm"
} 		Entity Pattern – by default date patterns
User Utterance “remind me at 3” Extracted Value “T15:00:00”
timeRangePossible true/false If true then the dialog will be looking for time range in the user input.
Example
JSON
{ “timeRangePossible”: “true” } 		Influences how utterances like “10 to 4” is interpreted. By default, this will be “3:50” but if there is the possibility of a range then will be decoded as 2 separate time values “10:00” and “16:00”
range { “from” : “now”,
}
or
{ “to” : “now”
} 		To extract date-times only in the specified range. Either endpoint is optional.
Currently, support is extended to the value “now” alone
Example
JSON
{
"range": {
"from": "now"
}
}
timeOnly true/false Rule to include or exclude the character “T” and the offset value in the time.
Possible values are:
  • True – Obtains only the time and excludes the character “T” and the offset value.
  • False – Obtains the time along with character T and the offset value.
    Note: The timeOnly rule supports multilang excluding English, German, French and Spanish.
    For example:
    • User Input: 6 pm
    • Result (with “timeOnly” rule): 18:00:00
    • Result (without “timeOnly” rule): T18:00:00+5:50
Example
JSON
{
"timeOnly": "true"
}

City type entity

RULENAME VALUE DESCRIPTION
ignoreWords <concept name> For the words in the concept not to be considered as cities.
The concept name could be a space-separated list of concepts or an array of concepts or space-separated strings.
Example
JSON
{
"ignoreWords": "Send"
} 		Entity Pattern – by default capitalized words
User Utterance “Send destination to my email” Extracted Value prompt for entity value, without the rule it would have been “Send” since it is a city name

Zip Code type entity

RULENAME VALUE DESCRIPTION
preferredCountries [<“country1”>,<“country2”>,..] To restrict zip code from the given set of preferred countries along with the user location country and the gleaned countries from the input.
Need to add two-letter country alpha-2 codes in place of <“country1”>,<“country2”>,…
Example
JSON
{
"preferredCountries": [
"GB"
]
} 		Entity Pattern – by default zip code patterns
User Utterance “check delivery to PO16 7GZ” Extracted Value “PO16 7GZ“, without the rule this might have been ignored if the user is not in the UK region

Location type entity

RULENAME VALUE DESCRIPTION
preferredCountries [<“country1”>,<“country2”>,..] To restrict location from the given set of preferred countries along with the user location country and the gleaned countries from the input.
Need to add two-letter country alpha-2 codes in place of <“country1”>,<“country2”>,…
Example
JSON
{
"preferredCountries": [
"GB"
]
}

List of Values (LoV) type entity

RULENAME VALUE DESCRIPTION
ignoreLemmaWords true/
false 		If ignoreLemmaWords is true, lemmatization is not performed on the given user input in the LoV lookup entity to predict the output. If it is set to false, lemmatization is performed.
Example
JSON
{
"ignoreLemmaWords": false
}
{
"ignoreLemmaWords": true
} 		Input Options – “ticket”, “tickets”.”
User Utterance “I need a ticket,” “I need tickets.”
Extracted Value When ignoreLemmaWords is set to false, the LoV entity extracts “ticket” for both the inputs – “ticket” and “tickets”. When it is set to true, the entity extracts “ticket” only when the input contains “ticket”. For a different input like “tickets”, the bot responds that it is not a valid option.
ignoreWords The rule for the list of values (LOV) entity type to extend support to ignore words defined in the stop words or ignore words list.
This rule helps to ignore words that are not part of every choice and if a particular word is either causing a specific match or an ambiguity. It assesses the common synonyms (common meaning present in more than one choice) and boost partial match score for same choice.
Syntax
{
"ignoreWords": [
""
]
}
Note: You can enter multiple synonyms as a comma-separated list of words to be ignored.
Example
JSON
{
"ignoreWords": [
"~size"
]
} 		Input Options – “~extra large size t-shirt”, “~large size t-shirt”, “~medium size t-shirt”, “~short size t-shirt”. “~child t-shirt, “~ men t-shirt” “~women t-shirt”
User Utterance – “I need extra large size t-shirt”, “I need large size t-shirt.”, “I need medium size t-shirt” “I need t-shirt for men”
Extracted Value – When ignoreWords is set to size, the LoV entity ignores the word “size” in the user input.

List of Items (enum) type entity

RULENAME VALUE DESCRIPTION
ownership include/
exclude 		To look for potential “ownership” phrasing, e.g. “is mine” to determine if a potential choice should be included or excluded from the entity value.
Examples of ownership phrases are “is mine”, “belongs to me”, etc.
Example
JSON
{
"ownership": "include"
} 		Input Options – Pick your item: “pen”, “watch”, “bottle”, “book”, “cap”
User Utterance “first two are mine” Extracted Value [“pen”, “watch”]
JSON
{
"ownership": "exclude"
} 		Input Options – Pick your item: “pen”, “watch”, “bottle”, “book”, “cap”
User Utterance “first two are mine” Extracted Value [“bottle”, “book”, “cap”], since the rule is to exclude the ownership values.
includeWords <concept name> or array of words The list of words that supplement to the ownership phrases.
The value can be an array of strings or a concept.To be used along with ownership: include rule
Example
JSON
{
"ownership": "include",
"includeWords": "great"
} 		Input Options – Pick your item: “pen”, “watch”, “bottle”, “book”, “cap”
User Utterance “first two are mine” Extracted Value [“pen”, “watch”]
User Utterance “first two are great” Extracted Value [“pen”, “watch”]
excludeWords <concept name> or array of words The list of words that can be used as non-ownership phrases.
The value can be an array of strings or a concept. To be used along with ownership: exclude rule
Example
JSON
{
"ownership": "exclude",
"excludeWords": "~lovConcept"
} 		Input Options – Pick your item: “pen”, “watch”, “bottle”, “book”, “cap”
lovConcept – dubiousUser Utterance “first two are dubious”
Extracted Value [“bottle”, “book”, “cap”]
noIndexMatch true To disable the alphabet and number Index match, would not allow the user to select the items using an index.
Example
JSON
{
"noIndexMatch": "true"
} 		Input Options – Pick your item: “pen”, “watch”, “bottle”, “book”, “cap”
User Utterance a Extracted Value prompt for input, without the rule it would have been [“pen”]