Patent Description:
<NPL> discusses making use of natural language processing techniques in order to identify the relevant features of spam comments.

<NPL>, considers the problem of detecting spammers on Twitter.

At least one aspect is directed to a system to restrict content transmission in a networked environment. The system can include a data processing system that can include an interface to receive a content request from a client computing device. The system can include a content selector component that is executed by the data processing system. The content selector component can select a first digital component based on the content request. The first digital component can include a first plurality of characters. The system can include a manipulation detection component that is executed by the data processing system. The manipulation detection component can retrieve a dictionary that includes an indication of a portion of candidate characters included in a plurality of digital components. The manipulation detection component can convert a first plurality of characters into a character array based on the dictionary that can include the indication of the portion of the candidate characters included in the plurality of digital components. The manipulation detection component can classify the first digital component into a first class of a plurality of classes based on a sequence of the first plurality of characters in the character array. The manipulation detection component can determine to not include the first digital component in a response to the content request from the client computing device based on the classification of the first digital component into the first class of the plurality of classes. The manipulation detection component can generate the response based on the content request from the client computing device.

At least one aspect is directed toward a method to restrict content transmission in a networked environment. The method can include receiving, by a data processing system, a content request from a client computing device. The method can include selecting, by a content selector component executed by the data processing system, a first digital component based on the content request. The first digital component can include a first plurality of characters. The method can include retrieving, by a manipulation detection component executed by the data processing system, a dictionary that can include an indication of a portion of candidate characters included in a plurality of digital components. The method can include converting, by the manipulation detection component, the first plurality of characters into a character array based on the dictionary. The method can include classifying the first digital component into a first class of a plurality of classes based on a sequence of the first plurality of characters in the character array. The method can include determining, by the manipulation detection component, to not include the first digital component in a response to the content request from the client computing device based on the classification of the first digital component into the first class of the plurality of classes. The method can include generating, by the data processing system, the response based on the content request from the client computing device.

At least one aspect of the disclosure is directed to a digital assistant device can include an audio driver, a transducer, a sensor to detect an input audio signal, and a pre-processor component. The pre-processor component can be coupled to the audio driver, the transducer, and the sensor. The pre-processor can filter the input audio signal to create a filtered input audio signal. The pre-processor can convert the filtered input audio signal to data packets. The pre-processor can transmit the data packets to a data processing system. The data processing system can include one or more processors and memory that execute a natural language processor component, a content selector component, an interface, and a manipulation detection component. The data processing system can receive, via the interface, from the pre-processor component, the data packets comprising the filtered input audio signal detected by the sensor. The data processing system can identify, by the natural language processor component, a content request in the filtered input audio signal. The data processing system can select, by the content selector component, a first digital component based on the content request. The first digital component can include a first plurality of characters. The data processing system can retrieve, by the manipulation detection component, a dictionary that can include an indication of a portion of candidate characters included in a plurality of digital components. The data processing system can convert, by the manipulation detection component, first plurality of characters into a character vector based on the dictionary comprising the indication of the portion of the candidate characters included in the plurality of digital components. The data processing system can classify, by the manipulation detection component, the first digital component into a first class of a plurality of classes based on a sequence of the first plurality of characters in the character vector. The data processing system can determine, by the manipulation detection component, to not include the first digital component in a response to the content request from the client computing device based on the classification of the first digital component into the first class of the plurality of classes. The data processing system can generate, by the manipulation detection component, the response based on the content request from the client computing device, the response not including the first digital component based on the determination to not include the first digital component in the response.

Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems to restrict content transmission in a networked environment. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.

Systems and methods of the present disclosure relate generally to a data processing system that identifies possibly manipulated or fraudulent digital components in a computer network environment. The data processing system can improve the efficiency and effectiveness of data packet (or other protocol-based) transmission over one or more computer networks by, for example, preventing or reducing the number of manipulated or fraudulent digital component transmissions. The manipulated or fraudulent digital components can be content items that include text or other content that is configured to not be detected by manual or automatic content filters. The manipulation of the text can include the inclusion of homoglyphs, the inclusion of repeated characters, the deletion of characters, the merging of words, the transposition of characters, or any combination thereof.

The systems and methods described herein can be in or with a digital assistant or other audio-based system. For example, the system can include a data processing system that receives an input audio query, which can also be referred to as an input audio signal. From the input audio query, the data processing system can identify a request and a trigger keyword corresponding to the request. Based on the trigger keyword or the request, the data processing system can generate a response that can include one or more digital components. The data processing system can select the digital components or the digital components can be provided to the data processing system from a third party. The digital components can be user generated content, such as reviews or comments. The data processing system can process the digital components to determine if the digital components are fraudulent, manipulated, or should be marked as spam. By identifying manipulated digital components, the data processing system can delete, purge, bar, or otherwise restrict the transmission of the digital component, which can reduce the total consumption of computational power, power, memory, and bandwidth as undesirable digital components are not transmitted through the network to client computing devices.

The data processing system can then select non-manipulated digital components that can be provided by a packet or other protocol-based data message transmission via a computer network to a client computing device. The digital component can also be referred to as a content item. A digital component can be included in the digital component. The output signal that includes the selected digital component can cause an audio driver component of the client computing device to generate an acoustic wave, e.g., an audio output, which can be output from the client computing device.

<FIG> illustrates an example system <NUM> to restrict content transmission in a networked environment. The data processing system <NUM> can include at least one server having at least one processor. For example, the data processing system <NUM> can include a plurality of servers located in at least one data center or server farm. The data processing system <NUM> can determine, from an input audio signal, a request and a trigger keyword associated with the request. Based on the request and trigger keyword the data processing system <NUM> can determine or select at least one action data structure, and can select at least one digital component (and initiate other actions as described herein).

The data processing system <NUM> can include multiple, logically-grouped servers and facilitate distributed computing techniques. The logical group of servers may be referred to as a data center, server farm, or a machine farm. The servers can be geographically dispersed. A data center or machine farm may be administered as a single entity, or the machine farm can include a plurality of machine farms. The servers within each machine farm can be heterogeneous - one or more of the servers or machines can operate according to one or more type of operating system platform. The data processing system <NUM> can include servers in a data center that are stored in one or more high-density rack systems, along with associated storage systems, located for example in an enterprise data center. The data processing system <NUM> with consolidated servers in this way can improve system manageability, data security, the physical security of the system, and system performance by locating servers and high-performance storage systems on localized high-performance networks. Centralization of all or some of the data processing system <NUM> components, including servers and storage systems, and coupling them with advanced system management tools, allows more efficient use of server resources, which saves power and processing requirements and reduces bandwidth usage.

