System and method for implementing artificial intelligence assistants in conjunction with user biometric data

Abstract

A system for implementing an artificial intelligence (AI) model in conjunction with biometric data. The system including one or more biometric trackers adapted to generate biometric data associated with a user and a computing system adapted to receive a query from the user. The computing system further adapted to normalize the biometric data, transmit the query and the normalized biometric data to the AI model, receive a response to the query from the AI model, the response generated by the AI model based in part on the normalized biometric data, and present the response to the user query to the user.

Description

[0001] Docket No. 531592.10050

[0002] SYSTEM AND METHOD FOR IMPLEMENTING ARTIFICIAL INTELLIGENCE

[0003] ASSISTANTS IN CONJUNCTION WITH USER BIOMETRIC DATA

[0004] TECHNICAL FIELD

[0005] The present teachings relate generally to artificial intelligence and, more particularly, to systems and methods for implementing artificial intelligence assistants in conjunction with user biometric data.

[0006] BACKGROUND ART

[0007] Generative artificial intelligence (Al) assistants, such as chatbots and / or virtual assistants, have been gaining in popularity are projected to save businesses over $4 trillion annually in productivity gains across sectors such as healthcare, customer service, and software development. These Al assistants can implement various Al models, workflows and / or other techniques (e.g., large language models (LLM), transformer models, natural language processing (NLP) techniques, deep learning techniques, machine learning techniques, model context protocol (MCP), and / or other suitable Al models, workflows, and / or other techniques) to quickly and efficiently respond to user queries. For example, Al assistants can be used to quickly respond to user questions, generate written content in response to receiving a prompt from a user, and / or even create unique images and / or video content.

[0008] However, the benefits offered by Al assistants in the form of improved efficiency and time savings do not come without their drawbacks. For example, in response to receiving a user query, many conventional Al assistants generate generic responses without considering specific attributes or characteristics associated with the user who generated the query. In that respect, conventional Al assistants may often generate the same response or very similar responses for users that ask similar questions to the Al assistant, regardless of the intention of the user asking the question, the state of mind of the user asking the question, and / or the application in which the Al assistant is being implemented. Docket No. 531592.10050

[0009] In addition, with the increased usage of Al assistants, there have also been growing concerns regarding the privacy of user data as users interact with Al assistants. For example, there are concerns a user’s personal data can be exploited by operators of the Al assistants and sold to third parties. As another example, there are concerns data that reveals identifying information about a user may be extracted from a query (e.g., question, prompt, etc.) that is submitted to an Al assistant in the event of a data breach or a network hack.

[0010] Therefore, it would be beneficial to have alternative systems and methods for protecting user data and generating more useful responses while using Al assistants.

[0011] DISCLOSURE OF INVENTION

[0012] The needs set forth herein as well as further and other needs and advantages are addressed by the present embodiments, which illustrate solutions and advantages described below.

[0013] The present teachings relate to systems and methods for implementing an artificial intelligence (Al) assistant, or more generally an Al model, in conjunction with biometric data. In particular, the present teachings relate to the secure transmission of queries and user biometric data over a network to an Al model, where the Al model can use the user biometric data to generate tailored, user-specific responses to the queries. In some examples, the Al model of the present teachings can be implemented using an Al agent. An Al agent is an Al model with certain capabilities suited for handling user queries and / or the transmission of user biometric data.

[0014] At least one technical advantage of the present teachings relative to the prior art solutions is that, with the present teachings, queries to an Al model, which can be implemented as a chatbot or a virtual assistant for example, are accompanied by biometric data indicative of a current bio state of the user. In that regard, the Al model can use the biometric data to generate a response to a query that is specifically tailored to the current bio state of the user. One additional technical advantage of the present teachings relative to the prior art solutions is that, with the present teachings, data privacy during transmission of queries and the user biometric data can be preserved Docket No. 531592.10050 through normalization of the user biometric data. In that regard, as opposed to exposing the actual user biometric data to an Al model hosted on a third-party server, with the disclosed techniques, only a normalization of the user biometric data is shared with the Al model.

[0015] In one aspect, a system for implementing an artificial intelligence (Al) model in conjunction with biometric data is provided. The system comprises one or more biometric trackers adapted to generate biometric data associated with a user. The system further comprises a computing system adapted to receive a query from the user; the computing system adapted to normalize the biometric data; the computing system adapted to transmit the query and the normalized biometric data to the Al model; the computing system adapted to receive a response to the query from the Al model, the response generated by the Al model based in part on the normalized biometric data; and the computing system adapted to present the response to the user query to the user.

