Patent ID: 12204858

DETAILED DESCRIPTION

Various embodiments will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.

Examples of the disclosure provide systems, methods, and devices for filtering predictive text surfacing candidates that include precarious terms. As described herein, a “term” comprises one or more words used to describe a thing or to express a concept. A predictive text engine may receive a text input from an electronic surface. The electronic surface may comprise one or more of an application surface, a file, or an operating system shell element. As described herein, an operating system shell, or just “shell”, describes a computer interface which exposes an operating system's services to a human user or other programs. It is typically referred to as a shell because it is the outermost layer around the operating system.

In some examples, the text input may be typed or pasted into the electronic surface where it is received. In other examples, the text input may initially be received as a voice input and transformed by a speech-to-text engine to a text format. The predictive text engine may comprise one or more predictive text neural models and/or one or more predictive text n-gram models. The predictive text engine may process the text input and generate or identify one or more surfacing candidates. The surfacing candidates that are generated by the predictive text engine may comprise one or more text characters, words, or punctuation that are determined to have at least a threshold likelihood of being selected by a user for following the text input.

One or more of the surfacing candidates may be received by a predictive text filtering model. The predictive text filtering model may process the surfacing candidates and determine whether they should be filtered from further processing or whether they should be surfaced for completing the text input. The predictive text filtering model may comprise a plurality of terms that are associated in the predictive text filtering model with a precarious classification. In some examples, the precarious terms may be associated with a precarious classification type (e.g., country names, company names, nouns for ethnic groups, nouns for minority groups, nouns for targets of bias, verbs of violence, verbs of touching, and in some examples a miscellaneous classification for terms that do not meet those previously mentioned categorizations yet are still precarious in nature). Other precarious classification types are also contemplated by the systems described herein. The association may comprise a term being tagged (e.g., with metadata) with the classification or being associated with the classification in a relational database. In other examples, precarious terms may not necessarily be tagged or otherwise associated with a specific precarious category type, although they will still be associated with at least a general precarious classification.

In some examples, the plurality of precarious terms may be stored in a storage location comprising a flat file database (e.g., a database that stores data in a plain text format). In other examples, the precarious terms may be stored in a relational database. In some examples, the precarious terms may be manually identified (e.g., by an administrative user) from one or more corpora and added to a precarious term storage location. In other examples, the precarious terms may be automatically identified using one or more embedding models comprised of vectors generated from one or more corpora and added to a precarious term storage location. In additional examples, a combination of manual classification and automatic classification may be utilized to identify precarious terms.

The predictive text filtering model may further comprise a range of a number of words for filtering cooccurrences of precarious terms from the plurality of terms. The range may comprise a rule that is applied by the predictive text filtering model to a surfacing candidate, alone or in combination with a text input, that specifies that if a first precarious term in a surfacing candidate is within a specific number of words from a second precarious term (e.g., less than one word between precarious terms, less than two words between precarious terms, less than three words between precarious terms, less than four words between precarious terms, less than five words between precarious terms) in the surfacing candidate or the text input, the surfacing candidate is to be filtered from being further processed and/or surfaced unless the surfacing candidate meets a cooccurrence exception or there is a range resetting character between the two precarious terms. In additional examples, if a precarious term in a surfacing candidate is within a specific number of words from a blocklist term in the text input, the surfacing candidate may be filtered from being further processed and/or surfaced unless there is a range resetting character between the precarious term and the text input.

The predictive text filtering model may further comprise one or more cooccurrence exceptions to filtering rules. A cooccurrence exception may comprise a rule that is applied by the predictive text filtering model to a surfacing candidate, alone or in combination with a text input, that specifies that if a first precarious term in a surfacing candidate is within a specific number of words from a second precarious term in the surfacing candidate or the text input, that surfacing candidate should nonetheless not be filtered from further processing and/or being surfaced. In some examples, for a cooccurrence exception to apply, the first precarious term may have to be associated with a first specific precarious classification type, and the second precarious term may also have to be of the first specific precarious classification type.

The predictive text filtering model may further comprise one or more range resetting characters or conjunctions that reset the range of the number of words for filtering cooccurrences of precarious terms. Range resetting characters may comprise periods, commas, semi-colons, and/or colons, for example. Range resetting conjunctions may comprise conjunctions including “for”, “and”, “nor”, “but”, “or”, “yet”, and “so”, for example.

When the predictive text filtering model receives a surfacing candidate, it may determine whether the surfacing candidate, alone or in combination with the corresponding text input, includes a first precarious term and a second precarious term. If a first precarious term and a second precarious term are identified in the surfacing candidate alone or in combination with the text input, and the first precarious term and the second precarious term are within the precarious word number range of one another (e.g., within one word, within two words, within three words), the surfacing candidate may be blocked from being further processed and/or from being surfaced unless a cooccurrence exception applies or a range resetting character or conjunction is identified between the first term and the second term.

Additional examples provide systems, methods, and devices for applying a heightened degree of scrutiny to match text candidates, generated by speech-to-text engines, that are determined to potentially include offensive language. For example, a text filtering model that is the same or substantially similar to the predictive text filtering model may be applied to match text candidates generated by speech-to-text engines in a variety of scenarios (e.g., voicemail transcription, digital assistant inputs, text transcription). The text filtering model may be applied to match text candidates, and match text candidates that are determined to potentially, or likely, include offensive language based on processing performed by the text filtering model may have to meet a heightened threshold surfacing score to be surfaced compared with match text candidates that are not determined to potentially, or likely, include offensive language based on processing performed by the text filtering model.

Still further examples provide systems, methods and devices for applying a heightened degree of scrutiny to match text candidates generated from handwriting analysis models. For example, a text filtering model that is the same or substantially similar to the predictive text filtering model may be applied to match text candidates generated by handwriting analysis models that have been applied to handwritten text (e.g., handwriting received on a display, handwriting received on a tablet, handwriting received via an image). The text filtering model may be applied to match text candidates, and match text candidates that are determined to potentially, or likely, include offensive language based on processing performed by the text filtering model may have to meet a heightened threshold surfacing score to be surfaced compared with match text candidates that are not determined to potentially, or likely, include offensive language based on processing performed by the text filtering model.

In some examples, potentially offensive match text candidates generated via handwriting analysis models and/or speech-to-text models may be filtered or suppressed based on an egregiousness classification associated with the potentially offensive match text candidates. In some examples, a first egregiousness classification may comprise innocent terms, a second egregiousness classification may comprise sensitive terms (e.g., non-profane terms for body parts, diseases, repulsive terms), a third classification may comprise profane terms (e.g., curse words), and a fourth classification may comprise blocklist terms (e.g., human trafficking terms, incestuous terms, terms for terrorist activities). As such, a threshold for surfacing a match text candidate may correspond to the egregiousness classification. That is, innocent terms may have the lowest surfacing threshold, sensitive terms may have a higher surfacing threshold than innocent terms, profane terms may have a higher surfacing threshold than sensitive terms and innocent terms, and blocklist terms may have the highest threshold. In some examples, blocklist terms may never be surfaced (e.g., there is no threshold associated with them).

In examples, users may have to affirmatively opt in before their data is collected and/or processed by any of the services, engines, models, or stores described herein (e.g., by a predictive text service, by a predictive text engine, by a training module, by a speech-to-text engine). In additional examples, users may affirmatively opt out (e.g., via settings, via an automatically presented menu) from having their data collected and/or processed by any of the services, engines, models, or stores described herein. Additionally, users may delete or correct any of their data that has been collected and/or stored by any of the services, engines, models, or stores described herein. Further, user data that has been collected or stored by any of the services, engines, models, or stores described herein is subject to anonymization and other privacy-preserving practices.