The data processing system <NUM> can include at least one natural language processor (NLP) component <NUM>, at least one interface <NUM>, at least one manipulation detection component <NUM>, at least one content selector component <NUM>, at least one audio signal generator component <NUM>, at least one direct action application programming interface (API) <NUM>, and at least one data repository <NUM>. The NLP component <NUM>, interface <NUM>, manipulation detection component <NUM>, content selector component <NUM>, audio signal generator component <NUM>, and direct action API <NUM> can each include at least one processing unit, server, virtual server, circuit, engine, agent, appliance, or other logic device such as programmable logic arrays configured to communicate with the data repository <NUM> and with other computing devices (e.g., at least one client computing device <NUM>, or at least one content provider computing device <NUM>) via the at least one computer network <NUM>. The network <NUM> can include computer networks such as the internet, local, wide, metro or other area networks, intranets, satellite networks, other computer networks such as voice or data mobile phone communication networks, and combinations thereof.

The network <NUM> can include or constitute a display network, e.g., a subset of information resources available on the internet that are associated with a content placement or search engine results system, or that are eligible to include third party digital components as part of a digital component placement campaign. The network <NUM> can be used by the data processing system <NUM> to access information resources such as web pages, web sites, domain names, or uniform resource locators that can be presented, output, rendered, or displayed by the client computing device <NUM>. For example, via the network <NUM> a user of the client computing device <NUM> can access information, data provided by the data processing system <NUM>, or otherwise interact with the data processing system <NUM> or the content provider device <NUM>.

The network <NUM> can include, for example, a point-to-point network, a broadcast network, a wide area network, a local area network, a telecommunications network, a data communication network, a computer network, an Asynchronous Transfer Mode (ATM) network, a Synchronous Optical Network (SONET) network, a Synchronous Digital Hierarchy (SDH) network, a wireless network or a wireline network, and combinations thereof. The network <NUM> can include a wireless link, such as an infrared channel or satellite band. The topology of the network <NUM> may include a bus, star, or ring network topology. The network <NUM> can include mobile telephone networks using any protocol or protocols used to communicate among mobile devices, including advanced mobile phone protocol ("AMPS"), time division multiple access ("TDMA"), code-division multiple access ("CDMA"), global system for mobile communication ("GSM"), general packet radio services ("GPRS"), Long-Term Evolution (LTE), or universal mobile telecommunications system ("UMTS"). Different types of data may be transmitted via different protocols, or the same types of data may be transmitted via different protocols.

The data processing system <NUM> can include a manipulation detection component <NUM>. The manipulation detection component <NUM> can include an application, script, or program that is executed by the data processing system <NUM> to detect digital components that include manipulated text. The manipulation detection component <NUM> can restrict the transmission of the manipulated digital component in a networked environment by detecting manipulated digital components and then preventing (or reducing) the transmission of the manipulated digital components to client computing devices <NUM>.

The manipulation detection component <NUM> can identify manipulated digital components that are provided by the content provider devices <NUM>. The manipulated digital components can include content that is configured to appear as if the digital component originated from a different content provider device <NUM>. The manipulated digital components can include content that is intended, by the content provider device <NUM>, to confuse or deceive an end user viewing the digital component. The manipulated digital components can include content configured to pass through automated and manual filters (e.g., spam filters).

The manipulation to the digital components can include the manipulation of the text in the digital components. The manipulations can include replacing characters with other characters, intermingling additional symbols, removing or adding letters, merging words, or transposing letters. Replacing characters with other characters can include replacing letters with homoglyphs (e.g., replacing uppercase O with a zero), replacing a character with a similar looking symbol (e.g., replacing an "a" with an @), or replacing letters that sound similar (e.g., replacing a "w" with a "v"). Intermingling additional symbols can include adding special characters, punctuation, or spaces to the words. Adding or removing letters can include duplicating one or more letters, adding random letters, removing intentionally repeated letters (e.g., replacing better with beter), or removing random letters. Merging words can include removing the space between neighboring words. Transposing letters can include transposing letter within the body of a word (e.g., replacing download with downlaod).

The manipulated text can be in digital components that the content provider devices <NUM> provides to the data processing system <NUM> or the client computing device <NUM>. The digital components can include electronic documents, web pages, ads, images, output audio files, output video files, uniform resource locators (URLs), visual uniform resource locators (vURLs), or other types of electronic content. The digital components can be retrieved from the content provider device <NUM> by the data processing system <NUM>. For example, the digital components can include user generated content that is hosted or provided by the content provider device <NUM>. The user generated content can include online comments or reviews. In one example, the comments can be related to a user's reviews of a restaurant.

Receiving an input query for information about a given restaurant, the data processing system <NUM> can search webpages provided by the content provider device <NUM> for reviews of the given restaurant. The digital components the data processing system <NUM> selects for a response can include reviews of the restaurant provided by the content provider device <NUM> or one or more of the comments (related to the given restaurant) made on the content provider device's webpages.

For example, in response to the input query "Ok, how do people like restaurant XYZ," the data processing system <NUM> can select a comment made by a user regarding the restaurant XYZ on a webpage hosted by the content provider device <NUM>. For example, the comment can be a review stating "Restaurant XYZ is a great place for a romantic dinner. " The data processing system <NUM> can include the comment in the response. For example, the response can be "Restaurant XYZ has favorable reviews. It was mentioned that restaurant XYZ is a great place for a romantic dinner.

The manipulation detection component <NUM> can detect manipulated text within digital components and then restrict the transmission of those digital components. For example, the digital component can be a text-based ad from which the manipulation detection component <NUM> can extract the text and determine if the text is manipulated.

The manipulation detection component <NUM> can determine if a given digital component includes manipulated text by retrieving a dictionary <NUM> from the data repository <NUM>. The dictionary <NUM> can indicate which characters (or symbols) are most likely to occur in digital components. The manipulation detection component <NUM> can select the dictionary <NUM> from a plurality of different dictionaries <NUM> based on the language or subject matter of the digital component being tested.