[0016] In another aspect, a method comprising generating biometric data associated with a user; normalizing the biometric data; detecting a user interaction, preprocessing the user interaction and the normalized biometric data to create an input that is compatible with an Al model; transmitting the input to the Al model; receiving an output generated by the Al model, the output generated by the Al model based in part on the input; and presenting the output to the user.

[0017] Other aspects of the system and methods are described in detail below and are also part of the present teachings.

[0018] For a better understanding of the present embodiments, together with other and further aspects thereof, reference is made to the accompanying drawings and detailed description, and its scope will be pointed out in the appended claims.

[0019] BRIEF DESCRIPTION OF DRAWINGS

[0020] FIG. 1 is an illustration of one embodiment of a computing system, according to the present teachings. Docket No. 531592.10050

[0021] FIG. 2 is a block diagram of a central computing device that may be implemented in conjunction with a computing system, according to the present teachings.

[0022] FIG. 3 is a block diagram of a central computing server that may be implemented in conjunction with a computing system, according to the present teachings.

[0023] FIG. 4 is a block diagram of an artificial intelligence server that may be implemented in conjunction with a computing system, according to the present teachings.

[0024] FIG. 5 illustrates an example flow diagram of a process for querying an artificial intelligence model, according to the present teachings.

[0025] FIG. 6 is a flow diagram of method steps for querying an artificial intelligence model, according to the present teachings.

[0026] BEST MODE FOR CARRYING OUT THE INVENTION

[0027] The present teachings are described more fully hereinafter with reference to the accompanying drawings, in which the present embodiments are shown. The following description is presented for illustrative purposes only and the present teachings should not be limited to these embodiments. Any computer configuration and architecture satisfying the speed and interface requirements herein described may be suitable for implementing the system and method of the present embodiments.

[0028] In compliance with the statute, the present teachings have been described in language more or less specific as to structural and methodical features. It is to be understood, however, that the present teachings are not limited to the specific features shown and described, since the systems and methods herein disclosed comprise preferred forms of putting the present teachings into effect.

[0029] For purposes of explanation and not limitation, specific details are set forth such as particular architectures, interfaces, techniques, etc. in order to provide a thorough understanding. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description with unnecessary detail. Docket No. 531592.10050

[0030] A “computing system” may provide functionality for the present teachings. The computing system may include software executing on computer readable media that may be logically (but not necessarily physically) identified for particular functionality (e.g., functional modules). The computing system may include any number of computers / processors / mobile devices, which may communicate with each other over a network. The computing system may be in electronic communication with a datastore (e.g., database) that stores control and data information. Forms of computer readable media include, but are not limited to, disks, hard drives, random access memory, programmable read only memory, or any other medium from which a computer can read.

[0031] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The use of “first”, “second,” etc. for different features / components of the present disclosure are only intended to distinguish the features / components from other similar features / components and not to impart any order or hierarchy to the features / components.

[0032] To aid the Patent Office and any readers of a patent issued on this application in interpreting the claims appended hereto, it is noted that none of the appended claims or claim elements are intended to invoke 35 U.S.C. 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim.

[0033] Recitations of numerical ranges by endpoints include all numbers within that range (e.g., 1 to 5 includes 1 , 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.). Where a range of values is “greater than”, “less than”, etc., of a particular value, that value is included within the range.

[0034] Any direction referred to herein, such as “top,” “bottom,” “left,” “right,” “upper,” lower,” “above,” below,” and other directions and orientations are described herein for Docket No. 531592.10050 clarity in reference to the figures and are not to be limiting of an actual device or system or use of the device or system. Many of the devices, articles, or systems described herein may be used in a number of directions and orientations.

[0035] Any citation to a reference in this disclosure or during the prosecution thereof is made out of an abundance of caution. No citation (whether in an Information Disclosure Statement or otherwise) should be construed as an admission that the cited reference qualifies as prior art or comes from an area that is analogous or directly applicable to the present teachings.

[0036] Referring now to FIG. 1 , shown is one embodiment of a computing system 100 according to the present teachings. The computing system 100 may be, for example, adapted to implement a large language model in the form of an artificial intelligence (Al) assistant, such as a chatbot or a virtual assistant, in conjunction with biometric data of a user 102. As will be described in more detail herein, the computing system 100 can monitor biometric data of the user 102 and provide the monitored biometric data of the user to an Al model when the user 102 queries or otherwise interacts with the Al model. The Al model can then generate a tailored response to the user query based in part on the biometric data of the user 102. In some examples, the Al model is implemented using an Al agent.

[0037] In some examples, the computing system 100 is implemented within a digital content playback environment. In such examples, the computing system 100 can be adapted to present, generate, and / or modify digital content to a user 102 and monitor the biometric data of the user 102 while the user 102 is presented with the digital content. Then, during playback of the digital content, the computing system 100 can provide the monitored biometric data of the user 102 to the Al model when the user 102 queries the Al model. Moreover, the Al model can generate a tailored response to the user query based in part on the biometric data of the user 102. In some examples, the response to the user query can include newly generated digital content, a digital content recommendation, and / or modified digital content generated by the Al model.