The systems, methods, and devices described herein provide technical advantages for filtering offensive terms from being surfaced as predictive text suggestions. Predictive text neural models are useful in accurately identifying predictive text surfacing candidates. However, due to their neural architecture, predictive text neural models are essentially black boxes which administrative users cannot accurately predict their outputs from. Therefore, while simple blocklists can be applied to surfacing candidates output by predictive text neural models, those blocklists will almost certainly miss a wide variety of offensive surfacing candidates composed of two or more words. The predictive text filtering model described herein provides mechanisms for automatically and accurately identifying and filtering potentially offensive surfacing candidates from being surfaced by predictive text neural models through the classification of precarious terms and application of filtering rules related to those classified precarious terms.

The mechanisms described herein are also technically advantageous in relation to predictive text n-gram models. Typically, with a predictive text n-gram model a developer must go through thousands of lines of code to scrub the model from surfacing offensive surfacing candidates. This process can take developers hundreds or thousands of hours depending on the size of the model and the size of the corpora that the model has been trained on. Alternatively, the predictive text filtering model described herein can automatically and accurately identify and filter potentially offensive surfacing candidates from being surfaced by n-gram models, without manual scrubbing of models.

Another technical advantage of the current solution is that predictive text neural models and n-gram models can be trained on untrusted corpora that may more accurately reflect predictive text intent for users compared with more trusted corpora. For example, while a standard language corpus may not produce as many offensive surfacing candidates and may therefore be considered a trusted source, a corpus comprised of application and service user inputs (e.g., user inputs to a web browser application, user inputs to social media services, user inputs to messaging application) would be much more likely to be useful in accurately predicting language for a user, but would also be much more likely to lead to offensive surfacing candidates if used to train a predictive text model. However, the predictive text filtering model described herein can be used to identify and filter potentially offensive surfacing candidates even with these less trusted corpora because it accurately identifies terms to filter based on precarious classifications and the application of precarious term rules to surfacing candidates after they have been generated by the predictive text models.

FIG.1is a schematic diagram illustrating an example distributed computing environment100for filtering predictive text surfacing candidates that include precarious terms. Distributed computing environment100includes local device sub-environment102and application service sub-environment108. Local device sub-environment102includes client computing device104and mobile computing device106. Application service sub-environment108includes network and processing sub-environment110, service store120, and predictive text service132.

Network and processing sub-environment110includes network112and server computing device114, via which any and all of the computing devices described herein may communicate with one another. Server computing device114is illustrative of one or more server computing devices that may host predictive text service132and/or service store120. Although predictive text service132and service store120are illustrated as being included in in application service sub-environment108, it should be understood that one or more components of those services (e.g., engines, models, corpuses, files, saved data, application programming interfaces [APIs]) may be stored on and/or executed by one of the local computing devices (e.g., client computing device104, mobile computing device106). Similarly, the local computing devices may execute applications associated with one or more of predictive text service132and service store120.

Service store sub-environment120may include information associated with predictive text service132and/or one or more other applications or services. For example, service store sub-environment120may include predictive text filtering user preferences and settings associated with one or more applications, as well as application data associated with one or more applications. User store122may be associated with a plurality of user accounts, such as user account124. User account124may have granted predictive text service132with access to the user's application data. User account124may additionally or alternatively be associated with one or more application services. In this example, user account124is associated with an email application service, a word processing application service, and a calendar application service. As such, service store120includes electronic messages128associated with user account124, electronic documents126associated with user account124, and electronic calendar information130associated with user account124. User data store may include additional information from one or more other applications or services, such as SMS messaging applications or services, group messaging/collaboration applications or services, social media applications or services, web browser applications or services, task management applications or services, to-do list applications or services, map applications or services, reservation applications or services, game applications or services, note taking applications or services, presentation applications or services, and spreadsheet applications or services, for example. In some examples, user data store122may include information for user accounts associated with predictive text service132.

Predictive text service132includes predictive text engine134, speech-to-text engine140, predictive text filtering model146, and training module158.

Predictive text engine134includes predictive text neural model136and predictive text n-gram model138. Predictive text neural model136is illustrative of one or more neural network models that may receive a text input from an electronic surface (e.g., an application surface, a service surface, an operating system shell surface, a file surface) and generate one or more surfacing candidates based on processing the text input with the one or more neural network models. The surfacing candidates that are generated by predictive text neural model136may comprise one or more text characters, words, or punctuation that are determined to have at least a threshold likelihood of being selected by a user for following the text input. Examples of neural models that predictive text neural model136may encompass include Generative Pre-Trained Transformers (GPT) (e.g., GPT2, GPT3), Bidirectional Encoder Representations from Transformers (BERT), Embeddings from Language Models (ELMo), and/or Recurrent Neural Networks (RNN).

Predictive text n-gram model138is illustrative of one or more n-gram models that may receive a text input from an electronic surface and generate one or more surfacing candidates based on processing the text input with the one or more n-gram models (e.g., 2-gram, 3-gram, 4-gram). The surfacing candidates that are generated by predictive text n-gram model138may comprise one or more text characters, words, or punctuation that are determined to have at least a threshold likelihood of being selected by a user for following the text input.

The likelihood that a generated surfacing candidate may be selected by a user following the processed text input, as determined by predictive text neural model136or predictive text n-gram model138, may correspond to a specific user account (e.g., predictive text neural model136or predictive text n-gram model138was trained on a corpus that includes text data from that user account) that entered the text input that is processed by predictive text engine134, or the likelihood that a generated surfacing candidate may be selected may correspond to a plurality of user accounts (e.g., predictive text neural model136or predictive text n-gram model138was trained on a corpus that includes text data from a plurality of user accounts and/or one or more general language corpus). In some examples, the likelihood that a generated surfacing candidate may be selected may correspond to user-specific data and data from one or more other user accounts.

In some examples, speech-to-text engine140may receive a speech audio input (e.g., from a microphone associated with client computing device104or mobile computing device106) and transform that speech audio input to text. The text may then be provided to predictive text engine134for processing. Speech-to-text engine140may comprise one or more neural networks (e.g., Long Short-Term Memory [LSTM] models, RNN models, Time Delay Neural Network [TDNN] models, Hidden Markov models, dynamic time warping models, and/or end-to-end models) that have been trained to transform speech audio input to text.

The text input that is processed by predictive text engine134may be initially received by an application executed all or in part by one of the local computing devices (e.g., client computing device104, mobile computing device106). In some examples, the application may be installed locally on one of the local computing devices. In other examples, the application may be a cloud-based application that is accessed by one of the local computing devices via network112. In other examples, the text input that is processed by predictive text engine134may be initially received by an operating system shell element executed by one of the local computing devices. In additional examples, the text input that is processed by predictive text engine134may be initially received by a digital assistant executed by one of the local computing devices, or a cloud-based digital assistant. In some examples, the text input may be communicated to the predictive text service132via one or more APIs.

Predictive text engine134may provide predictive text filtering model146with one or more surfacing candidates that meet a threshold likelihood of being selected by a user for following a processed text input. Predictive text filtering model146includes precarious terms148, precarious word number range150, cooccurrence exceptions152, range resetting characters and conjunctions154, and blocklist156. Precarious word number range150, cooccurrence exceptions152, range resetting characters and conjunctions154, and/or blocklist156may comprise a plurality of rules that are applied during the processing of a surfacing candidate. Predictive text filtering model146may process the one or more surfacing candidates provided to it by predictive text engine134and determine whether they should be filtered based on including one or more precarious terms.