To determine if a digital component is manipulated, the manipulation detection component <NUM> can extract the text of the digital component as a character set. If the digital component is video-based or audio-based, the manipulation detection component <NUM> can generate a transcript of the words spoken in the digital component and then extract the text of the digital component as a character set. The manipulation detection component <NUM> can divide the full character set into different portions. For example, the manipulation detection component <NUM> can select and analyze only about <NUM> characters at a time. If the digital component (or a resulting character set) includes less than <NUM> characters, the manipulation detection component <NUM> can zero-pad the character set to reach the <NUM> length. If the character set includes more than <NUM> characters, the manipulation detection component <NUM> can use a sliding window to divide the character set into different portions that each include <NUM> characters. For example, each portion can be between about <NUM> and about <NUM>,<NUM> characters, between about <NUM> and about <NUM> characters, between about <NUM> and about <NUM> characters, between about <NUM> and about <NUM> characters, or between about <NUM> and about <NUM> characters.

The manipulation detection component <NUM> can generate a character array for the character set (or portions thereof if the character set is divided into different portions). The dictionary <NUM> can include a value or array for each character listed in the dictionary <NUM>. The manipulation detection component <NUM> can use the dictionary <NUM> to convert the character array from an array of characters into an array of integers. For example, each possible character can be represented in the dictionary <NUM> as an integer. The dictionary <NUM> can be used as a lookup table to convert the input character into an integer. In some implementations, the dictionary <NUM> only includes the characters that are most likely to occur in a non-manipulated digital component. The dictionary <NUM> can include one or more characters that can occur in non-manipulated or manipulated digital components. If the input character is not in the dictionary <NUM>, the manipulation detection component <NUM> can place a <NUM> in the character array for the character. The dictionary <NUM> can be one hot encoded to represent each character as a binary array. The character array can be one hot encoded once converted to an array of integers.

The manipulation detection component <NUM> can generate the at least one dictionary <NUM>. To generate the dictionaries <NUM>, the manipulation detection component <NUM> can receive a plurality of digital components. The digital components can be non-manipulated digital components. The manipulation detection component <NUM> can parse through the received digital components to determine the number of occurrences of each character in the received digital components. For example, the manipulation detection component <NUM> can tally the number of times "a," "b,", "c," and so on appear in the received digital components. The manipulation detection component <NUM> can tally the number of occurrences for all types of characters including, letters, numbers, emoji, and special characters. The list of all possible characters can be referred to as candidate characters.

The manipulation detection component <NUM> can rank the candidate characters based on the number of times each of the respective candidate characters occurred in the received digital components. The manipulation detection component <NUM> can generate the dictionary by selecting, from the ranked candidate characters, the candidate characters that occur most frequently. For example, the manipulation detection component <NUM> can select the most commonly occurring <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> characters (or any range therein) for a dictionary <NUM>. The dictionary <NUM> can include more than <NUM> characters.

The manipulation detection component <NUM> can generate different dictionaries <NUM> for different groupings or categories of digital components. The groupings can be language-based or subject matter-based. For example, the manipulation detection component <NUM> can receive a plurality of digital components of a specific language and generate a dictionary <NUM> specific to that language. In another example, the manipulation detection component <NUM> can receive a plurality of digital components each having subject matter pertaining to a specific category (e.g., mobile games) and generate a dictionary <NUM> for mobile games based on the digital components having subject matter pertaining to mobile games.

The manipulation detection component <NUM> can include a machine learning module that can classify the digital component into one of a plurality of classes. The machine learning module can include a long short-term memory (LSTM) model or other recurrent neural network for classifying the input character strings. The LSTM model can be implemented with softmax function in one or more layers of the network. The LSTM model can be a three level LSTM. The LSTM model can include between about <NUM> and <NUM>, between about <NUM> and about <NUM>, or between about <NUM> and about <NUM> nodes. The classes can include a manipulated class and a non-manipulated class. Each class can correspond to a respective output node.

The manipulation detection component <NUM> can process the character array character by character. For example, the classification of the character array can be based on the sequence (or order) of the characters in the character array. If the manipulation detection component <NUM> classifies the digital component into a classification associated with manipulated digital components, the manipulation detection component <NUM> can determine to not transmit the digital component to the client computing device <NUM>.

The system <NUM> can also include one or more client computing devices <NUM> and one or more content provider devices <NUM>. A client computing device <NUM> and a content provider computing device <NUM> can each include at least one logic device such as a computing device having a processor to communicate with each other or with the data processing system <NUM> via the network <NUM>. The client computing device <NUM> and the content provider computing device <NUM> can each include at least one server, processor or memory, or a plurality of computation resources or servers located in at least one data center. The client computing device <NUM> and the content provider computing device <NUM> can each include at least one computing device such as a desktop computer, laptop, tablet, personal digital assistant, smartphone, portable computer, server, thin client computer, virtual server, or other computing device.

The client computing device <NUM> can include at least one sensor <NUM>, at least one transducer <NUM>, at least one audio driver <NUM>, at least one speaker <NUM>, and at least one pre-processor <NUM>. The client computing device <NUM> can be a digital assistant device. The digital assistant device can be speaker-based. The sensor <NUM> can include a microphone (or other audio input sensor) or a camera. The sensor <NUM> can be referred to as an interface. Other interfaces can also include the networking connection of a computing device, screen, or input devices. The transducer <NUM> can convert the audio input into an electronic signal, or vice-versa. The audio driver <NUM> can include a script or program executed by one or more processors of the client computing device <NUM> to control the sensor <NUM>, the transducer <NUM> or the audio driver <NUM>, among other components of the client computing device <NUM> to process audio input or provide audio output. The speaker <NUM> can transmit the audio output signal.

The client computing device <NUM> can include a pre-processor component <NUM>. The pre-processor component <NUM> can include one or more processors. The pre-processor component <NUM> can perform one or more functions on the input audio signal before the input audio signal is processed by the data processing system <NUM> or other components of the client computing device <NUM>. The pre-processor component <NUM> can be coupled with the audio driver <NUM>, the transducer <NUM>, and the sensor140. The pre-processor component <NUM> can filter the input audio signals detected by the sensor <NUM> (or input audio signals that are otherwise received by the client computing device <NUM>) to create a filtered input audio signal. The filtering by the pre-processor <NUM> can include filtering (or reducing) noise in the input audio signal, the amplification of predetermined frequencies in the input audio signal, the reduction of predetermined frequencies in the input audio signal, or up-sampling or down-sampling of the input audio signal. The pre-processor component <NUM> can convert the filtered input audio signal to data packets and transmit the data packets to the data processing system <NUM> via the network <NUM>.

