Abstract: The present disclosure provides a kit and method for constructing a long-acting depression animal model. The present disclosure adopts Bacille Calmette-Guerin (BCG) (or Mycobacterium bovis (M bovis)) and low-dose pertussis toxin (PTX) for combined induction, which greatly reduces modeling cost. A mouse model constructed by the method of the present disclosure can clearly distinguish a time interval of pathological behaviors from a time interval of depression-like behaviors and has prominent face validity and predictive validity. The method of the present disclosure is simple and convenient and does not require a large number of tedious daily operations as the depression model construction by chronic unpredictable stress, chronic restraint stress, social defeat, mother-child separation, or the like. The mouse model of the present disclosure can provide a valuable long-term intervention window for research on antidepressant therapy.

Abstract: A game controller includes a touchpad that a user, viewing a virtual keyboard on a screen, can soft-touch to move a cursor on the screen and then hard-touch to move the cursor and also send location data to a processor for inputting a letter from the virtual keyboard. Machine learning is used to predict a next letter or next word.

Abstract: A text-based real-time communication interface, such as a chatbot, is presented to a user for the exchange of customer support information. A user's freeform text input is analyzed using machine learning algorithms to derive the meaning of the input text as well as to determine the user sentiment expressed therein. These determinations may be further supported by signals extracted from session-based activity, which signals can be used to infer the intended workflow of the user and whether or not that workflow was achieved. The expressed user sentiment is considered along with other historical or session-based user data to generate tailored questions and responses to be delivered in real-time to the user. The responses are displayed to the user along with information that routes the user to a workflow resolution.

Abstract: A preliminary software feature is applied in a testing rollout to a discrete subset of customers. Survey data may be collected from those customers through a variety of sources, such as chatbot text, session workflow, historical user data, social media data, email survey data, user profile data, messaging threads, and the like. This survey data is analyzed using machine learning algorithms to derive the meaning of input text as well as to determine the user sentiment expressed therein. The outputs of this analysis are normalized across sources and aggregated at a feature-level to generate overall metrics of customer satisfaction with the feature. A holistic analysis is performed on this customer sentiment data to obtain an aggregate or combined user satisfaction score. This score is applied against a set of guardrails to determine whether to ship the feature to a broader customer base.

Abstract: A text-based real-time communication interface, such as a chatbot, is presented to a user for the exchange of customer support information. A user's freeform text input is analyzed using machine learning algorithms to derive the meaning of the input text as well as to determine the user sentiment expressed therein. These determinations may be further supported by signals extracted from session-based activity, which signals can be used to infer the intended workflow of the user and whether or not that workflow was achieved. The expressed user sentiment is considered along with other historical or session-based user data to generate tailored questions and responses to be delivered in real-time to the user. The responses are displayed to the user along with information that routes the user to a workflow resolution.

Abstract: An end-to-end cloud-based machine learning platform providing personalized game player experiences. Data lineage is generated for all transformed data for generating feature ETLs, and for training machine learning models. That data is used to understand the performance of off-line and online recommender systems for the personalization of the game player experiences. To that end, the platform pipeline provides the life cycle of the transformed data to a self-healing system that compare it to the life cycle of the user interactions. By comparing the two life cycles, the self-healing system can automatically provide a diagnostic, and it can also automatically provide an action if the performance of the model predictions has changed over time.

Abstract: A game controller includes a touchpad that a user, viewing a virtual keyboard on a screen, can soft-touch to move a cursor on the screen and then hard-touch to move the cursor and also send location data to a processor for inputting a letter from the virtual keyboard. A microphone on the touchpad can be used to receive voice signals for training a machine learning module to predict a next letter or next word, or to insert special characters/punctuations/graphics such as “smileys” during the swipe, or to indicate a tone of a Chinese character while typing with Chinese Pinyin.

Abstract: A game controller includes a touchpad that a user, viewing a virtual keyboard on a screen, can soft-touch to move a cursor on the screen and then hard-touch to move the cursor and also send location data to a processor for inputting a letter from the virtual keyboard. A microphone on the touchpad can be used to receive voice signals for training a machine learning module to predict a next letter or next word, or to insert special characters/punctuations/graphics such as “smileys” during the swipe, or to indicate a tone of a Chinese character while typing with Chinese Pinyin.

Abstract: A game controller includes a touchpad that a user, viewing a virtual keyboard on a screen, can soft-touch to move a cursor on the screen and then hard-touch to move the cursor and also send location data to a processor for inputting a letter from the virtual keyboard. A microphone on the touchpad can be used to receive voice signals for training a machine learning module to predict a next letter or next word, or to insert special characters/punctuations/graphics such as “smileys” during the swipe, or to indicate a tone of a Chinese character while typing with Chinese Pinyin.

Abstract: An end-to-end cloud-based machine learning platform providing personalized game player experiences. Data lineage is generated for all transformed data for generating feature ETLs, and for training machine learning models. That data is used to understand the performance of off-line and online recommender systems for the personalization of the game player experiences. To that end, the platform pipeline provides the life cycle of the transformed data to a self-healing system that compare it to the life cycle of the user interactions. By comparing the two life cycles, the self-healing system can automatically provide a diagnostic, and it can also automatically provide an action if the performance of the model predictions has changed over time.

Abstract: A game controller includes a touchpad that a user, viewing a virtual keyboard on a screen, can soft-touch to move a cursor on the screen and then hard-touch to move the cursor and also send location data to a processor for inputting a letter from the virtual keyboard. Machine learning is used to predict a next letter or next word.