Precarious terms148comprises a plurality of terms that have been classified as being precarious in type. Although precarious terms148may not necessarily be tagged or otherwise associated with precarious category types, in some examples precarious terms148may be identified and added to a precarious term storage location based on having a precarious category type. Precarious category types may include country names (e.g., United States, China, Armenia), company names (e.g., Microsoft, LinkedIn), nouns for ethnic groups (e.g., an African, African people, Middle Easterner, a South American, Brazilian woman, Canadian men, Jewish people, Arabic men), nouns for minority groups (e.g., black people, Mexican men, Asian women), nouns for targets of bias (e.g., homosexuals, gay people, transgender people, old people, women), verbs of violence (e.g., abuse, assault, coerce, demean, dominate, enslave, force, humiliate), verbs of touching (e.g., bang, bite, expose, finger, flash, insert), and in some examples a miscellaneous classification into which terms that do not meet those previously mentioned categorizations, yet are still precarious in nature, may be classified. Examples of terms that may be included in the miscellaneous classification category type include dangerous terms (e.g., anarchist, captor), minor terms (e.g., girl, boy, child, cutie), and illegal terms (e.g., forbidden, illegal, taboo, undercover, underground).

In some examples, the precarious term storage location where precarious terms148are stored may comprise a flat file database (e.g., a database that stores data in a plain text format). In other examples, the precarious term storage location where precarious terms148are stored may comprise a relational database. In other examples, the precarious term storage location where precarious terms148are stored may comprise a binary trie format. In some examples, precarious terms148may be manually identified (e.g., by an administrative user) from one or more corpora and added to a precarious term storage location. In other examples, precarious terms148may be automatically identified using one or more embedding models (e.g., sent-2-vec, ELMo, BERT) comprised of vectors generated from one or more corpora, and added to a precarious term storage location. In additional examples, a combination of manual classification and automatic classification may be utilized to identify terms that are added to precarious terms148(e.g., added to a precarious term storage location).

Precarious word number range150comprises a range of a number of words for filtering cooccurrences of precarious terms from the plurality of terms. Precarious word number range150may comprise a rule that is applied by predictive text filtering model146to a surfacing candidate, alone or in combination with a text input, that specifies that if a first precarious term in a surfacing candidate is within a specific number of words from a second precarious term (e.g., less than one word between precarious terms, less than two words between precarious terms, less than three words between precarious terms, less than four words between precarious terms, less than five words between precarious terms) in the surfacing candidate or the text input, the surfacing candidate is to be filtered from being surfaced unless the surfacing candidate meets a cooccurrence exception or there is a range resetting character or conjunction between the two precarious terms. In additional examples, precarious word number range150may comprise a rule that is applied by predictive text filtering model146to a surfacing candidate, alone or in combination with a text input, that specifies that if a precarious term in a surfacing candidate is within a specific number of words from a blocklist term (e.g., less than one word between a precarious term and a blocklist term, less than two words between a precarious term and a blocklist term, less than three words between a precarious term and a blocklist term, less than four words between a precarious term and a blocklist term, less than five words between a precarious term and a blocklist term) in the text input, the surfacing candidate is to be filtered from being surfaced unless there is a range resetting character or conjunction between the precarious term and the blocklist term.

Cooccurrence exceptions152comprise one or more exceptions that predictive text filtering model146may apply to keep a surfacing candidate that would otherwise be filtered based on including one or more precarious terms148alone or in combination with a text input that includes one or more precarious or blocklist terms. Cooccurrence exceptions152may comprise one or more rules that are applied by predictive text filtering model146to a surfacing candidate, alone or in combination with a text input, that specify that if a first precarious term in a surfacing candidate is within a specific number of words from a second precarious term or blocklist term in the surfacing candidate or the text input, that surfacing candidate should nonetheless not be filtered from being surfaced. For example, cooccurrence exceptions152may comprise rules that precarious terms that are of a country name type, a company name type, a nouns for ethnic groups type, and/or a nouns for minority groups type may be included next to one another or within a specific range number of one another, and not be filtered despite not meeting the criteria of precarious word number range150. As a specific example, a first cooccurrence exception may dictate that terms of a country name type are not be filtered even if they are directly next to one another in a surfacing candidate. In another specific example, a second cooccurrence exception may dictate that terms of a company name type are not be filtered even if they are directly next to one another in a surfacing candidate.

Range resetting characters and conjunctions154comprise punctuation characters or conjunctions that reset the range of the number of words for filtering cooccurrences of precarious terms. Range resetting characters and conjunctions154may comprise periods, commas, semi-colons, colons, and/or conjunctions (e.g., “and”, “or”, “nor”, “yet”) for example. As an example, if precarious word number range150comprises one word (e.g., surfacing candidates will be filtered if they include, alone or in combination with a text input, a first precarious term followed by a non-precarious word, followed by a precarious term), but there is an intervening period (corresponding to a range resetting character) between a first precarious term and a second precarious term in a surfacing candidate, alone or in combination with a text input, the surfacing candidate may nonetheless be surfaced (e.g., not filtered).

Thus, where periods are included in range resetting characters and conjunctions154, and precarious word number range150comprises one word, the following surfacing candidates would not be filtered, where the bold text corresponds to the surfacing candidates and the non-bold text corresponds to the text input.[non-precarious word A] [precarious term B]. [non-precarious term C] [precarious term D][non-precarious word A] [precarious term B]. [precarious term C] [non-precarious term D][non-precarious word A] [non-precarious word B] [precarious term C]. [precarious term D][non-precarious word A] [non-precarious word B] [precarious term C] [non-precarious word D]. [precarious term E]

Blocklist156comprises one or more words or terms that may cause surfacing candidates that include one or more of the words or terms in blocklist156to be filtered out (e.g., blocked from being surfaced, blocked from being further processed) regardless of any rule, exception, or range resetting character or conjunction included in predictive text filtering model146. Blocklist156may comprise a manually or automatically generated list (e.g., stored in a flat file, stored in a relational database) of words or terms. Although blocklist156is illustrated in this example as being included in predictive text filtering model146, a blocklist may additionally or alternatively be applied by predictive text engine134. For example, a blocklist may be applied by predictive text engine134, which would automatically filter out surfacing candidates generated by predictive text engine134that include a word or term included in the blocklist from being sent to predictive text filtering model146.

Training module158includes language corpus160, telemetry engine162, and training engine164. Language corpus160may comprise one or more corpora that predictive text engine134and/or predictive text filtering model may be trained on. Language corpus160may also be utilized to identify precarious terms148and/or words and terms in blocklist156via manual classification and/or automatic classification. Language corpus160may comprise a corpus that is comprised of words, sentences, or sentence fragments that are specific to a user account (e.g., they were generated by user account124). Language corpus160may additionally or alternatively be comprised of words, sentences, or sentence fragments that are not specific to a user account (e.g., American National Corpus, International Corpus of English, German Reference Corpus, Corpus Inscriptionum Semiticarum). In additional examples, language corpus160may comprise one or more domain specific corpora (science specific corpora, accounting specific corpora, legal specific corpora). In some examples, language corpus160may be generated from scraping of the Internet or from user data which has been scrubbed of personal content and metadata.

Telemetry engine162may request and/or receive feedback related to surfaced predictive text (e.g., a displayed surfacing candidate, an audibly produced surfacing candidate). For example, when presented with one or more surfacing candidates, a user may provide feedback (e.g., via voice input, via one or more user interface elements) to telemetry engine162indicating that one or more of the surfaced candidates is inappropriate (e.g., contains offensive text, is not helpful). In some examples, the feedback may be manually reviewed (e.g., by a developer) and one or more words or terms included in surfacing candidates that feedback has been received on may be added or removed from precarious terms148and/or blocklist156. In other examples, the feedback may be automatically processed by predictive text service132, and one or more words or terms included in surfacing candidates that feedback has been received on may be automatically added or removed from precarious terms148and/or blocklist156. In examples, for a word or term to be automatically added to or removed from precarious terms148and/or blocklist156, a threshold number of users may have to have provided a same type of feedback (e.g., positive feedback, negative feedback) related to the word or term. In other examples, precarious terms148and/or blocklist156may only be updated via manual intervention (e.g., by a developer of predictive text filtering model146).

