Method and system for generating and outputting task prompts - Patents.com

Abstract

To provide a method for generating and outputting a prompt for performing a task during a designated time segment.SOLUTION: A method includes: acquiring context data associated with a user related to time segments from a plurality of sensors; categorizing each of the time segments into one of a plurality of thought states on the basis of the context data; mapping a task taken out from a task data set associated with the user into one of the plurality of thought states; and generating a prompt for performing the task during a designated time segment of the time segments. The designated time segment corresponds to the one of the plurality of thought states to which the task is mapped.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] TECHNICAL FIELD This disclosure relates to a task prompt generation system, and more particularly to a task prompt generation system that generates prompts to encourage performance of a task during time segments that correspond to particular mind states associated with the task. [Background technology]

[0002] Using conventional systems, a user can interact with multiple tasks of varying difficulty and include those tasks in a digital calendar, which tasks can then be scheduled by the user using voice recognition based techniques, manual input, etc. However, conventional systems fail to facilitate efficient execution of tasks of varying difficulty based on the mind states associated with those users.

[0003] Therefore, there is a need to enable users to complete routine and complex tasks efficiently and effectively by taking into account their context, physiological state, and state of mind during the above mentioned periods. Summary of the Invention

[0004] In one embodiment, a method is provided for generating and / or outputting prompts for performance of a task during designated time segments, the method including obtaining context data associated with a user related to the time segments from a plurality of sensors, classifying each of the time segments into one of a plurality of mind states based on the context data, mapping a task from a task dataset associated with the user to one of the plurality of mind states, and generating a prompt for performance of the task during a designated one of the time segments, the designated time segment corresponding to one of the plurality of mind states to which the task is mapped.

[0005] In another embodiment, a system configured to generate and output prompts for performance of a task during designated ones of the time segments is provided, the system including a device including a plurality of sensors and a processor configured to obtain context data associated with a user associated with the time segments from the plurality of sensors, classify each of the time segments into one of a plurality of mind states based on the context data, map a task from a task dataset associated with the user to one of the plurality of mind states, and generate prompts for performance of a task during designated ones of the time segments.

[0006] The above and additional features provided by the embodiments described herein will be more fully understood from the following detailed description, taken in conjunction with the drawings. [Brief description of the drawings]

[0007] The embodiments illustrated in the drawings are exemplary and preferred in nature, and are not intended to limit the subject matter defined by the claims. The following detailed description of exemplary embodiments can be understood when read in conjunction with the following drawings, in which like structure is indicated with like reference numerals and in which:

[0008] [Figure 1] 1 is a schematic diagram of an example operating environment for the disclosed task prompt generation system, according to one or more embodiments described and illustrated herein. [Diagram 2] FIG. 1 is a schematic diagram of non-limiting components of a device of the present disclosure, according to one or more embodiments described and illustrated herein. [Diagram 3] 1 is a flow chart for generating and outputting prompts to perform tasks during designated time segments according to one or more embodiments described and illustrated herein. [Figure 4]FIG. 1 illustrates a flowchart for generating an artificial intelligence trained model based on training for use by the task prompt generation system of the present disclosure to generate prompts, according to one or more embodiments described and illustrated herein. [Diagram 5] FIG. 1 is a schematic diagram of an example operation of the disclosed task prompt generation system, in which prompts for the performance of routine and complex tasks are output on a mobile device display, according to one or more embodiments described and illustrated herein. [Figure 6] FIG. 13 is a schematic diagram of another example of the operation of a task prompt generation system in which prompts for the performance of a complex task are automatically output on a display of a mobile device according to one or more embodiments described and illustrated herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Embodiments of the present disclosure describe methods and systems for generating task prompts and outputting them on various devices or displays of audible prompts. These task prompts are generated and displayed to various users during specific time segments to maximize the likelihood of completing these tasks in an efficient and consistent manner. To this end, in embodiments, the task prompt generation system of the present disclosure can use artificial intelligence neural network trained models that are trained with contextual and physiological data associated with users during these time segments, e.g., one- or two-hour blocks of time on a typical weekday over a period of weeks, months, etc.

[0010] Based on this training, the task prompt generation system can identify different time segments suitable for performing complex tasks, routine tasks, etc. In particular, the task prompt generation system classifies tasks into long-term and short-term reactive thinking states, classifies time segments relative to the long-term and short-term reactive thinking states, and generates prompts to encourage performance of the task during designated time segments that correspond to the thinking states to which the task is mapped.

[0011] Referring to the drawings, Figure 1 is a schematic diagram of an example operating environment of the task prompt generation system of the present disclosure, according to one or more embodiments described and illustrated herein. As shown, Figure 1 illustrates a user 102 operating a mobile device 103 during time segments 116, 118, 120, and 122. These time segments may correspond to various time blocks during a day, week, month, etc. These time segments may correspond to one or two hour time blocks during a day, every other day, once or twice a week, etc. Other such time blocks are also contemplated. Although the time segments in Figure 1 are shown as contiguous, the time segments may be discontinuously distributed. For example, time segment 116 may be the time segment from 10:00 a.m. to 10:30 a.m. on Monday, and time segment 118 may be the time segment from 1:00 p.m. to 1:30 p.m. on Monday.