The client computing device <NUM> can be associated with an end user that enters voice-based, text-based, or image-based queries into the client computing device <NUM> (via an interface) and can receive a response to the queries from the data processing system <NUM>. The response can be in the same form as the query. For example, in response audio input query that is voice-based, the data processing system <NUM> (or the content provider device <NUM>) can provide an output in the form of a computer generated voice file to the client computing device <NUM>, which the client computing device <NUM> can output from the speaker <NUM>. The audio output can correspond to an action data structure received from the direct action API <NUM>, or a digital component selected by the content selector component <NUM>. The computer generated voice can include recordings from a real person or computer generated language.

The client computing device <NUM> can include an application, script, or program that is provided by (or associated with the data processing system <NUM>) that enables the client computing device <NUM> to communicate the input audio signals to at least one interface <NUM> of the data processing system <NUM>. The data processing system <NUM> can communicate with the application to enable the data processing system <NUM> to drive components of the client computing device <NUM> to render output audio signals (e.g., for action data structures) or other output signals (e.g., digital components).

The content provider computing device <NUM> can provide digital component for rending on the client computing device <NUM>. The content provider device <NUM> can transmit the digital component to the client computing device <NUM>, bypassing the data processing system <NUM>. The content provider device <NUM> can provide the digital components to the data processing system <NUM>, which can provide the digital components to the client computing device <NUM>. The digital components can be audio-based, text-based, or video-based digital components or action data structures for display by the client computing device <NUM> as an audio output. The action data structure or digital component can include an organic response or offer for a good or service, such as a voice based message that states: "Today it will be sunny and <NUM> degrees at the beach" as an organic response to a voice-input query of "Is today a beach day?". The data processing system <NUM> (or other system <NUM> component such as the content provider computing device <NUM> can also provide a digital component as a response, such as a voice or text message based digital component offering sunscreen.

The content provider computing device <NUM> can provide the digital components to the data processing system <NUM> for storage in the data repository <NUM>. The action data structures and digital components can include packet-based data structures for transmission via the network <NUM>. The content provider computing device <NUM> can also provide audio or text-based digital components (or other digital components) to the data processing system <NUM> where they can be stored in the data repository <NUM>. The data processing system <NUM> can select the audio action data structures or text-based digital components and provide (or instruct the content provider computing device <NUM> to provide) them to the same or different client computing devices <NUM> responsive to a query received from one of those client computing device <NUM>. The audio-based action data structures can be exclusively audio or can be combined with text, image, or video data. The digital components can be exclusively text or can be combined with audio, image or video data. The content provider device <NUM> can provide manipulated digital components.

The data repository <NUM> can include one or more local or distributed databases, and can include a database management system. The data repository <NUM> can include computer data storage or memory and can store one or more dictionaries <NUM>, one or more policies <NUM>, content data <NUM>, or templates <NUM> among other data. The policies <NUM> can include rules for the transmissions in a voice-based system. For example, the policies <NUM> can be used to define sessions, connections (and the establishment thereof) between the client computing device <NUM> and the data processing system <NUM>. The content data <NUM> can include digital components or associated metadata, as well as input audio messages that can be part of one or more communication sessions with the client computing device <NUM>. The metadata of the digital component can include an indication of the provider of the digital component (e.g., which content provider device <NUM>, or owner thereof, provided the digital component). The templates <NUM> can include data structures that can be used in communications the client computing device <NUM>. The templates <NUM> can include one or more place holders that the data processing system <NUM> can fill with, for example, content data <NUM>, digital components, or other data.

The dictionaries <NUM> can be generated by the manipulation detection component <NUM>. The manipulation detection component <NUM> can generate or update the dictionaries <NUM> at predetermined intervals. Each manipulation detection component <NUM> can indicate the most common characters across a plurality of non-manipulated digital components. The dictionaries <NUM> can be generated by inputting a large number of authentic digital components to the manipulation detection component <NUM>. The dictionaries <NUM> can be one hot encoded. The manipulation detection component <NUM> can include an embedding layer for the dictionaries <NUM>. For example, rather than one-hot encoded vectors, the manipulation detection component <NUM> can use an embedding matrix to keep the size of each dictionary <NUM> smaller than when compared to using one hot encoding only.

The data repository <NUM> can include different dictionaries <NUM> for different languages (e.g., a U. English dictionary, a British English dictionary, a French dictionary) or different subject matter dictionaries (e.g., a science dictionary or a clothing dictionary). The different dictionaries <NUM> can be generated by providing digital components related to that language or subject matter to the manipulation detection component <NUM>. For example, a plurality of digital components related to clothing (e.g., clothing ads) can be provided to the manipulation detection component <NUM> to generate the clothing dictionary.

The action data structures can include a number of organic or non-sponsored responses to the input audio signal. For example, the action data structures can include a beach weather forecast or directions to a beach. The action data structures in this example include organic, or non-sponsored, content that is directly responsive to the input audio signal. The digital components responsive to the input audio signal can include sponsored or non-organic content, such as an offer to buy sunscreen from a convenience store located near the beach. In this example, the organic action data structure (beach forecast) is responsive to the input audio signal (a query related to the beach), and the digital component (a reminder or offer for sunscreen) is also responsive to the same input audio signal. The data processing system <NUM> can evaluate system <NUM> parameters (e.g., power usage, available displays, formats of displays, memory requirements, bandwidth usage, power capacity or time of input power (e.g., internal battery or external power source such as a power source from a wall output)) to provide the action data structure and the digital component to different candidate interfaces on the same client computing device <NUM>, or to different candidate interfaces on different client computing devices <NUM>.

The NLP component <NUM> can convert the input audio signal into recognized text by comparing the input signal against a stored, representative set of audio waveforms (e.g., in the data repository <NUM>) and choosing the closest matches. The representative waveforms are generated across a large set of users, and can be augmented with speech samples. After the audio signal is converted into recognized text, the NLP component <NUM> can match the text to words that are associated, for example via training across users or through manual specification, with actions that the data processing system <NUM> can serve. In some implementations, the manipulation detection component <NUM> can process or otherwise analyze input audio signals without first converting the audio waveforms into text.