Training engine164may receive feedback related to surfaced predictive text. The feedback may be received directly from a user device (e.g., client computing device104, mobile computing device106), or the feedback may be received from telemetry engine162. Training engine164may use the feedback to train and update one or more models included in predictive text engine134and/or speech-to-text engine140.

FIG.2is a block diagram200illustrating precarious term types that may be used to filter predictive text surfacing candidates. Block diagram200includes precarious terms storage location202. Precarious terms storage location202may comprise a flat file database (e.g., a database that stores data in a plain text format), or a relational database. Precarious terms storage location202includes a plurality of precarious terms, where each of the precarious terms corresponds to, but is not necessarily tagged with or otherwise associated with (e.g., with metadata, in a column or row of precarious terms storage location202) a precarious classification type. In some examples, precarious terms may be manually identified (e.g., by an administrative user) from one or more corpora and added to precarious terms storage location202. In other examples, precarious terms may be automatically identified using one or more embedding models (e.g., sent-2-vec, ELMo, BERT) comprised of vectors generated from one or more corpora, and added to precarious term storage location202. In additional examples, a combination of manual classification and automatic classification may be utilized to identify terms that are added to precarious terms148(e.g., added to a precarious term storage location).

The precarious classification types included in precarious terms storage location202include country names204, company names206, nouns for ethnic groups208, nouns for minority groups210, nouns for targets of bias212, verbs of violence214, verbs of touching216, and additional precarious classifications218. Additional precarious classifications218correspond to terms that do not correspond to the other precarious classification types included in precarious terms storage location202yet are still precarious in nature. For example, additional precarious classifications218may include precarious classes of people (e.g., children, political parties, religions), objects (e.g., weapons, prescription drugs, gambling terms), insults, words for images, diseases, and words for evoking disgust.

FIG.3is a block diagram300illustrating the generation of surfacing candidates by a predictive text engine prior to their processing with a predictive text filtering model. Block diagram300includes text input302, predictive text engine134, surfacing candidates306, surfacing threshold326, predictive text filtering model146, and blocklist156.

Text input302includes at least one word. In this example, text input includes word A304. Text input302may be received by an electronic surface. In some examples, text input302may have been initially received in an audio format and transformed to text (e.g., via speech-to-text engine140).

Text input302is provided to predictive text engine134. In some examples, predictive text engine134may be applied to any text input in the application, service, operating system shell element, or file where text input302was received. In additional examples, settings associated with a user account may be selectable for determining which applications, services, operating system shell elements, or files predictive text engine134may process text for. As described in relation toFIG.1, predictive text engine may process text input302with predictive text neural model136and/or predictive text n-gram model138.

Predictive text engine134determines a plurality of surfacing candidates306based on its processing of text input302. In this example, surfacing candidates306includes surfacing candidate A308, which includes word B310followed by word C312; surfacing candidate B316, which includes word D318; and surfacing candidate C, which includes word B310, followed by word D318, followed by word C312. Predictive text engine134has also determined and assigned a surfacing score to each of surfacing candidates306. Those scores are surfacing score A314for surfacing candidate A308, surfacing score B320for surfacing candidate B316, and surfacing score324for surfacing candidate C322.

The surfacing score for each surfacing candidate may correspond to a likelihood of that surfacing candidate being selected by a user. In the case of predictive text neural model136, a surfacing score for a surfacing candidate may be determined based at least on an embedding value for the surfacing candidate in relation to text input302. In the case of predictive text n-gram model138, the surfacing score for a surfacing candidate may be determined based at least on a frequency of the n-gram corresponding to the surfacing candidate in one or more corpora used to train predictive text n-gram model138and/or how closely the surfacing candidate matches the user input (e.g., in the case of handwriting).

Upon generating surfacing candidates306and their corresponding surfacing scores, predictive text service132may determine whether a surfacing score for each of surfacing candidates306is above or below a surfacing threshold value, which is illustrated by surfacing threshold326. Any surfacing candidates that have surfacing scores below surfacing threshold326may be filtered from further processing (e.g., processing by predictive text filtering model146). Any surfacing candidates that have surfacing scores above surfacing threshold326may be further processed (e.g., by predictive text filtering model146).

Blocklist156is illustrated as spanning predictive text engine134and predictive text filtering model146. This is the case because one or more blocklist may be applied by predictive text engine134and/or by predictive text filtering model146. A blocklist applied by predictive text engine134may be the same blocklist or a different blocklist as applied by predictive text filtering model146. In applying blocklist156by predictive text engine134, one or more surfacing candidates may have been filtered from further processing despite having surfacing scores that exceed surfacing threshold326, based on those one or more surfacing candidates including a word or term included in blocklist156.

FIG.4is a block diagram400illustrating the processing of the surfacing candidates generated inFIG.3, in combination with a text input that includes a precarious term, with a predictive text filtering model. Block diagram400includes potential completed text inputs402, predictive text filtering model146, filter406, filtered surfacing candidate417, surfaced suggestions408, and precarious word key404.

Precarious word key404indicates the words in potential completed text outputs402that are precarious. That is, precarious word key404indicates words that are included in precarious terms storage location202. Specifically, precarious word key404indicates that word A304is a precarious word, word B310is not a precarious word, word C312is a precarious word, and word D318is not a precarious word. Although in this example individual words are indicated as being precarious or not precarious, it should be understood that the entries included in precarious terms storage location202correspond to terms, which may include one or more words. For example, the term “United States” is a country name and therefore would be included in precarious terms storage location202. Similarly, the term “African American” is a noun for a minority group and therefore would be included in precarious terms storage location202. The words that are included in precarious terms storage location202are also illustrated inFIG.4as being associated with a precarious type by having bold outline in potential completed text outputs402.

In the illustrated example, the surfacing scores for each of surfacing candidate A308, surfacing candidate B316, and surfacing candidate C322were determined to be above surfacing threshold326. As such, each of those surfacing candidates are processed with predictive text filtering model146. Surfacing candidates306are joined with text input302such that the entirety of a potential text string that would be generated via the addition of text input302and each of the surfacing candidates306may be processed by predictive text filtering model146. Thus, predictive text filtering model146processes each of potential completed text outputs402. Potential completed text outputs402include first potential completed text output410, which comprises text input302and surfacing candidate A308; second potential completed text output412, which comprises text input302and surfacing candidate B316; and third potential completed text output414, which comprises text input302and surfacing candidate C322.

In this example, precarious word number range150is two words. Thus, if there are less than two words between a first precarious term and a second precarious term, and no exceptions apply, the surfacing candidate will be filtered. As such, predictive text filtering model146, and filter406, filter out surfacing candidate A308, as indicated by filtered surfacing candidate417. This is the case because when joined with text input302, as illustrated by first potential completed text output410, word A304(which is identified as being precarious) is only word away from word C312(which is also identified as being precarious). Similarly, if word A304were a blocklist word, surfacing candidate A308would still have been filtered out.

Alternatively, neither of second potential completed text output412or third potential completed output414include precarious words or terms that are less than two words apart. As such, one or both of surfacing candidate B316, which is included in second potential text output412, and surfacing candidate C322, which is included in third potential completed output414, may be caused to be surfaced.