[0012] A processor (e.g., processor 202) of the mobile device 103, working in conjunction with one or more sensors installed as part of the mobile device 103 or embedded in an additional device (e.g., FitBit®, iWatch®, etc.) worn by the user 102, can collect various types of contextual data (e.g., as illustrated by contextual data points 104, 106, 108, and 110) based on the user 102's interactions with the mobile device 103. In particular, the contextual data points 104, 106, 108, and 110 may relate to contextual data associated with the user 102 obtained during time segments 116, 118, 120, and 122. Specifically, over a period of time, such as a day, week, month, etc., the mobile device 103 may collect physiological data, data related to the number of emails a user may send at certain times of the day, the user's reaction times related to scheduling tasks, the types of events a user may schedule and attend during these periods, how often a user may reschedule, cancel, or modify scheduled events during these periods, etc.

[0013] The contextual data can also be collected from an electronic calendar associated with the user 102. The physiological data may include data such as pulse rate, heart rate, body temperature, the number of steps taken by the user 102, the distance the user 102 may have walked, and the like. The physiological data can be indicative of various states associated with the user during various time segments, such as the user's relaxed state, the user's excited state, and the like. This data can be collected, collated, and stored locally in the memory (e.g., memory module 206) of the mobile device 103, as well as stored in the memory of the server 114. It should also be noted that such data can be transmitted from the mobile device 103 to the server 114 in real time over the communication network 112. Furthermore, the server 114 can communicate such data to the mobile device 103 in real time over the communication network 112.

[0014] Additionally, one or more artificial intelligence based software applications may operate on and be accessed via the mobile device 103. In an embodiment, physiological data associated with the user 102 and data associated with the user's interactions with the mobile device 103, as well as one or more external devices accessible via the mobile device 103, are included as part of a real-time updated dataset (e.g., a training dataset). The updated training dataset may also include real-time feedback from the user 102 regarding tasks performed during various time segments.

[0015] All this data and artificial intelligence neural network based algorithms are used by the mobile device 103 to generate and train an artificial intelligence neural network model. In an embodiment, the artificial intelligence neural network trained model can be used to generate and output prompts on the display (e.g., display 216) of the mobile device 103, which recommend the user to perform a task. The prompts can be generated based on the difficulty of the task. In other words, if the tasks in the generated prompts are complex tasks that require creative thinking, significant organization and analysis of information, etc. (e.g., writing an article, improving a product in multiple aspects, coming up with an idea for a new product line, etc.), these tasks may be associated with a long-term based thinking state. This thinking state, in other words, is a thinking state that requires the user 102 to spend significant mental energy and time thinking about solving a complex problem. For this reason, the tasks can be displayed as prompts on the mobile device 103 of the user 102 during a period suitable for performing such tasks.

[0016] In embodiments, the artificial intelligence neural network trained model may generate and output prompts associated with such complex tasks during particular time segments when the user 102 typically performed such tasks, as suggested by data analysis. Additionally, analysis of physiological data associated with the user 102 may suggest that particular time segments may also be favorable for effective and efficient completion of complex tasks. For example, analysis of the physiological data, in conjunction with other contextual data, may indicate that the user 102's body temperature, heart rate, pulse rate, and other vital signs are at equilibrium levels between 6:00 a.m. and 8:00 a.m., which may indicate that the user 102 may be able to focus and solve complex problems during this time.

[0017] Alternatively, the heart rate and pulse rate may be relatively high during another time segment, for example, between 10:00 and 11:00 a.m., which may indicate that the user 102 is energetic, excited, highly active, and somewhat distracted. Therefore, this time segment may be suitable for performing some daily tasks that do not require much concentration, such as scheduling a meeting, answering a phone call, etc. These tasks may be associated with a short-term reactive mindset. It is noted that a prompt may be generated and output on the display (e.g., display 216) of the mobile device 103 that includes a group of similar tasks that can be performed during a particular time segment. Multiple other types of tasks may be generated and output on the display of the mobile device 103.

[0018] Also, while the interaction between the user 102 and the mobile device 103 is described, it should be noted that the prompt generation system described in this disclosure may also be implemented within one or more vehicle systems. In particular, a processor (e.g., processor 222) of the vehicle (not shown) may also be configured to detect contextual data, physiological data, etc. associated with the user 102. Additionally, the vehicle may be configured to communicate with one or more devices external to the device, as with a mobile device, and store the contextual and physiological data locally in the vehicle's memory (e.g., one or more memory modules 226) or transmit this data over the communications network 112 to the server 114.

[0019] FIG. 2 is a schematic diagram of non-limiting components of a device of the present disclosure, according to one or more embodiments described and illustrated herein.

[0020] 2 is a schematic diagram of non-limiting components of a mobile device system 200 and a vehicle system 220 according to one or more embodiments described herein. Notably, while the mobile device system 200 is shown isolated in FIG. 2, the mobile device system 200 may be included within a vehicle. The vehicle in which the vehicle system 220 may be installed may be an automobile or any other passenger or non-passenger vehicle, such as, for example, a land, water, and / or air vehicle. In some embodiments, these vehicles may be autonomous vehicles that navigate a vehicular environment with limited or no human input.