The input audio signal can be detected by the sensor <NUM> (e.g., a microphone) of the client computing device <NUM>. The sensor <NUM> can be referred to as an interface of the client computing device <NUM>. Via the transducer <NUM>, the audio driver <NUM>, or other components, the client computing device <NUM> can provide the input audio signal to the data processing system <NUM> (e.g., via the network <NUM>) where it can be received (e.g., by the interface <NUM>) and provided to the NLP component <NUM> or stored in the data repository <NUM> as content data <NUM>.

The NLP component <NUM> can receive or otherwise obtain the input audio signal. From the input audio signal, the NLP component <NUM> can identify at least one request or at least one trigger keyword corresponding to the request. The request can indicate intent or subject matter of the input audio signal. The trigger keyword can indicate a type of action likely to be taken. For example, the NLP component <NUM> can parse the input audio signal to identify at least one request to go to the beach for the weekend. The trigger keyword can include at least one word, phrase, root or partial word, or derivative indicating an action to be taken. For example, the trigger keyword "go" or "to go to" from the input audio signal can indicate a need for transport or a trip away from home. In this example, the input audio signal (or the identified request) does not directly express an intent for transport, however the trigger keyword indicates that transport is an ancillary action to at least one other action that is indicated by the request.

The NLP component <NUM> can identify sentiment keywords or sentiment states in the input audio signal. The sentiment keywords or state can indicate the attitude of the user at the time the user provides the input audio signal. The content selector component <NUM> can use the sentiment keywords and states to select digital components. Based, for example, on the sentiment keywords and states, the content selector component <NUM> may skip the selection of a digital component. For example, if the NLP component <NUM> detects sentiment keywords such as "only" or "just" (e.g., "Ok, just give me the results for the movie times"), the content selector component <NUM> may skip the selection of a digital component such that only an action data structure is returned in response to the input audio signal.

The audio signal generator component <NUM> can generate or otherwise obtain an output signal that includes the digital component (as well as the action data structure) responsive to the input audio signal. For example, the data processing system <NUM> can execute the audio signal generator component <NUM> to generate or create an output signal corresponding to the action data structure or to the digital component. For example, the audio signal generator component <NUM> can convert a text-based digital component into an audio-based digital component. The interface component <NUM> of the data processing system <NUM> can provide or transmit one or more data packets that include the output signal via the computer network <NUM> to any client computing device <NUM>. The interface <NUM> can be designed, configured, constructed, or operational to receive and transmit information using, for example, data packets. The interface <NUM> can receive and transmit information using one or more protocols, such as a network protocol. The interface <NUM> can include a hardware interface, software interface, wired interface, or wireless interface. For example, the interface <NUM> can be a network interface or port of the data processing system <NUM>. The interface <NUM> can facilitate translating or formatting data from one format to another format. For example, the interface <NUM> can include an application programming interface that includes definitions for communicating between various components, such as software components of the system <NUM>.

The data processing system <NUM> can provide the output signal including the action data structure from the data repository <NUM> or from the audio signal generator component <NUM> to the client computing device <NUM>. The data processing system <NUM> can provide the output signal including the digital component from the data repository <NUM> or from the audio signal generator component <NUM> to the same or to a different client computing device <NUM>.

The data processing system <NUM> can also instruct, via data packet transmissions, the content provider computing device <NUM> or other computing device to provide the output signal (e.g., corresponding to the action data structure or to the digital component) to the client computing device <NUM>. The output signal can be obtained, generated, transformed to or transmitted as one or more data packets (or other communications protocol) from the data processing system <NUM> (or other computing device) to the client computing device <NUM>.

The content selector component <NUM> can identify, select, or obtain multiple digital components resulting from a multiple content selection processes. The content selection processes can be near real-time, e.g., part of the same conversation, communication session, or series of communications sessions between the data processing system <NUM> and the client computing device <NUM> that involve common subject matter. The conversation can include asynchronous communications separated from one another by a period of hours or days, for example. The conversation or communication session can last for a time period from receipt of the first input audio signal until an estimated or known conclusion of a final action related to the first input audio signal, or receipt by the data processing system <NUM> of an indication of a termination or expiration of the conversation. For example, the data processing system <NUM> can determine that a conversation related to a weekend beach trip begins at the time or receipt of the input audio signal and expires or terminates at the end of the weekend, e.g., Sunday night or Monday morning. The data processing system <NUM> that provides action data structures or digital components for rendering by one or more interfaces of the client computing device <NUM> or of another client computing device <NUM> during the active time period of the conversation (e.g., from receipt of the input audio signal until a determined expiration time) can be considered to be operating in real-time. In this example the content selection processes and rendering of the digital components and action data structures occurs in real time.

From the information received by the content selector component <NUM>, e.g., an indication of a forthcoming trip to the beach, the content selector component <NUM> can identify at least one digital component. The digital component can be responsive or related to the subject matter of the input audio query. For example, the digital component can include a data message identifying a store near the beach that has sunscreen, or offering a taxi ride to the beach. The content selector component <NUM> can query the data repository <NUM> to select or otherwise identify the digital component, e.g., from the content data <NUM>. The content selector component <NUM> can also select the digital component from the content provider computing device <NUM>. For example, responsive to a query received from the data processing system <NUM>, the content provider computing device <NUM> can provide a digital component to the data processing system <NUM> (or component thereof) for eventual output by the client computing device <NUM> that originated the input audio signal, or for output to the same end user by a different client computing device <NUM>.

The content selector component <NUM> can select the digital component or the action data structure as part of a real-time content selection process. For example, the action data structure can be provided to the client computing device <NUM> for transmission as audio output by an interface of the client computing device <NUM> in a conversational manner in direct response to the input audio signal. The real-time content selection process to identify the action data structure and provide the digital component to the client computing device <NUM> can occur within one minute or less from the time of the input audio signal and be considered real-time. The data processing system <NUM> can also identify and provide the digital component to at least one interface of the client computing device <NUM> that originated the input audio signal, or to a different client computing device <NUM>.

The action data structure (or the digital component), for example obtained or generated by the audio signal generator component <NUM> transmitted via the interface <NUM> and the computer network <NUM> to the client computing device <NUM>, can cause the client computing device <NUM> to execute the audio driver <NUM> to drive the speaker <NUM> to generate an acoustic wave corresponding to the action data structure or to the digital component. The acoustic wave can include words of or corresponding to the action data structure or digital component.