Predictive text filtering model146may cause surfacing candidates to be surfaced in various forms. In some examples, those forms may be dependent on the application, service, file, or operating system shell element that a surfacing candidate is surfaced in. For example, predictive text filtering model146may cause one or more surfacing candidates that are not filtered out by predictive text filtering model146to be displayed in an application user interface or an operating system shell element that includes the text input that was processed to determine the surfacing candidates. In some examples, the one or more surfacing candidates may be selectable (e.g., via mouse click, via keyboard input, via touch input, via voice input) for inserting a corresponding surfacing candidate into the application user interface or operating system shell element that includes the text input. In additional examples, predictive text filtering model146may cause one or more surfacing candidates that are not filtered out by predictive text filtering model146to be audibly produced via one or more speakers associated with a computing device that the text input that was processed to determine the surfacing candidates was input on. A user may then select (e.g., via mouse click on an accessibility UI element, via keyboard input, via touch input on an accessibility UI element, via voice input) one or more of the surfacing candidates that are not filtered out by predictive text filtering model146for inserting into the application user interface or operating system shell element that includes the text input.

FIG.5is a block diagram500illustrating the processing of surfacing candidates, in combination with a text input that does not include a precarious term, with a predictive text filtering model. In this example, the surfacing candidates comprise different words than the surfacing candidates discussed above inFIGS.3-4. Similarly, text input502is different than text input302. That is, text input502comprises word E504and text input302comprises word A304. Block diagram500includes potential completed text inputs501, predictive text filtering model146, filtered surfacing candidate517, surfaced suggestions522, and precarious word key518.

Precarious word key518indicates the words in potential completed text outputs501that are precarious. That is, precarious word key518indicates words that are included in precarious terms storage location202. Specifically, precarious word key518indicates that word E504is not a precarious word, word F508is a precarious word, word G510is not a precarious word, and word H514is a precarious word. Although in this example individual words are indicated as being precarious or not precarious, it should be understood that the entries included in precarious terms storage location202correspond to terms, which may include one or more words. The words that are included in precarious terms storage location202are also illustrated inFIG.5as being associated with a precarious type by having bold outline in potential completed text outputs501.

In the illustrated example, the surfacing scores for each of surfacing candidate D506, surfacing candidate E512, and surfacing candidate F516have been determined to be above surfacing threshold326. As such, each of those surfacing candidates are processed with predictive text filtering model146. Those surfacing candidates are joined with text input502such that the entirety of a potential text string that would be generated via the addition of text input502and each of the surfacing candidates may be processed by predictive text filtering model146. Although in this example text input502includes a single word it should be understood that text input502may comprise a plurality of words.

Predictive text filtering model146processes each of potential completed text outputs501. Potential completed text outputs501include first potential completed text output505, which comprises text input502and surfacing candidate D506; second potential completed text output507, which comprises text input502and surfacing candidate E512; and third potential completed text output509, which comprises text input502and surfacing candidate F516.

In this example, precarious word number range150is two words. Thus, if there are less than two words between a first precarious term and a second precarious term, and no exceptions apply, the surfacing candidate will be filtered. As such, predictive text filtering model146and filter520filter out surfacing candidate F516, as indicated by filtered surfacing candidate517. This is the case because, despite text input502not including a precarious term, surfacing candidate F516includes a first precarious word (word F508) and a second precarious word (word H514), which are less than two words apart from one another (e.g., they are within precarious word range150).

Alternatively, neither of first potential completed text output505or second potential completed output507include precarious words or terms that are less than two words apart. As such, one or both of surfacing candidate D506, which is included in first potential completed text output505, and surfacing candidate E512, which is included in second potential completed output507, may be caused to be surfaced.

Predictive text filtering model146may cause surfacing candidates to be surfaced in various forms. In some examples, those forms may be dependent on the application, service, file, and/or or operating system shell element that a surfacing candidate is surfaced in. For example, predictive text filtering model146may cause one or more surfacing candidates that are not filtered out by predictive text filtering model146to be displayed in an application user interface or an operating system shell element that includes the text input that was processed to determine the surfacing candidates. In some examples, the one or more surfacing candidates may be selectable (e.g., via mouse click, via keyboard input, via touch input, via voice input) for inserting a corresponding surfacing candidate into the application user interface or operating system shell element that includes the text input. In additional examples, predictive text filtering model146may cause one or more surfacing candidates that are not filtered out by predictive text filtering model146to be audibly produced via one or more speakers associated with a computing device that the text input that was processed to determine the surfacing candidates was input on. A user may then select (e.g., via mouse click on an accessibility UI element, via keyboard input, via touch input on an accessibility UI element, via voice input) the one of the surfacing candidates that are not filtered out by predictive text filtering model146for inserting into the application user interface or operating system shell element that includes the text input.

FIG.6illustrates a first exemplary user interface604for receiving a text input603that is utilized to generate surfacing candidates605by a predictive text engine134, and a second exemplary user interface604* displaying a surfacing candidate that was not filtered by a predictive text filtering model146.

First exemplary user interface604is displayed on computing device602, which is the same computing device as computing device602*. However, first exemplary user interface604displays content prior to processing performed by predictive text filtering model146. Specifically, first user interface604is an email application user interface that a user is drafting an email on to User A, User B, User C and User D, with the subject “Kickoff next week”. The body of the email includes text input603, which states [word A] [word B] [word C] [word A] [word D] [Word E] [word F], and is followed with a period. In this example, for ease of illustration, the words that are precarious (e.g., included in precarious terms storage location202) are bolded. That is, [word A] and [word F] in text input603are precarious words.

Predictive text engine134processes text input603(e.g., with predictive text neural model136, with predictive text n-gram model138) and determines that there are two surfacing candidates that have a threshold surfacing score that exceeds a threshold surfacing value (e.g., surfacing threshold326). Those surfacing candidates605are surfacing candidate G608and surfacing candidate H616. Surfacing candidate G608comprises word A, followed by word C612, followed by word G614. Surfacing candidate H616comprises word H618followed by word I620. As noted above, word A610is a precarious word. The bolding around word H618and word I620illustrates in this example that those words are also precarious (e.g., included in precarious terms storage location202).

In this example, precarious word number range150is two words. As such, without an exception, that rule would dictate that both of surfacing candidate G608and surfacing candidate616be filtered from being surfaced. For example, word F (which is precarious) in text input603would be less than two words away from word A610(which is precarious) if surfacing candidate G608was inserted after text input603. Similarly, word F (which is precarious) in text input603would be less than two words away from both word H618(which is precarious) and word I620(which is precarious) if surfacing candidate H616was inserted after text input603. However, in this example range resetting characters and conjunctions154for predictive text filtering model146includes a rule that periods reset precarious word number range150. As such, surfacing candidate G608is not filtered from being surfaced because the period after word F in text input603resets precarious word number range150. However, predictive text filtering model146filters surfacing candidate H616from being surfaced because surfacing candidate H616includes two terms (in this case words) that are within precarious word number range150. That is, in this example, word H618and word I620in surfacing candidate H616are directly next to one another (there are less than two words between precarious word H618and precarious word I620, which meets the rule corresponding to precarious word number range150), and there are no range resetting characters and conjunctions154between them or cooccurrence exceptions152that apply to them. As such, surfacing candidate H616is filtered from being surfaced by predictive text filtering model146.

Surfacing candidate G608was not filtered by predictive text filtering model146and it is therefore caused to be displayed on application user interface604* on computing device602*, as indicated by selectable surfacing candidate element609. Selectable surfacing candidate element609may be interacted with (e.g., via mouse click, via touch input, via voice input, via keyboard input) for causing surfacing candidate608to be inserted after text input603. Although in this example only a single non-filtered surfacing candidate is surfaced for selection, it should be understood that in examples where more than one surfacing candidate is not filtered by predictive text filtering model146, a plurality of non-filtered surfacing candidates may be surfaced for selection by a user and subsequent insertion after a processed text input.

FIG.7Ais an exemplary method for assisting with filtering predictive text surfacing candidates. The method700A begins at a start operation and flow moves to operation702A.