[0038] As described herein, the term digital content can refer to any one or more of virtual reality (VR) content, augmented reality (AR) content, mixed reality (MR) content, Docket No. 531592.10050 audio content, spatial content, video content, 2D video content, flat or non-spatial video content, vibrational or haptic content, aroma content, and / or audiovisual content. In that regard, the system 100 is configured to provide VR content, AR content, MR content, audio content, spatial content, video content, vibrational or haptic content, aroma content, and / or audiovisual content to a user and monitor the biometrics of the user 102 resulting from the provided VR content, AR content, MR content, audio content, spatial content, video content, vibrational or haptic content, aroma content, and / or audiovisual content.

[0039] As shown in FIG. 1 , the computing system 100 includes a central computing device 104, one or more biometric trackers 106, a central computing server 108, and one or more Al servers 110 that are connected to each other via one or more communications networks 112. The one or more communications networks 112 can hereinafter be referred to as the “network 112.”

[0040] In some examples, the network 112 can be implemented as a combination of one or more of a wide area network (WAN) (e.g., the Internet, any TCP / IP based network, a cellular network, such as, for example, a Global System for Mobile Communications [GSM] network, a General Packet Radio Services [GPRS] network, a Code Division Multiple Access [CDMA] network, an Evolution-Data Optimized [EV-DO] network, an Enhanced Data Rates for GSM Evolution [EDGE] network, a 3 GSM network, a 4GSM network, a Digital Enhanced Cordless Telecommunications [DECT] network, a Digital AMPS [IS-136 / TDMA] network, or an Integrated Digital Enhanced Network [iDEN] network, etc.), a local area network (LAN), a neighborhood area network (NAN), a home area network (HAN), and / or a personal area network (PAN) employing any of a variety of communications protocols, such as Wi-Fi, Bluetooth, ZigBee, etc. In some examples, the network 112 can be implemented using a WebSocket connection and / or an HTTP connection. In some examples, the network 112 can be implemented using RTP / SRTP (e.g., WebRTC) and / or ICE (e.g., STUN / TURN) protocols.

[0041] As will be described in more detail herein, the central computing device 104 is adapted to interact with an Al model. For example, the user 102 can use the central computing device 104 to query an Al model and receive responses to the queries from the Al model. As used herein, a user query to the Al model can include one or more of a Docket No. 531592.10050 prompt input by the user 102, a question input by the user 102, a conversation between the user 102 and the Al model, and / or some other type of interaction (e.g., guided meditation) between the user 102 and the Al model. The central computing device 104 can further be adapted to present digital content to the user 102. The central computing device 104 can be implemented as, for example, a smartphone, a tablet, a pin, a wearable computing device (e.g., a virtual reality (VR) headset, a smart necklace, a fitness band, or an augmented reality (AR) headset), a laptop, a desktop computer, a television, a smart television, and / or any other suitable computing device.

[0042] Hereinafter, the one or more biometric trackers 106 may be collectively and / or individually referred to as “the biometric tracker(s) 106.” The biometric trackers 106, which can also include cameras, are adapted to monitor one or more biometric parameters of the user 102 and generate biometric data indicative of the one or more biometric parameters of the user 102. Biometric data can include data indicative of one or more biometric parameters of the user 102 such as, without limitation, heart rate, heart rate variability (HRV), blood volume controlled by the heart's pumping actions, blood oxygen saturation, blood pressure, the rise and fall of a user’s chest, temperature, skin conductance, brainwaves, fMRI, Vagus Nerve output, and / or any other biometric data associated with a user. In some examples, the biometric tracker 106 can include one or more of an electroencephalogram (EEG) monitor, an electrocardiogram (ECG) monitor, a heart rate monitor, a respiratory monitor, a blood pressure monitor, a blood oxygen saturation monitor, a temperature sensor, a skin conductance monitor, a functional magnetic resonance imaging (fMRI) monitor, a near-infrared spectroscopy (NIRS) monitor, and / or any other suitable tracker or monitor now known or hereafter developed. In some examples, the biometric tracker 106 can be a single device capable of capturing data associated with multiple biometric parameters of the user.