[0021] The mobile device system 200 and the vehicle system 220 may include a processor 202, 222. The processor 202, 222 may be any device capable of executing machine-readable / executable instructions. Thus, the processor 202, 222 may be a controller, an integrated circuit, a microchip, a computer, or any other computing device.

[0022] The processors 202, 222 may be coupled to communication paths 204, 224, respectively, that provide signal interconnection between various modules of the vehicle system 220 and the mobile device system 200. Thus, the communication paths 204, 224 may interconnect any number of processors (e.g., comparable to the processors 202, 222) and allow the modules coupled to the communication paths 204, 224 to operate in a distributed computing environment. Specifically, each of the modules is operable as a node that can transmit and receive data. As used herein, the term "communicatively coupled" means that the coupled components can exchange data signals with each other, e.g., electrical signals through a conductive medium, electromagnetic signals through the air, and optical signals through optical waveguides.

[0023] Thus, the communication paths 204, 224 may be formed of any medium capable of transmitting a signal, such as, for example, conductive wires, conductive traces, optical waveguides, or the like. In some embodiments, the communication paths 204, 224 may facilitate the transmission of wireless signals, such as WiFi, Bluetooth, Near Field Communication (NFC), and the like. Furthermore, the communication paths 204, 224 may be formed of a combination of media capable of transmitting a signal. In one embodiment, the communication paths 204, 224 comprise a combination of conductive traces, conductive wires, connectors, and buses that cooperate to enable the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. Thus, the communication paths 204, 224 may comprise, for example, vehicle buses, such as LIN buses, CAN buses, VAN buses, and the like. Further, it should be noted that the term "signal" refers to a waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic), such as DC, AC, sine wave, triangular wave, square wave, vibration, and the like, that can be passed through a medium.

[0024] The mobile device system 200 and the vehicle system 220 each include one or more memory modules 206, 226 coupled to the communication paths 204, 224. The one or more memory modules 206, 226 may comprise RAM, ROM, flash memory, hard drives, or any device capable of storing machine readable / executable instructions such that the machine readable / executable instructions are accessible to the processors 202, 222. The machine readable / executable instructions may comprise a machine language directly executable by the processors 202, 222, or logic or algorithms written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as assembly language, object oriented programming (OOP), scripting languages, microcode, etc., that can be compiled or assembled into machine readable / executable instructions and stored in the one or more memory modules 206, 226. Alternatively, the machine readable / executable instructions may be written in a hardware description language (HDL), such as logic implemented by a field programmable gate array (FPGA) configuration or an application specific integrated circuit (ASIC), or an equivalent of an FPGA configuration or ASIC. Thus, the methods described herein may be implemented in any conventional computer programming language, as preprogrammed hardware elements, or as a combination of hardware and software components. In some embodiments, one or more memory modules 206, 226 may store data relating to status and operational state information associated with one or more vehicle components, such as brakes, airbags, cruise control, electric power steering, battery status, etc.

[0025] The mobile device system 200 and the vehicle system 220 may include one or more sensors 208, 228. Each of the one or more sensors 208, 228 is coupled to a communication path 204, 224 and communicatively coupled to the processor 202, 222. The one or more sensors 208 may include one or more motion sensors for detecting and measuring vehicle movement and changes in movement. The motion sensors may include an inertial measurement unit. Each of the one or more motion sensors may include one or more accelerometers and one or more gyroscopes. Each of the one or more motion sensors converts sensed physical movement of the vehicle into a signal indicative of the vehicle's orientation, rotation, speed, or acceleration. The one or more sensors may also include a microphone, a motion sensor, a proximity sensor, etc. The one or more sensors 208, 228 may also be capable of detecting heart rate, pulse rate, etc. The one or more sensors 208, 228 may also include a temperature sensor.

[0026] 2, the mobile device system 200 and the vehicle system 220 optionally include a satellite antenna 210, 230 coupled to the communication paths 204, 224 such that the communication paths 204, 224 communicatively couple the satellite antenna 210, 230 to other modules of the mobile device system 200. The satellite antenna 210, 230 is configured to receive signals from Global Positioning System satellites. Specifically, in one embodiment, the satellite antenna 210, 230 includes one or more conductive elements that interact with electromagnetic signals transmitted from the Global Positioning System satellites. The received signals are converted by the processors 202, 222 into data signals indicative of the position of the satellite antenna 210, 230 or the position (e.g., latitude and longitude) of an object proximate to the satellite antenna 210, 230. The location information may be included in the context data points described above.

[0027] The mobile device system 200 and the vehicle system 220 may include network interface hardware 212, 234 for communicatively coupling the mobile device system 200 and the vehicle system 220 to the server 114, for example, via the communications network 112. The network interface hardware 212, 234 is coupled to the communications paths 204, 224 such that the communications path 204 communicatively couples the network interface hardware 212, 234 to other modules of the mobile device system 200 and the vehicle system 220. The network interface hardware 212, 234 may be any device capable of transmitting and receiving data over a wireless network, for example, the communications network 112. Thus, the network interface hardware 212, 234 may include a communications transceiver for transmitting and receiving data according to any wireless communication standard. For example, the network interface hardware 212, 234 may include a chipset (antenna, processor, machine-readable instructions, etc.) for communicating over a wireless computer network, such as Wireless Fidelity (Wi-Fi), WiMax, Bluetooth, IrDA, Wireless USB, Z-Wave, ZigBee, or the like. In some embodiments, the network interface hardware 212, 234 includes a Bluetooth transceiver that enables the mobile device system 200 and the vehicle system 220 to exchange information with the server 114 via Bluetooth.

