Abstract: Systems and methods for transcription analysis of a recording are provided. The recording includes an audio and screenshot/video portion. The audio portion is transcribed using a machine learned model. Models may be selected by the recording quality and potentially accents or other speech patterns that are present. The transcription is then linked to the video/screen capture chronology, so that automatic scrolling is enabled, clip selection from the transcription, and searching to a video time is possible. There is improvements to user experience question generation, review of study results, and in managing the study participants.

Abstract: Advanced user experience study can be accomplished by enabling recording on a device such as a smart phone, providing a participant with one or more survey questions and a task. Typically, the participants are recorded and redirected to a starting URL. The recording can be terminated when the participant reaches a validation condition. Results, for example, videos, survey results, click flows, and heat maps may be filterable across any participant feature or any study validation criteria. Validation criteria includes time taken to complete a given action, ending up at a particular URL (or class of URLs), based upon question answers, or any combination thereof. These criteria can be classified as either a successful completion of the study, or a failed attempt. Additionally, the validation criteria can also include a decision by the participant to abandon the study.

Abstract: Systems and methods for AI assisted analysis of a user experience study are provided. A study objective (a goal of the study) and data relating to all possible navigation routes within a digital interface are received. Simulated clickstreams for navigating from any state of the digital interface to the study objective are generated. This simulated clickstream data is then used to train one or more machine learning models to determine a most efficient path to achieve the study objective from any state of the digital interface. Subsequently, study results from many different participants is received. Key events are then identified within the study results. Additionally, the likelihood of failure for each of the plurality of study results is predicted using the machine learning model, and information density of the plurality of study results is determined.

Abstract: Systems and methods for sourcing participants for a usability study are provided. In some embodiments the systems and methods receive study parameters including the type of study, time-to-field of the study, required number of participants, and required participant attributes. Additionally, a set of business rules for the study are received. These business rules may be received from a client, extrapolated from a service contract with a client for which the study is being performed, or generated based on the monitored outcomes of sourcing of previous studies. Next, panel sources for potential participants and pricing data are queried, and a set of the sources are selected based upon the pricing data. Participants are then received from these sources, which are then fielded in the study and monitored for outcomes.

Abstract: The present disclosure relates to the field of communications technologies, and discloses a channel quality information determining method, an apparatus, and a system. The method may include: a terminal device obtains a configured TBS, and/or determines channel quality information of each of K segments of frequency domain resources based on the configured TBS and/or a time domain resource range. A frequency domain resource of the terminal device may be divided into N segments, where both N and K are integers greater than or equal to 1, and K is less than or equal to N. In some embodiments, the configured TBS may be fully considered when the channel quality information is determined, so that a network device properly may determine, based on the channel quality information, a frequency domain resource used to transmit a data packet.

Abstract: Systems and methods for selecting participants for a user experience study are provided. In some embodiments the systems and methods first receive at least three features for each participant profile. The participant profile is scored by quantile-based discretization. Next the participants are grouped into clusters using an unsupervised machine learning (ML) clustering algorithm(s) for each participant profile. The clusters are ranked using a number of models. These models are a function of geography and study type. The participant profile is assigned to a single cluster for each model. Participants are sampled from the clusters by their ranking.

Abstract: Systems and methods for transcription analysis of a recording are provided. The recording includes an audio and screenshot/video portion. The audio portion is transcribed using a machine learned model. Models may be selected by the recording quality and potentially accents or other speech patterns that are present. The transcription is then linked to the video/screen capture chronology, so that automatic scrolling is enabled, clip selection from the transcription, and searching to a video time is possible. There is improvements to user experience question generation, review of study results, and in managing the study participants.

Abstract: Systems and methods for generating, administering and analyzing a user experience study are provided. In particular, intents can be generated from a user experience study by applying one or more screener questions to participants and subjecting the screened participants to one or more tasks. Corresponding clickstreams and success data for each participant engaging in the tasks can be recorded. The success and clickstream data can also be aggregated for all the screened participants as aggregated results. Video data including audio for each of the screened participants can also be recorded.

Abstract: Systems and methods for sourcing participants for a usability study are provided. In some embodiments the systems and methods receive study parameters including the type of study, time-to-field of the study, required number of participants, and required participant attributes. Additionally, a set of business rules for the study are received. These business rules may be received from a client, extrapolated from a service contract with a client for which the study is being performed, or generated based on the monitored outcomes of sourcing of previous studies. Next, panel sources for potential participants and pricing data are queried, and a set of the sources are selected based upon the pricing data. Participants are then received from these sources, which are then fielded in the study and monitored for outcomes.

Abstract: Systems and methods for AI assisted analysis of a user experience study are provided. A study objective (a goal of the study) and data relating to all possible navigation routes within a digital interface are received. Simulated clickstreams for navigating from any state of the digital interface to the study objective are generated. This simulated clickstream data is then used to train one or more machine learning models to determine a most efficient path to achieve the study objective from any state of the digital interface. Subsequently, study results from many different participants is received. Key events are then identified within the study results. Additionally, the likelihood of failure for each of the plurality of study results is predicted using the machine learning model, and information density of the plurality of study results is determined.