[0043] In some examples, the biometric tracker 106 can be provided in the form of an integrated device with multiple sensing devices, wherein each sensing device can be designed to collect biometric data associated with one or more biometric parameters. In some non-limiting embodiments, the biometric tracker 106 can be provided in the form of a personal consumer electronic or wearable monitoring device like a fitness tracker, smartwatch, smart ring, earbuds, headphones, VR / AR / MR headset or device, or any other suitable device now known or hereafter developed. In some embodiments, the Docket No. 531592.10050 biometric tracker 106 can be provided in the form of a pair of headphones that includes one or more integrated biometric sensors, a pair of earbuds that includes one or more integrated biometric sensors, and / or some other wearable device that includes biometric sensors.

[0044] Although shown as separate devices, in some examples, the central computing device 104 and the one or more biometric trackers 106 can be integrated within a single device. In some examples, the central computing device 104 can include one or more internal biometric trackers 106. In some examples, the central computing device 104 can be connected directly to one or more external biometric trackers 106 via a wired connection and / or a local network connection. In some examples, the central computing device 104 can be connected to one or more external biometric trackers 106 via the network 112. In some examples, the central computing device 104 can be connected to one or more biometric trackers 106 via the central computing server 108.

[0045] In some examples, the central computing device 104, the one or more biometric trackers 106, the central computing server 108, and / or the Al server 110 can be integrated within and / or implemented using a single device. In such examples, functionality described herein as being performed by each of the central computing device 104, one or more biometric trackers 106, the central computing server 108, and / or the Al server 110 can be performed by and / or implemented using a single computing device (e.g., the central computing device 104). In some examples, the central computing device 104, the central computing server 108, and / or the Al server 110 can be integrated within and / or implemented using a single device. In such examples, functionality described herein as being performed by each of the central computing device 104, the central computing server 108, and / or the Al server 110 can be performed by and / or implemented using a single computing device (e.g., the central computing device 104).

[0046] The central computing server 108 may be implemented using one or more servers, a cloud-based computing system, or any other suitable types of computing devices. As will be described in more detail herein, the central computing server 108 can be adapted to connect, via the network 112, the central computing device 104 to one or more biometric trackers 106 and / or to the one or more Al servers 110. For Docket No. 531592.10050 example, the central computing server 108 can be used to relay biometric data generated by one or more biometric trackers 106 to the central computing device 104. As another example, the central computing server 108 can be adapted to relay user queries to an Al model from the central computing device 104 to one or more Al servers 110 and / or relay responses to user queries from the Al servers 110 to the central computing device 104. In some examples, the central computing server 108 is adapted to manage communication sessions between the central computing device 104 and one or more Al servers 110. In some examples, the central computing server 108 can further be adapted to analyze, process, and / or modify biometric data generated by the biometric trackers 106. For example, the central computing server 108 can normalize biometric data generated by a biometric tracker 106, where normalizing biometric data can include converting biometric data from one value to another value.

[0047] In some examples, one or more functions described herein as being performed by the central computing server 108 can also and / or alternatively performed by the central computing device 104. For example, the central computing device 104 can be adapted to communicate, via the network 112, directly with the biometric trackers 106 and / or with one or more Al servers 110. In that regard, in some examples, the computing system 100 can operate without the central computing server 108 relaying communications between the central computing device 104, the biometric trackers 106, and / or one or more Al servers 110. In some examples, the central computing device 104 can further be adapted to normalize biometric data generated by the biometric trackers 106 and / or manage communication sessions between the central computing device 104 and / or the one or more Al servers 110. In some examples, the central computing server 108 and the central computing device 104 can be integrated in and / or implemented as a single computing device.

[0048] The one or more Al servers 110 are adapted to host, or execute, one or more Al models adapted to process and respond to user queries. These Al models can, for example but without limitation, implement one or more natural language processing (NLP) techniques, large language models (LLM), deep learning techniques, machine learning techniques, generative Al techniques, model context protocol (MCP), and / or other suitable Al models, workflows, and / or other techniques to process and respond to user queries. For simplicity, in the following description, the one or more Al servers 110 Docket No. 531592.10050 may collectively be referred to as an Al server 110 or the Al servers 110. The Al servers 110 may be implemented using one or more servers, a cloud-based computing system, or any other suitable types of computing devices. In some examples, the functionality described herein as being performed by the Al server 110 can additionally and / or alternatively be performed by the central computing server 108.

[0049] During operation of the computing system 100, the user 102 can use the central computing device 104 to query an Al model hosted on the Al server 110. For example, the user can input, via voice or text, a query to the central computing device 104. The central computing device 104 is adapted to receive the query from the user 102 and transmit, via the network 112, the query to the Al server 110. In some examples, the central computing device 104 transmits the user query directly to the Al server 110. In other examples, the central computing device 104 transmits the user query to the central computing server 108 and the central computing server 108 then transmits the user query to the Al server 110. In some examples, the Al model(s) running on the Al server 110 can be implemented using one or more Al agents.