The action data structures and digital components can correspond to subject matter of the input audio signal. The direct action API <NUM> can execute programs or scripts, for example from the NLP component <NUM> or the content selector component <NUM>, to identify action data structures or digital components for one or more of these actions. The direct action API <NUM> can execute a specified action to satisfy the end user's intention, as determined by the data processing system <NUM>. Depending on the action specified in its inputs, the direct action API <NUM> can execute code or a dialog script that identifies the parameters required to fulfill a user request. Such code can look up additional information, e.g., in the data repository <NUM>, such as the name of a home automation service, or it can provide audio output for rendering at the client computing device <NUM> to ask the end user questions such as the intended destination of a requested taxi. The direct action API <NUM> can determine necessary parameters and can package the information into an action data structure, which can then be sent to another component such as the content selector component <NUM> or the content provider device <NUM> to be fulfilled.

The direct action API <NUM> of the data processing system <NUM> can generate, based on the request or the trigger keyword, the action data structures. The action data structures can be generated responsive to the subject matter of the input audio signal. Based on the input audio signal parsed by the NLP component <NUM>, the direct action API <NUM> can determine to which, if any, of a plurality of content provider device <NUM> the message should be sent. For example, if an input audio signal includes "OK, I would like to go to the beach this weekend," the NLP component <NUM> can parse the input audio signal to identify requests or trigger keywords such as the trigger keyword "to go to" as an indication of a need for a taxi. The direct action API <NUM> can package the request into an action data structure for transmission as a message to a content provider computing device <NUM> of a taxi service. The message can also be passed to the content selector component <NUM>. The action data structure can include information for completing the request. In this example, the information can include a pick up location (e.g., home) and a destination location (e.g., a beach). The direct action API <NUM> can retrieve a template <NUM> from the data repository <NUM> to determine which fields to include in the action data structure. The direct action API <NUM> can retrieve content from the data repository <NUM> to obtain information for the fields of the data structure. The direct action API <NUM> can populate the fields from the template with that information to generate the data structure. The direct action API <NUM> can also populate the fields with data from the input audio signal. The templates <NUM> can be standardized for categories of content providers or can be standardized for specific content providers. For example, ride sharing providers can use the following standardized template <NUM> to create the data structure:
{client_device_identifier; authentication_credentials; pick_up_location; destination_location; no_passengers; service_level}.

The data processing system <NUM> can also provide the action data structure with a prompt that queries the user to determine user interest in obtaining the digital component. For example, the action data structure can indicate "it will be sunny and <NUM> degrees at the beach on Saturday, would you like to hear about some services to assist with your trip?" The data processing system <NUM> can receive another input audio signal from the client computing device <NUM> in response to the prompt "would you like to hear about some services to assist with your trip?" such as "sure". The NLP component <NUM> can parse this response and interpret it as authorization for audio rendering of the digital component by the client computing device <NUM>. In response, the data processing system <NUM> can provide the digital component for audio rendering by the same client computing device <NUM> from which the response "sure" originated.

The data processing system <NUM> can delay transmission of the digital component associated with the action data structure to optimize processing utilization. For example, the data processing system <NUM> provide the action data structure for rendering as audio output by the client computing device in real-time responsive to receipt of the input audio signal, e.g., in a conversational manner, and can delay digital component transmission until an off-peak or non-peak period of data center usage, which results in more efficient utilization of the data center by reducing peak bandwidth usage, heat output, or cooling requirements. The data processing system <NUM> can also initiate a conversion or other activity associated with the digital component, such as ordering a car service responsive to a response to the action data structure or to the digital component, based on data center utilization rates or bandwidth metrics or requirements of the network <NUM> or of a data center that includes the data processing system <NUM>.

Based on a response to a digital component or to the action data structure for a subsequent action, such as a click on the digital component rendered via the selected interface, the data processing system <NUM> can identify a conversion, or initiate a conversion or action. Processors of the data processing system <NUM> can invoke the direct action API <NUM> to execute scripts that facilitate the conversion action, such as to order a car from a car share service to take the end user to or from the beach. The direct action API <NUM> can obtain content data <NUM> (or parameters or policies <NUM>) from the data repository <NUM>, as well as data received with end user consent from the client computing device <NUM> to determine location, time, user accounts, logistical or other information in order to reserve a car from the car share service.

<FIG> depicts a flow diagram <NUM> to restrict the transmission of digital components contained manipulated content. The method <NUM> can include receiving a request (ACT <NUM>). The method <NUM> can include selecting a digital component (ACT <NUM>). The method <NUM> can include retrieving a dictionary (ACT <NUM>). The method <NUM> can include generating a character array (ACT <NUM>). The method <NUM> can include classifying the digital component (ACT <NUM>). The method <NUM> can include determining to not include the digital component in a response (ACT <NUM>). The method <NUM> can include generating the response (ACT <NUM>).

As set forth above, the method <NUM> can include receiving a request (ACT <NUM>). The data processing system <NUM> can receive the request from the client computing device <NUM>. The request can be text-based, image-based, or audio-based. The data processing system <NUM> can receive the request at an interface, such as a network interface or other interface. For audio-based input signals, the data processing system <NUM> can execute the NLP component <NUM>, which can parse the input signal to identify the request and one or more trigger keywords in the input audio signal.

The method <NUM> can include selecting a digital component (ACT <NUM>). The content selector component <NUM> can select the digital component. The content selector component <NUM> can select the digital component based on the request and the one or more trigger keywords identified in the request. The selected digital component can include text (e.g., one or more characters). The digital component can be user-generated content, such as user-provided comments or reviews. When the digital components are video-based or audio-based, the manipulation detection component <NUM> can transcribe the audio from the digital component into a string of characters. The manipulation detection component <NUM> can determine whether or not to process the digital component based on the selected digital component or its metadata. For example, the metadata can indicate that the digital component was received from a trusted content provider device <NUM> and the manipulation detection component <NUM> can determine to include the digital component in a response without determining whether the digital component includes manipulated text because the digital component was received from a trusted content provider device <NUM>.