Abstract: Systems and methods for generating and analyzing a user experience score (QXscore) are provided. A plurality of success metrics for tasks on a website are collected and averaged together to generate a single success percentage value. This percentage value may be converted into a five-point scale (the behavior metric). Users are presented a series of statements and a question which are averaged to yield a score for the trust, appearance, ease of user (usability), and loyalty attitudes. Subsequently, all four attitude measurements are averaged to generate the final attitude value. This average may be a straight average, or may be a weighted average. The final attitude value and the behavior metric are then combined (again either by straight or weighted averaging) to generate the raw QXscore. This can be converted back into a 100-point scale for assisting in the comparison of the given QXscore to other websites' QXscore values.

Abstract: Systems and methods for transcription analysis of a recording are provided. The recording includes an audio and screenshot/video portion. The audio portion is transcribed using a machine learned model. Models may be selected by the recording quality and potentially accents or other speech patterns that are present. The transcription is then linked to the video/screen capture chronology, so that automatic scrolling is enabled, clip selection from the transcription, and searching to a video time is possible. There is improvements to user experience question generation, review of study results, and in managing the study participants.

Abstract: Systems and methods for AI assisted analysis of a user experience study are provided. A study objective (a goal of the study) and data relating to all possible navigation routes within a digital interface are received. Simulated clickstreams for navigating from any state of the digital interface to the study objective are generated. This simulated clickstream data is then used to train one or more machine learning models to determine a most efficient path to achieve the study objective from any state of the digital interface. Subsequently, study results from many different participants is received. Key events are then identified within the study results. Additionally, the likelihood of failure for each of the plurality of study results is predicted using the machine learning model, and information density of the plurality of study results is determined.

Abstract: A system for performing remote usability testing of a website includes a module for generating particular tasks, a module for moderating a session with a number of participants, and a module for receiving usability data. The system further includes an analytics module for analyzing the received usability data. The module for generating the particular tasks includes a research server configured to interface with user experience researchers and storing multiple testing modules for selecting qualified participants from the number of participants and for generating the particular tasks having research metrics associated with a target web site. In an embodiment, the research server randomly assigns one or more of the multiple testing modules to one of the participants. The multiple testing modules may include card sorting studies for optimizing a web site's architecture or layout.

Abstract: Systems and methods for generating, administering and analyzing a click test study are provided. This includes uploading a static image and selecting portions of the static image as answers. The study author also generates a prompt for a participant, which is then presented, along with the static image. The participant then clicks with a cursor, or other selection mechanism, and the selection location(s) are recorded. Delay between the prompt display and the click is measuring in some embodiments. The recorded clicks are used to generate a heat map when aggregated with the results of other users. Likewise, the at least one click location may be compared to the selected portions to generate success metrics. In addition to the regular heat map, a high-contrast negative image heat map may likewise be generated.

Abstract: Systems and methods for sourcing participants for a usability study are provided. In some embodiments the systems and methods receive study parameters including the type of study, time-to-field of the study, required number of participants, and required participant attributes. Additionally, a set of business rules for the study are received. These business rules may be received from a client, extrapolated from a service contract with a client for which the study is being performed, or generated based on the monitored outcomes of sourcing of previous studies. Next, panel sources for potential participants and pricing data are queried, and a set of the sources are selected based upon the pricing data. Participants are then received from these sources, which are then fielded in the study and monitored for outcomes.

Abstract: Systems and methods for sourcing participants for a usability study are provided. In some embodiments the systems and methods receive study parameters including the type of study, time-to-field of the study, required number of participants, and required participant attributes. Additionally, a set of business rules for the study are received. These business rules may be received from a client, extrapolated from a service contract with a client for which the study is being performed, or generated based on the monitored outcomes of sourcing of previous studies. Next, panel sources for potential participants and pricing data are queried, and a set of the sources are selected based upon the pricing data. Participants are then received from these sources, which are then fielded in the study and monitored for outcomes.

Abstract: Systems and methods for improving an algorithm using physically or logically partitioned datasets, in compliance with use restrictions, is provided. The datasets belong to various customers in local data modules. A policy database is queried for use restrictions (contractual or legal) on the partitioned datasets. The datasets are then cataloged, and a set of functionalities are generated and then deployed. This deployment may include running the functionality on a central system on partitions of the datasets adhering to the restrictions, or packaging the functionality into discrete bins for each of the datasets, and delivering the bins to local computational devices at each local data module. The output of the functionalities are then aggregated, typically with a weighted function, and then used to train a unified algorithm.

Abstract: Systems and methods for generating, administering and analyzing a user experience study are provided. In particular, intents can be generated from a user experience study by applying one or more screener questions to participants and subjecting the screened participants to one or more tasks. Corresponding clickstreams and success data for each participant engaging in the tasks can be recorded. The success and clickstream data can also be aggregated for all the screened participants as aggregated results. Video data including audio for each of the screened participants can also be recorded.

Abstract: Systems and methods for monitored usability testing are provided. A participant is identified, scheduled and their system is checked for suitability. The participant is placed in a waiting room and asked to present their screen. A video thumbnail is displayed over the shared screen. The participant is presented with a unique link and added into the study session to complete a task. The task may include any of a navigation activity, a questionnaire, a click test, and a card sorting activity. A set of note takers and/or observers are present in the study, but their presence is not known to the participant. The only interaction the participant has is between the study prompts and questions, and with one or more moderators. The study moderator(s), observer(s) and not taker(s) are all able to communicate with one another over a private channel.