[0050] Furthermore, during operation of the computing system 100, the one or more biometric trackers 106 generate biometric data indicative of one or more biometric parameters of the user 102. Hereinafter, biometric data indicative of one or more biometric parameters of the user 102 can be referred to as “user biometric data.” For example, the biometric tracker(s) 106 generate user biometric data as the user 102 interacts with the Al model hosted on the Al server 110 and / or as the central computing device 104 presents digital content to the user 102. In some examples, the one or more biometric trackers 106 generate user biometric data on a continuous and / or periodic basis. In other examples, the one or more biometric trackers 106 generate user biometric data on an ad-hoc basis and / or upon request.

[0051] In some examples, the biometric tracker(s) 106 can transmit the generated user biometric data to the central computing device 104. For example, the biometric tracker(s) 106 can transmit the generated user biometric data to the central computing device 104 via a wired connection, a wireless connection, and / or via the network 112. In some examples, the biometric tracker(s) can transmit the generated user biometric data to the central computing device 104 via the central computing server 108. For example, Docket No. 531592.10050 a biometric tracker 106 can transmit, via the network 112, user biometric data to the central computing server 108 and the central computing server 108 can then forward, via the network 112, the user biometric data to the central computing device 104.

[0052] The biometric data generated by the one or more biometric tracker(s) 106 can be used by the Al model hosted on the Al server 110 when the Al model generates a response to the user queries. In that regard, the central computing device 104 and / or the central computing server 108 can transmit the user biometric data to the Al server 110 when the user query is transmitted to the Al server 110. In some examples, the central computing device 104 and / or the central computing server 108 transmit the user biometric data to the Al server 110 at the same time the central computing device 104 and / or the central computing server 108 transmit the user query to the Al server 110. In some examples, the central computing device 104 and / or the central computing server 108 transmit the user biometric data to the Al server 110 before the user query has been transmitted to the Al server 110. In some examples, the central computing device 104 and / or the central computing server 108 transmit the user biometric data to the Al server 110 after the user query has been transmitted to the Al server 110. In some examples, the central computing device 104 and / or the central computing server 108 transmit the user biometric data to the Al server 110 in response to receiving a request for the user biometric data from the Al server 110.

[0053] As described herein, the Al model hosted on the Al server 110 can generate a response to the user query based in part on the user biometric data. For example, in response to receiving the user query and the user biometric data from the central computing device 104 and / or the central computing server 108, the Al model hosted on the Al server 110 can generate a response to the user query that factors in the current bio state of the user 102 (e.g., based in part on the user biometric data). In that regard, the Al model can more accurately tailor a response to a user query by considering the current bio state of the user 102. In some examples, the Al model implements one or more large language models to generate the response to the user query. In other examples, the Al model implements one or more other types of Al models and / or techniques to generate the response to the user query. The Al server 110 can then transmit the response to the user query back to central computing device 104, either directly via the network 112 or indirectly via the central computing server 108. The Docket No. 531592.10050 central computing device 104 then presents the response to the user query to the user 102. For example, the central computing device 104 presents the response to the user query to the use 102 via a display device and / or one or more speakers.

[0054] In some examples, to protect the privacy of the user 102, the user biometric data is normalized and / or otherwise modified before being transmitted to the Al server 110. For example, the central computing device 104 and / or the central computing server 108 can scale, or normalize, the user biometric data received from the biometric tracker(s) 106 and then transmit the normalized user biometric data to the Al server 110. In some examples, normalizing the user biometric data can include using the variation in the data to place user biometric data values onto a standard bell curve that is specific to the user. For example, normalizing heart rate data associated with the user can include fitting values of user hear rate data onto a standard bell curve for heart rate data associated with the user.

[0055] In some examples, normalizing the user biometric data by the includes filtering incoming sensor streams from the biometric trackers 106 to remove artifacts from the user biometric data. In some examples, normalizing the user biometric data includes segmenting the user biometric data into one or more epochs and computing features such as, but not limited to, HRV, galvanic skin response (GSR) peaks, EEG bandpower, and / or others. In some examples, normalizing the user biometric data includes normalizing each feature in the biometric data relative to the user’s baseline biometric (e.g., z-score, min-max, etc.). In some examples, normalizing the user biometric data includes mapping each normalized feature of the user biometric data to a discrete performance bin (e.g., mapping to one of bins 1-5). Mapping the normalized data to discrete performance bins serves not only to further obscure data, but also to put the data in a format that can be more efficiently processed by an LLM or some other type of Al model. In some examples, normalizing the user biometric data includes packaging the user biometric data into anonymized indices, not raw values, for transmission to one or more Al models and / or the Al server 1 10.