At operation702A a predictive text filtering model is maintained. The predictive text filtering model may comprise a plurality of terms that are associated in the predictive text filtering model with a precarious classification. In some examples, the precarious terms may be associated with a precarious classification type (e.g., country names, company names, nouns for ethnic groups, nouns for minority groups, nouns for targets of bias, verbs of violence, verbs of touching, and in some examples a miscellaneous classification for terms that do not meet those previously mentioned categorizations yet are still precarious in nature). The association may comprise being tagged (e.g., with metadata) with the classification or being associated with the classification in a relational database, for example. In other examples, the precarious terms may not necessarily be tagged or otherwise associated with a precarious category type. In some examples, the plurality of terms may be stored in a storage location comprising a flat file database (e.g., a database that stores data in a plain text format). In other examples, the precarious terms may be stored in a relational database or a binary trie format. In some examples, the precarious terms may be manually identified (e.g., by an administrative user) from one or more corpus and added to a precarious term storage location. In other examples, the precarious terms may be automatically identified using one or more embedding models (e.g., sent-2-vec, ELMo, BERT) comprised of vectors generated from one or more corpus, and added to a precarious term storage location. In additional examples, a combination of manual classification and automatic classification may be utilized to identify precarious terms.

The predictive text filtering model may further comprise a range of a number of words for filtering cooccurrences of precarious terms from the plurality of terms. The range may comprise a rule that is applied by the predictive text filtering model to a surfacing candidate, alone or in combination with a text input, that specifies that if a first precarious term in a surfacing candidate is within a specific number of words from a second precarious term in the surfacing candidate or the text input, the surfacing candidate is to be filtered from being surfaced unless the surfacing candidate meets a cooccurrence exception or there is a range resetting character or conjunction between the two precarious terms.

From operation702A flow continues to operation704A where a text input is processed with a predictive text model. The text input may comprise one or more text strings (e.g., text characters, words, sentences). The text input may be received by the predictive text model from an electronic surface accessed by a computing device. In some examples, the text input may initially be received in an audio format and transformed to a text input with a speech-to-text engine.

The predictive text model may comprise one or more predictive text neural models and/or one or more predictive text n-gram models. A predictive text neural model may comprise a neural network that may receive a text input from an electronic surface and generate one or more surfacing candidates based on processing the text input with the neural network. Examples of neural models that may process the text input include GPT models, BERT, ELMo, and RNN models. A predictive text n-gram model may similarly receive the text input from an electronic surface and generate one or more surfacing candidates based on processing the text input. Examples of n-gram models that may process the text input include 2-gram models, 3-gram models, and 4-gram models.

From operation704A flow continues to operation706A where a plurality of surfacing candidates is determined based on the processing of the text input with the predictive text model. Each of the plurality of surfacing candidates may comprise at least one word. In some examples, each of the plurality of surfacing candidates may be determined to have at least a threshold likelihood of being selected by a user for following the text input. The likelihood that a generated surfacing candidate may be selected by a user following the processed text input may correspond to a specific user account that entered the text input that is processed by the predictive text model, or the likelihood that a generated surfacing candidate may be selected may correspond to a plurality of user accounts. In some examples, the likelihood that a generated surfacing candidate may be selected may correspond to user-specific data and data from one or more other user accounts. In additional examples, the likelihood that a generated surfacing candidate may be selected by a user for following the text input may correspond to a surfacing score determined for a surfacing candidate. In the case of a predictive text neural model, a surfacing score for a surfacing candidate may be determined based at least on an embedding value for a surfacing candidate in relation to the text input. In the case of a predictive text n-gram model, a surfacing score for a surfacing candidate may be determined based at least on a frequency of the n-gram corresponding to the surfacing candidate in one or more corpus used to train the predictive text n-gram model.

From operation706A flow continues to operation708A where the predictive text filtering model is applied to a surfacing candidate of the plurality of candidates. In some examples, the predictive text filtering model may be applied to any text input in the application, surface, or operating system shell element where the text input was received. In additional examples, settings associated with a user account may be selectable for determining which applications, surfaces, or operating system shell elements the predictive text filtering model may process text for.

From operation708A flow continues to operation710A where the surfacing candidate is filtered by the predictive text filtering model. The surfacing candidate may be filtered from further processing because the surfacing candidate, alone or in combination with the text input, includes a first term that is identified as having a precarious classification within the range of the number of words (e.g., one word, two words, three words) from a second term that is identified as having a precarious classification. In addition, there may not be a cooccurrence exception that applies to the two precarious terms in their context in relation to one another and/or there may not be any range resetting characters or conjunctions between the two terms. In additional examples, the surfacing candidate may be filtered from further processing if it includes a precarious term within the range of the number of words from a blocklist term.

From operation710A flow continues to operation712A where at least one non-filtered surfacing candidate is caused to be displayed. The at least one non-filtered surfacing candidate may be displayed in the electronic surface where the text input was received. In some examples, the at least one non-filtered surfacing candidate may be selectable for inserting the non-filtered surfacing candidate into the electronic surface directly after the text input.

From operation712A flow moves to an end operation and the method700A ends.

FIG.7Bis another exemplary method700B for assisting with filtering predictive text surfacing candidates. The method700B begins at a start operation and flow moves to operation702B.

At operation702B a text input is received. The text input may comprise one or more text strings (e.g., text characters, words, sentences). The text input may be received by an application (e.g., email application, word processing application, calendar application, SMS application, group messaging application, collaboration application, social media application, web browser application, task management application, to-do list application, map application, game application, reservation application, presentation application, spreadsheet application). In additional examples the text input may be received by a predictive text service from an application or application service.

From operation702B flow continues to operation704B where the text input is displayed on an application user interface. The text input may have been typed or pasted in the application user interface, for example. In additional examples, the text input may initially be received in an audio format and transformed to a text input with a speech-to-text engine.

From operation704B flow continues to operation706B where the text input is processed with a predictive text model. The predictive text model may comprise one or more predictive text neural models and/or one or more predictive text n-gram models. A predictive text neural model may comprise a neural network that may receive a text input from an electronic surface and generate one or more surfacing candidates based on processing the text input with the neural network. Examples of neural models that may process the text input include GPT models, BERT, ELMo, and RNN models. A predictive text n-gram model may similarly receive the text input from an electronic surface and generate one or more surfacing candidates based on processing the text input. Examples of n-gram models that may process the text input include 2-gram models, 3-gram models, and 4-gram models.

From operation706B flow continues to operation708B where a plurality of surfacing candidates is determined based on the processing of the text with the predictive text model. Each of the plurality of surfacing candidates may comprise at least one word. In some examples, each of the plurality of surfacing candidates may be determined to have at least a threshold likelihood of being selected by a user for following the text input. The likelihood that a generated surfacing candidate may be selected by a user following the processed text input may correspond to a specific user account that entered the text input that is processed by the predictive text model, or the likelihood that a generated surfacing candidate may be selected may correspond to a plurality of user accounts. In some examples, the likelihood that a generated surfacing candidate may be selected may correspond to user-specific data and data from one or more other user accounts. In additional examples, the likelihood that a generated surfacing candidate may be selected by a user for following the text input may correspond to a surfacing score determined for a surfacing candidate. In the case of a predictive text neural model, a surfacing score for a surfacing candidate may be determined based at least on an embedding value for a surfacing candidate in relation to the text input. In the case of a predictive text n-gram model, a surfacing score for a surfacing candidate may be determined based at least on a frequency of the n-gram corresponding to the surfacing candidate in one or more corpus used to train the predictive text n-gram model and/or how similar the text input is to a surfacing candidate.