[0028] The network interface hardware 212, 234 can establish connections between multiple mobile devices and / or vehicles using a variety of communication protocols. For example, in embodiments, the network interface hardware 212, 234 may use a communication protocol that enables communication between vehicles and various other devices, such as vehicle-to-everything (V2X). Additionally, in other embodiments, the network interface hardware 212, 234 can use a communication protocol that is specific to short-range communications (DSRC). Compatibility with other equivalent communication protocols is also contemplated.

[0029] It should be noted that communication protocols include multiple layers defined by the Open Systems Interconnection Model (OSI model), which defines telecommunication protocols as protocols having, for example, an application layer, a presentation layer, a session layer, a transport layer, a network layer, a data link layer, and a physical layer. To function properly, each communication protocol includes a top layer protocol and one or more bottom layer protocols. Examples of top layer protocols (e.g., application layer protocols) include HTTP, HTTP2 (SPDY), HTTP3 (QUIC), which are suitable for transmitting and exchanging data in a general format. Application layer protocols such as RTP and RTCP are suitable for various real-time communications such as telephony and messaging in some cases. Furthermore, SSH and SFTP may be suitable for secure maintenance, MQTT and AMQP may be suitable for status notifications and wake-up triggers, and MPEG-DASH / HLS may be suitable for live video streaming with user end systems. Examples of transport layer protocols that may be selected by the various application layer protocols listed above include, for example, TCP, QUIC / SPDY, SCTP, DCCP, UDP, and RUDP.

[0030] The mobile device system 200 and the vehicle system 220 include cameras 214, 232. The cameras 214, 232 can have any resolution. In some embodiments, one or more optical components, such as a mirror, a fisheye lens, or any other type of lens, can be optically coupled to the cameras 214, 232. In embodiments, the cameras can have wide-angle features that allow them to capture digital content within an arc range of 150 degrees to 180 degrees. Alternatively, the cameras 214, 232 can have narrow-angle features that allow them to capture digital content within a narrow arc range, for example, an arc range of 60 degrees to 90 degrees. In embodiments, the one or more cameras can be capable of capturing high definition images, such as at a resolution of 720 pixels, a resolution of 1080 pixels, and the like. The cameras 214, 232 can capture images of the user's face or body, and the captured images can be processed to generate data indicative of the user's status.

[0031] In embodiments, the mobile device system 200 and the vehicle system 220 may include displays 216, 236 to provide a visual output. The displays 216, 236 may output digital data, images, and / or live video streams of various types of data. The displays 216, 236 are coupled to the communication paths 204, 224. The communication paths 204, 224 thus communicatively couple the displays 216, 236 to other modules of the mobile device system 200 and the vehicle system 220, including, but not limited to, one or more of the processors 202, 222 and / or memory modules 206, 226.

[0032] 2, server 114 may be a cloud server that includes one or more processors, memory modules, network interface hardware, and communication paths communicatively coupling each of these components. It should be noted that server 114 may be a single server or a combination of communicatively interconnected servers.

[0033] 3 illustrates a flowchart 300 for generating and outputting prompts for the performance of tasks during designated time segments, according to one or more embodiments described and illustrated herein. In an embodiment, interactions that a user 102 may have with a mobile device 103 may be tracked by one or more sensors installed as part of the mobile device 103. These interactions may also be tracked, monitored, and stored in memory by one or more devices external to the mobile device 103, such as the server 114, one or more third party servers, etc. The one or more sensors 208 of the mobile device 103 may monitor various physiological characteristics of the user 102, such as body temperature, pulse rate, heart rate, the number of steps the user has taken, the distance the user 102 may have walked, etc. Additionally, the mobile device 103 may monitor interactions that the user 102 may have with various digital applications on his or her mobile device 103, such as scheduling appointments for various tasks, modifying existing appointments, canceling appointments, etc. The mobile device 103 can also be configured to analyze and monitor the time that a user performs a task.

[0034] For example, the mobile device 103 may determine that the user 102 consistently communicates text messages, participates in video conferences, etc., during a certain period of time, e.g., most Wednesdays, Fridays, and Saturdays between 6:00 PM and 8:00 PM. The mobile device 103 may determine that the user 102 schedules appointments during a particular time frame in the morning, e.g., between 7:30 AM and 8:00 AM. Multiple other such interactions may be tracked, analyzed, and collated by the mobile device 103 automatically and without user intervention.