The method <NUM> can include retrieving a dictionary (ACT <NUM>). The dictionary can indicate the characters most often found in non-manipulated digital components. The dictionary can include a portion of the total possible characters that can be used in the digital components. The characters in the dictionary can be one hot encoded to form a matrix of one hot encoded characters. For example, each character can form a row of the matrix with the values from the binary array generated from the one hot encoding forming the columns of the row.

The data repository <NUM> can include a plurality of different dictionaries <NUM>. The data processing system <NUM> can generate and store the different dictionary <NUM> for different groupings of digital components. The digital components can be grouped based on the language used in the digital components, region in which the digital component was generated (or is intended to be displayed), or the digital component's subject matter. The manipulation detection component <NUM> can identify the grouping with which the selected digital component is associated (e.g., which language or subject matter category) and then select the corresponding dictionary <NUM>.

Also referring to <FIG>, the method <NUM> can include generating a character array (ACT <NUM>). <FIG> illustrates the process of dividing a digital component <NUM> into a plurality of portions <NUM> and then converting the portions <NUM> into at least one character array <NUM>.

As illustrated in <FIG>, among others, the digital component <NUM> can include a plurality of characters <NUM>. The manipulation detection component <NUM> can divide the plurality of characters into portions <NUM>. The manipulation detection component <NUM> can analyze character arrays of a predetermined length. For example, the manipulation detection component <NUM> may generate and analyze character arrays that are between about <NUM> and about <NUM> characters, between about <NUM> and about <NUM> characters, between about <NUM> and about <NUM> characters, between about <NUM> and about <NUM> characters, or between about <NUM> and about <NUM> characters in length. When, as illustrated in <FIG>, the digital component <NUM> includes more characters than the predetermined length, the manipulation detection component <NUM> can divide the plurality of characters into different portions that are each of a predetermined length (e.g., <NUM> characters).

When a portion is less than the predetermined length, the portion can be zero-padded to reach the predetermined length. When a digital component <NUM> is less than the predetermined length in total, the manipulation detection component <NUM> can generate a single portion that is zero-padded to reach the predetermined length. The different portions can be generated by a sliding window. In some implementations, there is no overlap between the portions. In some examples there can be between <NUM>% and about <NUM>%, between about <NUM>% and about <NUM>%, between about <NUM>% and about <NUM>%, or between about <NUM>% and about <NUM>% overlap between the portions. As illustrated in <FIG>, the portions <NUM>(<NUM>) and <NUM>(<NUM>) are generated by sliding the window a distance of one character. In the example illustrated in <FIG>, if each portion has a length of n characters <NUM>, the overlap is (n-<NUM>) characters <NUM>.

Referring to <FIG> and <FIG>, among others, the manipulation detection component <NUM> can convert each of the portions <NUM> into character arrays <NUM> by using the dictionary <NUM> selected and retrieved at ACT <NUM>. For example, as illustrated in <FIG>, the portion <NUM>(<NUM>) can be converted into the character array <NUM>(<NUM>) and the portion <NUM>(<NUM>) can be converted into the character array <NUM>(<NUM>). The character arrays <NUM> can be a data structure that includes an array of values. Each character <NUM> in a portion <NUM> can be converted to a value (or integer) in the character array <NUM> by using the character <NUM> as an input to the dictionary <NUM> which can be used as a lookup table for converting between characters <NUM> and their respective integers. The character arrays <NUM> can be one hot encoded. For example, each value of the character array <NUM> can be converted into a binary array. The dictionary index can be one hot encoded to generate one hot encoded character arrays when converting the characters to integers.

Referring to <FIG>, among others, the method <NUM> can include classifying the digital component (ACT <NUM>). The manipulation detection component <NUM> can classify the digital component into a first class of a plurality of classes. The classification by the manipulation detection component <NUM> can be based on a sequence (or order) of the first plurality of characters in the character array. For example, the characters from the digital component (or one hot encoded versions of the characters) can be sequentially fed into the manipulation detection component's classifier as inputs. The classifier can have two output classes: a manipulated class and a non-manipulated class. The classifier can be, include, or be part of a long short-term memory neural network.

The method <NUM> can include determining to not include the digital component in a response (ACT <NUM>). The determination to not include the digital component into the response to the content request from the client computing device can be based on the classification of the digital component into the manipulated class. If, for example, the digital component is classified into the manipulated class, the digital component can be removed or purged from the data repository <NUM> or restricted or excluded from further consideration during the content selection process (e.g., ACT <NUM>).

The method <NUM> can include generating a response (ACT <NUM>). The response can be generated responsive to the request received at ACT <NUM>. The data processing system <NUM> can generate the response to not include the digital component selected at ACT <NUM>. The decision to not include the digital component can be based on the classification of the digital component in the manipulated category.

The manipulation detection component <NUM> can instruct the content selector component <NUM> to select a second digital component based on determining to not include the originally selected digital component in the response because the selected digital component was classified into the manipulated category. The manipulation detection component <NUM> can repeat ACTS <NUM>-<NUM> to classify the second digital component. The manipulation detection component <NUM> can repeat the above ACTs until a digital component is selected that is classified into the non-manipulated class. The digital component classified into the non-manipulated class can be included into the response.

The manipulation detection component <NUM> can classify a content provider device <NUM> as an untrusted content provider if the manipulation detection component <NUM> determines that a predetermined number of digital components from the content provider device <NUM> are classified as manipulated. Once classified as an untrusted content provider, the data processing system <NUM> may not request (or may automatically reject) digital components from the untrusted content provider. The manipulation detection component <NUM> can classify a content provider device <NUM> as a trusted content provider if the manipulation detection component <NUM> determines that a predetermined number of digital components from the content provider device <NUM> are classified as non-manipulated. Based on being classified as a trusted content provider device, the manipulation detection component <NUM> may not process the digital components from the trusted content provider device to determine if they include manipulated text or the manipulation detection component <NUM> can only process a portion of the digital components form the trusted content provider device. For example, the manipulation detection component <NUM> may only process every nth digital component from the trusted content provider device. The manipulation detection component <NUM> can mark the content provider device <NUM> as a trusted content provider based on establishing the owner or authenticity of the content provider device <NUM>. For example, the data processing system <NUM> can flag a content provider device <NUM> as a trusted content provider if, for example, during registration with the data processing system <NUM>, the content provider device's identification can be verified.

The input request can be voice-based and the digital component that is included in the response can be text-based, such as a user generated review or comment. The audio signal generator component can convert the digital component into a output audio file to be transmitted and rendered at the client computing device <NUM>.