[0056] In some examples, normalizing the user biometric data may include, converting the user biometric data from a first value to a second value, averaging multiple user biometric data measurements, inputting the user biometric data into an algorithm and / or Docket No. 531592.10050 a trained model that outputs a normalized user biometric data value, and / or some other type of modification to the user biometric data. In some examples, normalizing the user biometric data includes assigning a score (e.g., a score of 0 to 3) to the user biometric data and then transmitting the assigned score to the Al server 110. For example, the central computing device 104 and / or the central computing server 108 can assign a score of 0 to user biometric data having a value that falls within a first range, can assign a score of 1 to user biometric data having a value that falls within a second range, can assign a score of 2 to user biometric data having a value that falls within a third range, and can assign a score of 3 to user biometric data having a value that falls within a fourth range.

[0057] In one particular example, the user biometric data includes heart rate data of the user 102 that was generated by one or more biometric trackers 106. The heart rate data may be, for example, represented as values in beats per minute (bpm). In this example, the central computing device 104 and / or the central computing server 108 averages the most recent heart rate measurements generated by the one or more biometric trackers 106 and assigns a numeric score to the average heart rate value. For example, the central computing device 104 and / or the central computing server 108 assigns a heart rate score of 0 to 3 to the averaged heart rate value. After assigning the heart rate score, instead of transmitting the original user biometric data to the Al server 110, the central computing device 104 and / or the central computing server 108 can merely transmit the normalized user biometric data (e.g., the assigned heart rate score) to the Al server 110 so as to protect the actual user biometric data from being exposed to the Al model hosted on the Al server 110.

[0058] Similar to the above, the Al model hosted on the Al server 110 can generate a response to the user query based in part on the normalized user biometric data. For example, in response to receiving the user query and the normalized user biometric data from the central computing device 104 and / or the central computing server 108, the Al model hosted on the Al server 110 can generate a response to the user query that factors in the current bio state of the user 102 (e.g., based in part on the normalized user biometric data). In that regard, the Al model can more accurately tailor a response to a user query by considering the current bio state of the user 102. The Al server 110 can then transmit the response to the user query back to central computing device 104, Docket No. 531592.10050 either directly via the network 112 or indirectly via the central computing server 108. The central computing device 104 presents the response to the user query to the user 102. For example, the central computing device 104 presents the response to the user query to the use 102 via a display device and / or one or more speakers.

[0059] In some examples, during a user session in which the user 102 interacts with the Al model, communications between the user 102 and the Al model hosted on the Al server 110 are stored in a thread. As used herein, the term “thread” refers to a interaction between the user 102 and the Al model hosted on the Al server 110. For example, a thread can include a history and / or contextual details associated with previous queries to the Al model, responses to queries that were previously generated by the Al model and sent to the user 102, previous user biometric data, previous normalized user biometric data, and / or other information associated with conversation between the user 102 and the Al model. In some examples, the interaction between the user 102 and the Al model hosted on the Al server 110 can include a conversation. In other examples, the interaction between the user 102 and the Al model hosted on the Al server 110 can be of some other type, such as a guided meditation.

[0060] Threads associated with a particular user 102 can be stored on the central computing device 104, the central computing server 108, and / or the Al server 110. In some examples, there is no persistence between different user sessions. In such examples, each time a new user session is initiated, a new thread is generated to track the conversation and / or other type of interaction between the user 102 and the Al model. Moreover, in such examples, each time a user session is ended, the thread is deleted thereby erasing data included the thread.

[0061] In some examples, there may be persistence between different user sessions. In such examples, when a new user session is initiated, either a new thread or an existing thread can be used to track the conversation and / or other type of interaction (e.g., guided meditation) between the user 102 and the Al model. In some examples, threads associated with a user 102 are deleted when a particular amount of time passes after a user session is ended. For example, a thread may be deleted one hour after a user session is ended, one day after a user session is ended, one week after a user session Docket No. 531592.10050 is ended, one month after a user session is ended, or some other amount of time after a user session is ended.

[0062] FIG. 2 is a block diagram of the central computing device 104 that may be implemented in conjunction with the computing system 100, according to present teachings. As described herein, the central computing device 104 can be implemented as, for example, a smartphone, a tablet, a wearable computing device (e.g., a smartwatch, a virtual reality (VR) headset, or an augmented reality (AR) headset), a laptop, a desktop computer, a television, a smart television, and / or any other suitable computing device. Persons skilled in the art will understand that the central computing device 104 shown in FIG. 2 provides just one non-limiting example architecture that can be used to implement the central computing device 104 included in the computing system 100. Moreover, other suitable computing devices not described herein may be used to implement the central computing device 104.