[0035] At block 310, the processor 202 of the mobile device 103 obtains contextual data associated with the user from multiple sensors. The contextual data is also associated with various time segments. In an embodiment, the contextual data relates to one or more physiological characteristics of the user (e.g., various vital signs detected and tracked in real time), data related to tasks and events scheduled by the user 102 (e.g., using the mobile device 103), patterns associated with these events, durations during which the user 102 performs certain types of tasks, etc. The contextual data may also include tracking and monitoring the durations during which various tasks are performed and matching such durations with physiological data such as heart rate, pulse rate, body temperature, etc. In an embodiment, other physiological data such as blood pressure, blood glucose level, etc. can be accessed from the mobile device 103, for example, by communicating with the server 114 via the communication network 112. The types of contextual data described in this disclosure are non-limiting.

[0036] At block 320, the processor 202 of the mobile device 103 may classify each of the time segments into one of a plurality of thought states based on the context data. In an embodiment, based on the acquired context data, the processor 202 of the mobile device 103 may classify each time segment, e.g., associated with a day, hours, etc., into one or more of a plurality of thought states. In an embodiment, a time segment may be, for example, a two-hour period from 6:00 AM to 8:00 PM on a typical weekday. In an embodiment, each two-hour period from 6:00 AM to 8:00 PM may be classified into a long-term based thought state or a short-term instinctual reaction based thought state. For example, the time block from 6:00 AM to 8:00 AM may be classified into a long-term based thought state based on the context data associated with the user. Additionally, the classification of each time period may be based on context data associated with a plurality of other users with different physiologies, demographics, habits, etc.

[0037] In an embodiment, a long-term based thinking state may be a state in which important and essential thinking about solving complex problems may occur. Moreover, in such thinking states, thoughts or activities that require a lot of effort, time, and energy may be performed, such as, for example, analysis related to buying stocks, ideas for creating new products and / or services, analysis of investment properties to be purchased, writing novels, short stories, etc. In contrast, a short-term, instinctive reaction based thinking state may be associated with a state in which quick decisions are made, such as, for example, what to eat for lunch, scheduling a dentist appointment, planning a game night with the family, buying gifts for the family, etc. In an embodiment, the classification of the time segments may be performed automatically and without user intervention. In an embodiment, the classification may also be performed manually by the user 102.

[0038] At block 330, the processor 202 of the mobile device 103 may map the task retrieved from the task dataset associated with the user to one of a plurality of thinking states, e.g., a long-term based thinking state or a short-term instinctual response based thinking state. It should be noted that other plurality of thinking states are also contemplated. In some embodiments, the plurality of thinking states may be three or more thinking states based on a plurality of characteristics of the thinking state. For example, the plurality of thinking states may include a long-term logical thinking state, a long-term creative thinking state, a short-term logical thinking state, and a short-term creative thinking state. In embodiments, the task dataset may include a plurality of tasks of different types with different levels of difficulty, e.g., tasks related to scheduling various tasks (e.g., buying groceries, scheduling a doctor's appointment, deciding what to eat for lunch, where to go and buy a suit or dress), to tasks related to analyzing a 401K plan, buying stocks, determining an appropriate investment strategy, analyzing a real estate deal, writing a short story, etc. In embodiments, the user 102 may map the task retrieved from the dataset to a particular thinking state. For example, a user 102 may interact with one or more software applications running on a mobile device 103, enter a particular task into the software application's interface (e.g., a list, a table, etc.), and categorize the particular task into one of a number of thought states.

[0039] In other embodiments, the processor 202 may use an artificial intelligence trained model (described in FIG. 4) to map a particular task entered into the user interface by the user 102 to either a long-term based thought state, a short-term instinctual response based thought state, or a variety of additional thought states. As previously discussed, these tasks may include scheduling appointments for various everyday tasks, or tackling the solution of more complex problems.

[0040] At block 340, the processor 202 of the mobile device 103 may generate a prompt to prompt the user to perform a task during a specified one of the time segments. The specified time segment may correspond to one of a number of mind states to which the task is mapped. In an embodiment, as described above, the prompt may be output on the display of the mobile device 103 in association with a particular time segment based on an analysis of the context data and the various mind states to which one or more of the various tasks may be mapped. For example, the user 102 may input a task into an interface of a software application, and the software application may automatically, and without user intervention, suggest that the user perform the task during the specified time segment. The specified time segment may have been determined to be suitable depending on the complexity of the task. For example, a time segment from 6:00 a.m. to 8:00 a.m. may be suggested for a task that requires a great deal of creativity and concentration, such as writing a report, a short story, or part of a novel. A prompt may be automatically generated requesting the user to perform the task between 6:00 a.m. and 8:00 a.m. (e.g., at 6:30 a.m.) on a particular day.

[0041] The artificial intelligence trained model can be dynamically generated in real time by training using contextual data associated with the user 102 collected whenever the user interacts with the mobile device 103 and based on real-time detection and analysis of the various physiological characteristics described above. For example, the user responds to prompts each time they enter a task (e.g., confirming and accepting a proposal to perform the task during a specified time segment, rejecting a proposal to perform the task, rescheduling the task from a specific time to another time), and data associated with these decisions is incorporated into a dynamically updated training data set used to generate the artificial intelligence trained model based on the training. Furthermore, data such as heart rate, pulse rate, body temperature, etc. are associated with the time prompts are provided to the user 102, and the manner in which the user 102 responds to these prompts can be monitored, tracked, and incorporated into the training data set used to generate the artificial intelligence trained model based on the training.