From operation708B flow continues to operation710B where a predictive text filtering model is applied to a surfacing candidate of the plurality of candidates. Application of the predictive text filtering model may comprise determining that the surfacing candidate or the text input includes a first term that is associated with a precarious classification in the predictive text filtering model, determining that the surfacing candidate or the text input includes a second term that is associated with the precarious classification in the predictive text filtering model, and determining that the first term is within a range of a number of words (e.g., one word, two words, three words) for filtering cooccurrences of precarious terms.

From operation710B flow continues to operation712B where the surfacing candidate is filtered by the predictive text filtering model. The surfacing candidate may be filtered from further processing because the first term is associated with a precarious classification in the predictive text filtering model and the second term is associated with a precarious classification in the text filtering model, and the first term is within the range of the number of words for filtering cooccurrences of precarious terms. In addition, there may not be a cooccurrence exception that applies to the two precarious terms in their context in relation to one another and/or there may not be any range resetting characters or conjunctions between the two terms. Additionally, for the surfacing candidate to be filtered, the first term and the second term need not necessarily be associated with a same precarious classification type.

From operation712B flow continues to operation714B where the at least one non-filtered surfacing candidate is displayed in the application user interface. The at least one non-filtered surfacing candidate may be displayed in the application user interface where the text input was received. In some examples, the at least one non-filtered surfacing candidate may be selectable for inserting the non-filtered surfacing candidate into the application user interface directly after the text input.

From operation714B flow moves to an end operation and the method700B ends.

FIGS.7C and7Dare an exemplary method700C/700D for applying a text filtering model to a plurality of match text candidates generated by a speech-to-text engine. The method700C begins atFIG.7Cand continues throughFIG.7Dand the method700D. The method700C/700D begins at a start operation and flow moves to operation702C.

At operation702C a voice input is received. The voice input may be received by a microphone of computing device (e.g., client computing device104, mobile computing device106). In additional examples, the voice input may be initially received by a microphone and sent to a speech-to-text service and/or a text filtering service.

From operation702C flow continues to operation704C where a speech-to-text engine (e.g., speech-to-text engine140) is applied to the voice input. Although speech-to-text engine140is illustrated as being included in predictive text service132, in some examples where predictive text engine134is not applied, speech-to-text engine140may be included as a separate service. For example, a user may simply be using dictation software that a speech-to-text engine and a text filtering model is being applied to, or a voicemail transcription service may utilize a speech-to-text engine and a text filtering model. The speech-to-text engine may be applied to the voice input based on settings associated with an application that is currently being executed by an open application or settings associated with an operating system or operating system shell element. In other examples, the speech-to-text engine may be applied to the voice input based on being provided to a digital assistant executed all or in part by the computing device that receives the voice input.

From operation704C flow continues to operation706C where a plurality of match text candidates for the voice input are determined by the speech-to-text engine and surfacing scores for each of the plurality of match text candidates are also determined. A match text candidate for a voice input may comprise one or more words or terms that the speech-to-text engine determines match a voice input to a threshold degree of confidence (e.g., a confidence score above 75%, a confidence score above 90%) based on its processing of the voice input. Thus, more than one match text candidate may be determined for a voice input. The surfacing score for each of the plurality of match text candidates may correspond to a corresponding confidence score that is calculated by the speech-to-text engine.

From operation706C flow continues to operation708C where a text filtering model is applied to each of the plurality of match text candidates. In examples, the text filtering model that is applied at operation708C may be substantially similar to or the same as predictive text filtering model146. For example, the text filtering model may comprise a plurality of terms that are associated in the text filtering model with a precarious classification. In some examples, the precarious terms may be associated with a precarious classification type (e.g., country names, company names, nouns for ethnic groups, nouns for minority groups, nouns for targets of bias, verbs of violence, verbs of touching, and in some examples a miscellaneous classification for terms that do not meet those previously mentioned categorizations yet are still precarious in nature). The association may comprise being tagged (e.g., with metadata) with the classification or being associated with the classification in a relational database, for example. In other examples, the precarious terms may not necessarily be tagged or otherwise associated with a precarious category type. In some examples, the plurality of terms may be stored in a storage location comprising a flat file database (e.g., a database that stores data in a plain text format). In other examples, the precarious terms may be stored in a relational database. In some examples, the precarious terms may be manually identified (e.g., by an administrative user) from one or more corpus and added to a precarious term storage location. In other examples, the precarious terms may be automatically identified using one or more embedding models (e.g., sent-2-vec, ELMo, BERT) comprised of vectors generated from one or more corpus, and added to a precarious term storage location. In additional examples, a combination of manual classification and automatic classification may be utilized to identify precarious terms.

The text filtering model may further comprise a range of a number of words for filtering cooccurrences of precarious terms from the plurality of terms. The range may comprise a rule that is applied by the text filtering model to a match text candidate, that specifies that if a first precarious term in the match text candidate is within a specific number of words from a second precarious term in the match text candidate, the match text candidate must have to meet a heightened threshold surfacing value to be surfaced unless the match text candidate meets a cooccurrence exception or there is a range resetting character or conjunction between the two precarious terms.

From operation708C flow continues to operation710C where a first match text candidate of the plurality of match text candidates is determined to include potentially offensive language based on the processing performed at operation708C. That is, the text filtering engine may have identified a first precarious term within the range of the number of words for filtering cooccurrences of precarious terms from the plurality of terms, of a second precarious term.

From operation710C flow continues to operation712C where a determination is made that a surfacing score for the first match text candidate of the plurality of match text candidates is higher than a surfacing score for a second match text candidate of the plurality of match text candidates.

From operation712C flow continues to operation714D (FIG.700D). At operation714D a determination is made as to whether the surfacing score for the first match text candidate is above a heightened threshold value. That is, to be surfaced and/or displayed, the first match text candidate may have to have a surfacing score that meets a threshold that is higher than a threshold for other match text candidates that have not been determined to include potentially offensive language based on application of the text filtering model. In this manner, the text filtering model may only allow potentially, or likely, offensive language to be surfaced from a voice input processed by a speech-to-text engine if the text filtering model determines to a heightened degree of certainty that the user really wants the output to include that potentially, or likely, offensive language.

If at operation714D a determination is made that the surfacing score for the first match text candidate is above the heightened threshold value, flow moves to operation718D where the first match text candidate is displayed by a computing device (e.g., client computing device104, mobile computing device106) or otherwise surfaced (e.g., audibly produced, brail haptic output).

From operation718D flow moves to an end operation and the method700C/700D ends.

Alternatively, if at operation714D a determination is made that the surfacing score for the first match text candidate is not above the heightened threshold value, flow moves to operation716D where the second match text candidate is displayed by a computing device (e.g., client computing device104, mobile computing device106) or otherwise surfaced (e.g., audibly produced, brail haptic output).

From operation716D flow moves to an end operation and the method700C/700D ends.

FIGS.8and9illustrate a mobile computing device800, for example, a mobile telephone, a smart phone, wearable computer (such as smart eyeglasses), a tablet computer, an e-reader, a laptop computer, or other AR compatible computing device, with which embodiments of the disclosure may be practiced. With reference toFIG.8, one aspect of a mobile computing device800for implementing the aspects is illustrated. In a basic configuration, the mobile computing device800is a handheld computer having both input elements and output elements. The mobile computing device800typically includes a display805and one or more input buttons810that allow the user to enter information into the mobile computing device800. The display805of the mobile computing device800may also function as an input device (e.g., a touch screen display). If included, an optional side input element815allows further user input. The side input element815may be a rotary switch, a button, or any other type of manual input element. In alternative aspects, mobile computing device800may incorporate more or fewer input elements. For example, the display805may not be a touch screen in some embodiments. In yet another alternative embodiment, the mobile computing device800is a portable phone system, such as a cellular phone. The mobile computing device800may also include an optional keypad835. Optional keypad835may be a physical keypad or a “soft” keypad generated on the touch screen display. In various embodiments, the output elements include the display805for showing a graphical user interface (GUI), a visual indicator820(e.g., a light emitting diode), and/or an audio transducer825(e.g., a speaker). In some aspects, the mobile computing device800incorporates a vibration transducer for providing the user with tactile feedback. In yet another aspect, the mobile computing device800incorporates input and/or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., a HDMI port) for sending signals to or receiving signals from an external device.