[0063] As shown in FIG. 2, the central computing device 104 may include, without limitation, a processor 202, a graphics subsystem 204, an I / O device interface 206, a network interface 208, an interconnect 210, a memory subsystem 212, and a system disk 214. The interconnect, or bus, 210 can include one or more wires, cables, traces, contacts, analog components, digital components, wireless connection components, and / or other suitable means for interconnecting hardware components of the central computing device 104.

[0064] In some embodiments, the processor 202 (e.g., a CPU or similar processor) is adapted to retrieve and execute programming instructions stored in the memory subsystem 212. Similarly, the processor 202 is adapted to store and retrieve application data (e.g., software libraries) residing in the memory subsystem 212 and / or the system disk 214. The interconnect 210 is adapted to facilitate transmission of data, such as programming instructions and application data, between the processor 202, the graphics subsystem 204, the I / O devices interface 206, the network interface 208, the memory subsystem 212, and the system disk 214.

[0065] In some embodiments, the graphics subsystem 204 is adapted to generate frames of image and / or video data and transmit the frames of image and / or video data Docket No. 531592.10050 to display device 216. In some embodiments, the graphics subsystem 204 may be integrated into an integrated circuit, along with the processor 202. The display device 216 may comprise any technically feasible means for generating an image for display. For example, the display device 216 may be fabricated using liquid crystal display (LCD) technology, cathode-ray technology, and light-emitting diode (LED) display technology. The display device 216 may include, for example, one or more monitors.

[0066] The input / output (I / O) device interface 206 is adapted to receive input data from user I / O devices 218 and transmit the input data to the processor 202 via the interconnect 210. For example, user I / O devices 218 may comprise one or more buttons, a touchscreen, a keyboard, and a mouse or other pointing device. The I / O device interface 206 also includes an audio output unit adapted to generate an electrical audio output signal. User I / O devices 218 may comprise one or more speakers adapted to generate an acoustic output in response to the electrical audio output signal. In alternative embodiments, the display device 216 may include the speaker. In some examples, the I / O device interface 206 can also receive user biometric data from one or more biometric trackers 106 connected to the central computing device 104.

[0067] The network interface 208 is adapted to transmit and receive packets of data via the network 106. For example, the network interface 208 is adapted to transmit user queries to the central computing server 108 and / or the Al server 110 via the network 112. As another example, the network interface 208 is adapted to receive responses to the user queries and / or user biometric data via the network 112. In some embodiments, the network interface 208 is adapted to communicate using the well-known Ethernet standard. In some embodiments, the network interface 208 is adapted to communicate using the well-known wireless communication protocols. The network interface 208 is coupled to the processor 202 via the interconnect 210.

[0068] The system disk 214, such as a hard disk drive or flash memory storage drive, is adapted to store non-volatile data. For example, the system disk 214 stores one or more files, applications, and / or programs to be implemented by the processor 202. In some examples, the system disk 214 stores digital content to be played back to the user 102. Docket No. 531592.10050

[0069] In some embodiments, the memory subsystem 212 includes programming instructions and application data that comprise an operating system 220, a user interface 222, a digital content playback application 224, and an Al user application 226. The operating system 220 performs system management functions such as managing hardware devices including graphics subsystem 204, I / O devices interface 206, the network interface 208, and system disk 214. The operating system 220 also provides process and memory management models for the user interface 222, the digital content playback application 224, and the Al user application 226. The user interface 222, such as a window and object metaphor, provides a mechanism for user interaction with central computing device 104. Persons skilled in the art will recognize the various operating systems and user interfaces that are well-known in the art and suitable for incorporation into the central computing device 104.

[0070] When executed by the processor 202, the digital content playback application 224 can be used to present, or play back, digital content to the user 102. For example, the digital content playback application 224 can present digital content to the user via the display device 216 and / or one or more I / O devices 218. As described herein, the digital content can include any one or more of virtual reality (VR) content, augmented reality (AR) content, mixed reality (MR) content, audio content, spatial content, particle effects, vibrational or haptic content, aroma content, video content, 2D video content, flat or non-spatial video content, and / or audiovisual content. In some examples, the digital content played back by the digital content playback application 224 includes meditative digital content.

[0071] When executed by the processor 202, the Al user application 226 implements one or more of the functions described herein with respect to FIG. 1 . For example, the Al user application 226 provides an interface through which the user 102 can interact with an Al model hosted on the Al server 110. In that regard, the Al user application 226 provides a mechanism through which the user 102 can enter user queries to the Al model. For example, the Al user application 226 can receive user queries from the user 102 in the form of voice, text, touch, and / or some other format. Moreover, the Al user application 226 can prompt the 102 to enter a user query to the Al model. Docket No. 531592.10050

[0072] In response to receiving a user query, the Al user application 226 can transmit, via the network interface 208, the user query to the Al model hosted on the Al server 110. For example, the Al user application 226 can transmit the user query directly to the Al server 110 or to the central computing server 108, which relays the user query to the Al server 110. Moreover, the Al user application 226 can receive, via the network interface 208, responses to the user query from the Al model. In some examples, the Al user application 226 can receive the response to the user query directly from the Al server 110. In other examples, the Al user application 226 can receive the response to the user query from the central computing server 108. In response to receiving response to the user query, the Al user application 226 can present the response to the user query to the user 102 via the display device 216 and / or one or more I / O devices 218 (e.g., one or more speakers and / or haptic feedback devices).