[0042] FIG. 4 illustrates a flowchart 400 for generating an artificial intelligence trained model based on training for use by the task prompt generation system of the present disclosure to generate prompts, according to one or more embodiments described and illustrated herein. In an embodiment, at block 402, contextual data, physiological data, etc. may be obtained and included as part of a training data set 403 based on various actions of the user 102, interactions with one or more devices, and physical condition of the user 102. Note that the training data set 403 may also include a number of other user actions, interactions, and physical conditions. At block 404, one or more data input labels 406 may be included in the training data set 403 in association with the contextual data and physiological data. At block 410, an artificial intelligence neural network algorithm 412 may be used to train an artificial intelligence based model as described herein. At block 414, an artificial intelligence neural network trained model 416 may be generated based on training using natural language based techniques, heuristic based techniques, one or more artificial neural networks (ANNs), Markov decision processes, etc. At blocks 418 and 420, prompts 1 and 2 can be generated. These prompts may be associated with tasks to be performed in designated time segments associated with short-term, instinctual response-based mindsets or long-term based mindsets, as described in this disclosure.

[0043] In an embodiment, a convolutional neural network (CNN) can be used. For example, a convolutional neural network (CNN) can be used as an ANN belonging to a class of deep feed-forward ANNs applicable, for example, in the field of machine learning, to audiovisual analysis. A CNN can be shift or space invariant, and uses a shared weight architecture and translation invariance properties. Additionally or alternatively, a recurrent neural network (RNN) can be used as an ANN, which is a feedback neural network. An RNN can process a variable length sequence of inputs using an internal memory state to generate one or more outputs. In an RNN, the connections between nodes can form a DAG along time. One or more different types of RNNs can be used, such as a standard RNN, a long short-term memory (LSTM) RNN architecture, and / or a gated recurrent unit RNN architecture. Other techniques are also contemplated.

[0044] FIG. 5 is a schematic diagram of an example operation of the task prompt generation system of the present disclosure, where prompts for performing routine and complex tasks are output on a mobile device display, according to one or more embodiments described and illustrated herein. For example, on a typical weekday, a user 102 may interact with a mobile device 103 multiple times, for example, to answer calls, schedule and reschedule meetings, check emails, etc. As previously described, data associated with all this activity may be monitored, tracked, and used to dynamically generate an artificial intelligence trained model based on the training. In an embodiment, on a Monday weekday, a user 102 may sense a vibration from the mobile device 103, check the display of the user's phone, and receive a prompt 510 requesting the user to make a selection regarding what they would like to eat for lunch. For example, the prompt 510 may output various food items that the user may have previously ordered (e.g., using the Uber Eats® application, Grubhub®, etc.). The user 102 may select one of these items. Thus, the additional effort required to think about the decision to select an item to eat at lunchtime can be alleviated.

[0045] In an embodiment, the artificial intelligence trained model can automatically, and without user intervention, generate a prompt to select a food item for lunch during a time segment 502 that may be determined to be suitable for making routine decisions, such as selecting a food item for lunch, scheduling a doctor's appointment, voting for a candidate, selecting a gift for a family member, etc. The processor 202 can determine, based on analyzing, monitoring, and tracking contextual data associated with the user 102, that the time segment 502 is suitable for performing a task or decision that requires only short-term, gut reaction thought processes corresponding to a short-term, gut reaction-based thought state.

[0046] In an embodiment, the processor 202 may use artificial intelligence to track, analyze, and monitor the contextual data and determine that the user 102 tends to schedule and perform various daily tasks between 10:30 a.m. and 11:00 a.m. (time segment 502). Further, during the time segment 502, one or more sensors 208 of the mobile device 103 may detect a heart rate, pulse rate, body temperature (and other such physiological characteristics) and determine that the heart rate and pulse rate are slightly higher than usual and that the user 102 is regularly interacting with the mobile device 103. It should be noted that data related to heart rate, pulse rate, body temperature, etc. may be tracked by one or more sensors installed as part of the mobile device 103 and may be received by the mobile device 103 from one or more external devices, such as the server 114, or other devices worn by the user 102, such as a FitBit®, iWatch®, etc.

[0047] In an embodiment, on the same day (i.e., Monday), the user 102 may sense another vibration from the mobile device 103, check the user's phone display, and receive a prompt 512 requesting that the user review his / her 401K statement. The prompt 512 may be generated at a time segment 506, for example, at 1:00 p.m., which is typically a time immediately after lunch. In response, the user 102 may acknowledge receipt of the prompt 512 and select "No" (e.g., a negative response). Such a response may be included as part of the training data set 403, which is updated in real time. Additionally, an artificial intelligence trained model may analyze the training data set 403 and may be generated based on training in accordance with the training data set 403. Additionally, physiological data associated with the time segment 506, such as heart rate, pulse rate, etc., may also be tracked. The heart rate, pulse rate, etc. may be low, indicating that the user 102 has just had lunch and is not in a highly active state.