The computer system or computing device <NUM> can include or be used to implement the system <NUM>, or its components such as the data processing system <NUM>. The computing system <NUM> includes a bus <NUM> or other communication component for communicating information and a processor <NUM> or processing circuit coupled to the bus <NUM> for processing information. The computing system <NUM> can also include one or more processors <NUM> or processing circuits coupled to the bus for processing information. The computing system <NUM> also includes main memory <NUM>, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus <NUM> for storing information, and instructions to be executed by the processor <NUM>. The main memory <NUM> can be or include the data repository <NUM>. The main memory <NUM> can also be used for storing position information, temporary variables, or other intermediate information during execution of instructions by the processor <NUM>. The computing system <NUM> may further include a read only memory (ROM) <NUM> or other static storage device coupled to the bus <NUM> for storing static information and instructions for the processor <NUM>. A storage device <NUM>, such as a solid state device, magnetic disk or optical disk, can be coupled to the bus <NUM> to persistently store information and instructions. The storage device <NUM> can include or be part of the data repository <NUM>.

The computing system <NUM> may be coupled via the bus <NUM> to a display <NUM>, such as a liquid crystal display, or active matrix display, for displaying information to a user. An input device <NUM>, such as a keyboard including alphanumeric and other keys, may be coupled to the bus <NUM> for communicating information and command selections to the processor <NUM>. The input device <NUM> can include a touch screen display <NUM>. The input device <NUM> can also include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor <NUM> and for controlling cursor movement on the display <NUM>. The display <NUM> can be part of the data processing system <NUM>, the client computing device <NUM> or other component of <FIG>, for example.

For situations in which the systems discussed herein collect personal information about users, or may make use of personal information, the users may be provided with an opportunity to control whether programs or features that may collect personal information (e.g., information about a user's social network, social actions or activities, a user's preferences, or a user's location), or to control whether or how to receive content from a content server or other data processing system that may be more relevant to the user. In addition, certain data may be anonymized in one or more ways before it is stored or used, so that personally identifiable information is removed when generating parameters. For example, a user's identity may be anonymized so that no personally identifiable information can be determined for the user, or a user's geographic location may be generalized where location information is obtained (such as to a city, postal code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about him or her and used by the content server.

The subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. The subject matter described in this specification can be implemented as one or more computer programs, e.g., one or more circuits of computer program instructions, encoded on one or more computer storage media for execution by, or to control the operation of, data processing apparatuses. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. While a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate components or media (e.g., multiple CDs, disks, or other storage devices).

The terms "data processing system," "computing device," "component," or "data processing apparatus" encompass various apparatuses, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations of the foregoing. The apparatus can include special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The direct action API <NUM>, content selector component <NUM>, or NLP component <NUM> and other data processing system <NUM> components can include or share one or more data processing apparatuses, systems, computing devices, or processors.

A computer program (also known as a program, software, software application, app, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can correspond to a file in a file system. A computer program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).

The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs (e.g., components of the data processing system <NUM>) to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatuses can also be implemented as, special purpose logic circuitry, e.g., an FPGA or an ASIC. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.

The subject matter described herein can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back-end, middleware, or front-end components.

The computing system such as system <NUM> or system <NUM> can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network (e.g., the network <NUM>). In some implementations, a server transmits data (e.g., data packets representing action data structures or digital components) to a client device (e.g., to the client computing device <NUM> for purposes of displaying data to and receiving user input from a user interacting with the client device, or the content provider computing device <NUM>). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server (e.g., received by the data processing system <NUM> from the computing device <NUM> or the content provider computing device <NUM>).

The separation of various system components does not require separation in all implementations, and the described program components can be included in a single hardware or software product. For example, the NLP component <NUM> or the content selector component <NUM> can be a single component, app, or program, or a logic device having one or more processing circuits, or part of one or more servers of the data processing system <NUM>.

Having now described some illustrative implementations, it is apparent that the foregoing is illustrative and not limiting, having been presented by way of example. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements, and features discussed in connection with one implementation are not intended to be excluded from a similar role in other implementations or implementations.

The use of "including," "comprising," "having," "containing," "involving," "characterized by," "characterized in that," and variations thereof herein, is meant to encompass the items listed thereafter, equivalents thereof, and additional items, as well as alternate implementations consisting of the items listed thereafter exclusively.

Any implementation disclosed herein may be combined with any other implementation or embodiment, and references to "an implementation," "some implementations," "one implementation," or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the implementation may be included in at least one implementation or embodiment. Such terms as used herein are not necessarily all referring to the same implementation. Any implementation may be combined with any other implementation, inclusively or exclusively, in any manner consistent with the aspects and implementations disclosed herein.

References to "or" may be construed as inclusive so that any terms described using "or" may indicate any of a single, more than one, and all of the described terms. For example, a reference to "at least one of 'A' and 'B'" can include only 'A', only 'B', as well as both 'A' and 'B'. Such references used in conjunction with "comprising" or other open terminology can include additional items.

Claim 1:
A data processing system (<NUM>) to restrict content transmission in a networked environment, comprising:
an interface (<NUM>) configured to receive a content request from a client computing device (<NUM>);
a natural language processor (<NUM>) configured to:
receive an input audio signal comprising the content request;
parse the input audio signal to identify the content request and a trigger keyword;
a content selector component (<NUM>) which, when executed by the data processing system, is configured to select a first digital component based on the content request and the trigger keyword, the first digital component comprising a first plurality of characters; and
a manipulation detection component (<NUM>) which, when executed by the data processing system, is configured to:
retrieve a dictionary (<NUM>) comprising a plurality of candidate characters included in a plurality of digital components and a value or binary array for each candidate character listed in the dictionary;
convert the first plurality of characters into an array of values or an array of binary arrays using the dictionary;
classify the first digital component into a non-manipulated class or into a manipulated class based on a sequence of the first plurality of characters in the array of values or the array of binary arrays;
based on a classification of the first digital component into the non-manipulated class, determine to include the first digital component in a response to the content request from the client computing device;
based on a classification of the first digital component into the manipulated class, determine not to include the first digital component in the response to the content request from the client computing device; and
generate the response based on the content request from the client computing device, wherein the response includes the first digital component when the first digital component is classified into the non-manipulated class and the response does not include the first digital component when the first digital component is classified into the manipulated class.