FIG.9is a block diagram illustrating the architecture of one aspect of a mobile computing device. That is, the mobile computing device900can incorporate a system (e.g., an architecture)902to implement some aspects. In one embodiment, the system902is implemented as a “smart phone” capable of running one or more applications (e.g., browser, e-mail, calendaring, contact managers, messaging clients, games, and media clients/players). In some aspects, the system902is integrated as a computing device, such as an integrated personal digital assistant (PDA) and wireless phone.

One or more application programs966may be loaded into the memory962and run on or in association with the operating system964. Examples of the application programs include phone dialer programs, e-mail programs, personal information management (PIM) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. The system902also includes a non-volatile storage area968within the memory962. The non-volatile storage area968may be used to store persistent information that should not be lost if the system902is powered down. The application programs966may use and store information in the non-volatile storage area968, such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) also resides on the system902and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area968synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into the memory962and run on the mobile computing device900, including instructions for providing and operating an asset disposition engine.

The system902has a power supply970, which may be implemented as one or more batteries. The power supply970might further include an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the batteries.

The system902may also include a radio interface layer972that performs the function of transmitting and receiving radio frequency communications. The radio interface layer972facilitates wireless connectivity between the system902and the “outside world,” via a communications carrier or service provider. Transmissions to and from the radio interface layer972are conducted under control of the operating system964. In other words, communications received by the radio interface layer972may be disseminated to the application programs966via the operating system964, and vice versa.

The visual indicator820may be used to provide visual notifications, and/or an audio interface974may be used for producing audible notifications via the audio transducer825. In the illustrated embodiment, the visual indicator820is a light emitting diode (LED) and the audio transducer825is a speaker. These devices may be directly coupled to the power supply970so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor960and other components might shut down for conserving battery power. The LED may be programmed to remain on indefinitely until the user takes action to indicate the powered-on status of the device. The audio interface974is used to provide audible signals to and receive audible signals from the user. For example, in addition to being coupled to the audio transducer825, the audio interface974may also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. In accordance with embodiments of the present disclosure, the microphone may also serve as an audio sensor to facilitate control of notifications, as will be described below. The system902may further include a video interface976that enables an operation of an on-board camera830to record still images, video stream, and the like.

A mobile computing device900implementing the system902may have additional features or functionality. For example, the mobile computing device900may also include additional data storage devices (removable and/or non-removable) such as, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.9by the non-volatile storage area968.

Data/information generated or captured by the mobile computing device900and stored via the system902may be stored locally on the mobile computing device900, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio interface layer972or via a wired connection between the mobile computing device900and a separate computing device associated with the mobile computing device900, for example, a server computer in a distributed computing network, such as the Internet. As should be appreciated such data/information may be accessed via the mobile computing device900via the radio interface layer972or via a distributed computing network. Similarly, such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems.

FIG.10is a block diagram illustrating physical components (e.g., hardware) of a computing device1000with which aspects of the disclosure may be practiced. The computing device components described below may have computer executable instructions for assisting with surfacing a resource on a target device in its same state from an application executed on a source device via transformation of application context data. In a basic configuration, the computing device1000may include at least one processing unit1002and a system memory1004. Depending on the configuration and type of computing device, the system memory1004may comprise, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. The system memory1004may include an operating system1005suitable for running one or more software applications. The operating system1005, for example, may be suitable for controlling the operation of the computing device1000. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG.10by those components within a dashed line1008. The computing device1000may have additional features or functionality. For example, the computing device1000may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.10by a removable storage device1009and a non-removable storage device1010.

As stated above, a number of program modules and data files may be stored in the system memory1004. While executing on the processing unit1002, the program modules1006(e.g., predictive text application1020) may perform processes including, but not limited to, the aspects, as described herein. Predictive text engine1011may perform one or more operations associated with receiving a text input and processing that text input with one or more predictive text neural models and/or one or more predictive text n-gram models to identify one or more surfacing candidates that are likely to be selected by a user for following the text input. Text filtering engine1013may perform one or more operations associated with processing a surfacing candidate to determine whether it includes a first term that is associated with a precarious classification within a range of words from a second term that is associated with a precarious classification (in the same surfacing candidate or in a text input the surfacing candidate would follow). If so, and there are no cooccurrence exceptions that apply to the two precarious terms in their context in relation to one another, and there are no range resetting characters or conjunctions between the two terms, text filtering engine1013may filter the surfacing candidate from further processing. Telemetry engine1015may perform one or more operations associated with requesting and/or receiving feedback related to surfaced predictive text. Model training engine1017may receive feedback related to surfaced predictive text. The feedback may be received directly from a user device, or the feedback may be received from telemetry engine1015. Model training engine1017may use the feedback to train and update one or more models included in predictive text engine1011and/or speech-to-text engine140.

Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, embodiments of the disclosure may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated inFIG.10may be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communications units, system virtualization units and various application functionality all of which are integrated (or “burned”) onto the chip substrate as a single integrated circuit. When operating via an SOC, the functionality, described herein, with respect to the capability of client to switch protocols may be operated via application-specific logic integrated with other components of the computing device1000on the single integrated circuit (chip). Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems.

The computing device1000may also have one or more input device(s)1012such as a keyboard, a mouse, a pen, a sound or voice input device, a touch or swipe input device, etc. The output device(s)1014such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used. The computing device1000may include one or more communication connections1016allowing communications with other computing devices1050. Examples of suitable communication connections1016include, but are not limited to, radio frequency (RF) transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports.

The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory1004, the removable storage device1009, and the non-removable storage device1010are all computer storage media examples (e.g., memory storage). Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device1000. Any such computer storage media may be part of the computing device1000. Computer readable media and computer storage media as described herein does not include transitory media such as a carrier wave or other propagated or modulated data signal. A computer readable storage device as described herein does not include transitory media such as a carrier wave or other propagated or modulated data signal.

Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

FIG.11illustrates one aspect of the architecture of a system for processing data received at a computing system from a remote source, such as a personal/general computer1104, tablet computing device1106, or mobile computing device1108, as described above. Content displayed at server device1102may be stored in different communication channels or other storage types. For example, various documents may be stored using a directory service1122, a web portal1124, a mailbox service1126, an instant messaging store1128, or a social networking site1130. The program modules1006may be employed by a client that communicates with server device1102, and/or the program modules1006may be employed by server device1102. The server device1102may provide data to and from a client computing device such as a personal/general computer1104, a tablet computing device1106and/or a mobile computing device1108(e.g., a smart phone) through a network1115. By way of example, the computer system described above may be embodied in a personal/general computer1104, a tablet computing device1106and/or a mobile computing device1108(e.g., a smart phone). Any of these embodiments of the computing devices may obtain content from the store1116, in addition to receiving graphical data useable to be either pre-processed at a graphic-originating system, or post-processed at a receiving computing system.

Aspects of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to aspects of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode of claimed disclosure. The claimed disclosure should not be construed as being limited to any aspect, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present disclosure, one skilled in the art may envision variations, modifications, and alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure. The various embodiments described above are provided by way of illustration only and should not be construed to limit the claims attached hereto. Those skilled in the art will readily recognize various modifications and changes that may be made without following the example embodiments and applications illustrated and described herein, and without departing from the true spirit and scope of the following claims.