[0048] The processor 202 can use the artificial intelligence trained model to determine that the time segment 506 may not be a suitable time for the user 102 to perform a task that requires significant thought, concentration, and effort, for example, as characteristic of a task performed in a prolonged state of mind. Data associated with the selection of “No” by the user 102 may be obtained, collated, and included as part of the training data set 403 on which the artificial intelligence trained model is dynamically generated based on training. Additionally, as previously described, data associated with various physiological characteristics of the user 102 may also be included in the training data set 403 and associated with various time segments, for example, the time segments 502, 504, 506, and 508. The processor 202 can use the artificial intelligence trained model to determine that the time segment 508 may be a more suitable time segment in which the user may perform various complex tasks.

[0049] FIG. 6 is a schematic diagram of another example operation of a task prompt generation system in which prompts for the performance of a complex task are automatically output on a display of a mobile device according to one or more embodiments described and illustrated herein.

[0050] In an embodiment, the processor 202 may generate additional prompts during the time segments 606 on different weekdays using an artificial intelligence trained model dynamically trained (i.e., trained in real time) on the contextual data, physiological data, etc. Specifically, based on analyzing input received from the user 102 regarding the performance of a complex task in the time segment 506 and using the artificial intelligence trained model, the processor 202 may generate a prompt 610 that recommends that the user 102 perform a review of a real estate transaction at a more suitable time, e.g., a time different from the time segment 506 (e.g., the task may be associated with a long-term state of mind). For example, the processor 202 may generate a prompt at the time segment 606 that refers to a time block from 10:30 a.m. to 11:30 a.m. In an embodiment, the user 102 may provide an acknowledgment response (e.g., select "yes") as shown in FIG. 6. It should be noted that other time segments (e.g., time segments 602, 604, and 608) may also be determined to be suitable for performing a task that can be classified as being associated with a long-term state of mind. It should be noted that reviewing a real estate transaction can be a complex task that requires reviewing various financial statements, income statements, tax records, etc. In other embodiments, the user 102 can input specific tasks (e.g., additional tasks) and receive real-time prompts to perform the inputted tasks at different designated time segments.

[0051] The method includes obtaining context data associated with a user relating to time segments from a plurality of sensors, classifying each of the time segments into one of a plurality of mind states based on the context data, mapping a task retrieved from a task dataset associated with the user to one of the plurality of mind states, and generating a prompt to perform the task during a specified one of the time segments, the specified time segment corresponding to one of the plurality of mind states to which the task is mapped.

[0052] The terms used herein are intended to describe particular aspects only and are not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural, including "at least one," unless the content clearly dictates otherwise. "Or" means "and / or." The term "and / or," as used herein, includes one or any and all combinations of the associated listed items. It is further understood that, as used herein, the terms "comprises" and / or "comprising," or "includes" and / or "including," specify the presence of stated features, regions, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and / or groups thereof. The term "or combinations thereof" means combinations including at least one of the aforementioned elements.

[0053] It should be noted that the terms "substantially" and "about" can be used herein to express the degree of inherent uncertainty attributed to any quantitative comparison, value, measurement, or other representation. These terms are also used herein to express the degree to which a quantitative representation may deviate from the stated standard without changing the basic functionality of the subject matter at issue.

[0054] While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications can be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter are described herein, such aspects need not be used in combination. Accordingly, the appended claims are intended to cover all such changes and modifications that are within the scope of the claimed subject matter. The invention disclosed in this specification includes the following aspects. <Aspect 1> 1. A method implemented by a processor of a user device, comprising: acquiring context data associated with the user related to a time segment from a plurality of sensors; classifying each of the time segments into one of a plurality of mind states based on the context data; mapping a task from a task dataset associated with the user to one of the plurality of mind states; and generating a prompt to perform the task during a designated one of the time segments, the designated time segment corresponding to the one of the plurality of mind states to which the task is mapped, the method being implemented by a processor of the user's device. <Aspect 2> 2. The method of claim 1, further comprising outputting the prompt to perform the task during the specified time segment on a display of the device. <Aspect 3> receiving an acknowledgment from the user to the prompt to perform the task during the specified time segment; incorporating, by the processor, the acknowledgement as part of a training data set; using the training data set including the confirmation responses, Dynamically training an artificial intelligence based model in real time; and 2. The method of claim 1, further comprising: generating, by the processor, an artificial intelligence trained model based on the training of the artificial intelligence based model. <Aspect 4> inputting, by the user, additional tasks into the task dataset; mapping the additional task to one of the plurality of thought states using the dynamically trained artificial intelligence trained model; and The method of embodiment 3, further comprising: generating additional prompts to perform the additional task during different designated ones of the time segments using the dynamically trained artificial intelligence trained model. <Aspect 5> 5. The method of embodiment 4, further comprising outputting, on a display of the device, the additional prompt to perform the additional task during the different designated time segments. <Aspect 6> receiving a negative response from the user to the prompt to perform the task during the specified time segment; incorporating, by the processor, the negative responses as part of a training data set; by the processor, using the training data set including the negative responses, Dynamically training an artificial intelligence based model in real time; and 2. The method of claim 1, further comprising: generating, by the processor, an artificial intelligence trained model based on the training of the artificial intelligence based model. <Aspect 7> inputting, by the user, additional tasks into the task dataset; mapping the additional task to one of the plurality of thought states using the dynamically trained artificial intelligence trained model; and The method of embodiment 6, further comprising: generating additional prompts to perform the additional task during different designated ones of the time segments using the dynamically trained artificial intelligence trained model. <Aspect 8> 8. The method of embodiment 7, further comprising outputting, on a display of the device, the additional prompt to perform the additional task during the different designated time segments. <Aspect 9> 2. The method of claim 1, wherein the plurality of thought states includes long-term based thought states and short-term instinctual response based thought states. <Aspect 10> 2. The method of claim 1, wherein the contextual data associated with the user relates to a relaxed state of the user, an excited state of the user, and a reaction time of the user. <Aspect 11> 2. The method of claim 1, wherein the contextual data associated with the user relates to a heart rate or pulse rate. <Aspect 12> 2. The method of embodiment 1, wherein the plurality of sensors includes a motion sensor, a camera, a physiological monitor sensor, and a microphone. <Aspect 13> 2. The method of aspect 1, further comprising obtaining the contextual data for the user associated with the time segment from an electronic calendar of the user. <Aspect 14> 2. The method of embodiment 1, wherein the plurality of sensors is incorporated into an additional device external to the user's device, the plurality of sensors being communicatively coupled to the device. <Aspect 15> 2. The method of claim 1, wherein the task dataset includes a plurality of tasks, such as scheduling a doctor's appointment, voting for a candidate, purchasing a gift, and purchasing stocks. <Aspect 16> A plurality of sensors; obtaining, from the plurality of sensors, context data associated with a user related to a time segment; classifying each of the time segments into one of a plurality of thought states based on the context data; Mapping a task from a task dataset associated with the user to one of the plurality of mind states; a device including a processor configured to generate a prompt to perform the task during a designated one of the time segments, the designated time segment configured to correspond to the one of the plurality of mind states to which the task is mapped. <Aspect 17> 17. The system of claim 16, wherein the processor is further configured to output the prompt to perform the task during the specified time segment on a display of the device. <Aspect 18> The processor, receiving an acknowledgment from the user to the prompt to perform the task during the specified time segment; incorporating, by the processor, the acknowledgement as part of a training data set; using the training data set including the confirmation responses, Dynamically train artificial intelligence-based models in real time, The system of embodiment 16, further configured to generate an artificial intelligence trained model based on the training of the artificial intelligence based model. <Aspect 19> The processor, inputting additional tasks into the task dataset by the user; using the dynamically trained artificial intelligence trained model to map the additional task to one of the plurality of thought states; The system of embodiment 18, further configured to generate additional prompts to perform the additional task during different designated ones of the time segments using the dynamically trained artificial intelligence trained model. <Aspect 20> 17. The system of embodiment 16, wherein the task dataset includes a plurality of tasks, such as scheduling a doctor's appointment, voting for a candidate, selecting a food item, purchasing a gift, and purchasing stocks.

Claims

1. 1. A method implemented by a processor of a user device, comprising: acquiring context data associated with the user related to a time segment from a plurality of sensors; classifying each of the time segments into one of a plurality of mind states based on the context data; mapping a task from a task dataset associated with the user to one of the plurality of mind states; and generating a prompt to perform the task during a designated one of the time segments, the designated time segment corresponding to the one of the plurality of mind states to which the task is mapped, the method being implemented by a processor of the user's device.

2. The method of claim 1 , further comprising outputting the prompt to perform the task during the designated time segment on a display of the device.

3. The method of claim 1 , wherein the plurality of thought states includes long-term based thought states and short-term instinctual response based thought states.

4. The method of claim 1 , wherein the contextual data associated with the user relates to a relaxed state of the user, an excited state of the user, and a reaction time of the user.

5. The method of claim 1 , wherein the contextual data associated with the user relates to a heart rate or pulse rate.

6. The method of claim 1 , wherein the plurality of sensors includes a motion sensor, a camera, a physiological monitor sensor, and a microphone.

7. The method of claim 1 , further comprising obtaining the contextual data for the user associated with the time segment from an electronic calendar for the user.

8. The method of claim 1 , wherein the plurality of sensors is incorporated into an additional device external to the user's device, the plurality of sensors being communicatively coupled to the device.

9. The method of claim 1 , wherein the task dataset includes a plurality of tasks such as scheduling a doctor's appointment, voting for a candidate, purchasing a gift, and purchasing stocks.

10. A plurality of sensors; obtaining, from the plurality of sensors, context data associated with a user related to a time segment; classifying each of the time segments into one of a plurality of thought states based on the context data; Mapping a task from a task dataset associated with the user to one of the plurality of mind states; a device including a processor configured to generate a prompt to encourage performance of the task during a designated one of the time segments, the designated time segment configured to correspond to the one of the plurality of mind states to which the task is mapped.

11. The system of claim 10 , wherein the processor is further configured to output the prompt to perform the task during the designated time segment on a display of the device.

12. 11. The system of claim 10, wherein the task dataset includes a plurality of tasks such as scheduling a doctor's appointment, voting for a candidate, selecting a food item, purchasing a gift, and purchasing a stock.

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