[0073] In some examples, the Al user application 226 can also receive user biometric data from the one or more biometric trackers 106. For example, the Al user application 226 can receive the user biometric data via the network interface 208 and / or via the I / O devices interface 206. The Al user application 226 can then transmit the user biometric data to the central computing server 108 and / or the Al server 110. In some examples, the Al user application 226 can normalize and / or otherwise modify the user biometric data using one or more of the techniques described herein with respect to FIG. 1 . In such examples, the Al user application 226 can then transmit the normalized user biometric data to the central computing server 108 and / or the Al server 110.

[0074] In some examples, the Al user application 226 can implement an Al model. In such examples, the functionality described herein as being performed by the Al server 110 and / or the Al model hosted on the Al serve 110 can alternatively be performed by the central computing device 104, for example, via the Al user application 226.

[0075] In some examples, the Al user application 226 can be integrated within one or more other applications. For example, the Al user application 226 can be integrated within a third-party digital content application, such as the digital content playback application 224. In some examples, the Al user application 226 can be overlayed on a third-party application such that the third-party application is generating, modifying, and / or recommending digital content on the basis of the bio-data of a user 102 as Docket No. 531592.10050 captured through one or more biometric trackers 106, the digital content playback application 224, and / or the Al user application 226.

[0076] FIG. 3 is a block diagram of the central computing server 108 that may be implemented in conjunction with the computing system 100, according to the present teachings. As described herein, the central computing server 108 can be implemented as, for example, one or more servers, a cloud-based computing system, and / or any other suitable types of computing devices. Persons skilled in the art will understand that the central computing server 108 shown in FIG. 3 provides just one non-limiting example architecture that can be used to implement the central computing server 108 included in the computing system 100. Moreover, other suitable computing devices not described herein may be used to implement the central computing server 108.

[0077] As shown, in FIG. 3, the central computing server 108 may include, without limitation, a processor 302, an input / output (I / O) devices interface 304, a network interface 306, an interconnect 308, a system memory 310, and a system disk 312. The interconnect, or bus, 308 can include one or more wires, cables, traces, contacts, analog components, digital components, wireless connection components, and / or other suitable means for interconnecting hardware components of the central computing server 108.

[0078] The processor 302 is adapted to retrieve and execute programming instructions stored in the system memory 310. Similarly, the processor 302 is adapted to store application data (e.g., software libraries) and retrieve application data from the system memory 310. The interconnect 408 is adapted to facilitate transmission of data, such as programming instructions and application data, between the processor 302, I / O devices interface 304, the network interface 306, the system memory 310, and the system disk 312. The I / O devices interface 304 is adapted to receive input data from I / O devices 314 and transmit the input data to the processor 302 via the interconnect 308. For example, I / O devices 316 may include one or more buttons, a keyboard, a mouse, and / or other input devices. The I / O devices interface 304 is further adapted to receive output data from the processor 302 via the interconnect 308 and transmit the output data to the I / O devices 314. Docket No. 531592.10050

[0079] The system disk 312 may include one or more hard disk drives, solid state storage devices, cloud-based storage, or similar storage devices. The system disk 312 is adapted to store non-volatile data such as files (e.g., audio files, video files, subtitles, application files, software libraries, etc.). For example, the system disk 312 can be adapted to store one or more user profiles, communication threads, user biometric data, and / or data associated with users. In some examples in which the central computing server 108 is further adapted to implement the functions described herein as being performed by the Al server, the system disk 312 can store one or more Al models.

[0080] The system memory 310 includes a management application 316 and one or more threads 318. When executed by the processor 302, the management application 316 implements one or more of the functions described herein with respect to FIG. 1 as being performed by the central computing server 108. For example, the management application 316 manages communications between the central computing device 104 and the Al server 110 and / or manages communications between the central computing device 104 and the one or more biometric trackers 106.

[0081] In managing communications between the central computing device 104 and the one or more biometric trackers 106, the management application 316 can receive, via the network interface 306, user biometric data from one or more biometric trackers 106 and provide the user biometric data to the central computing device 104. In some examples, the management application 316 can instead receive the user biometric data directly from the central computing device 104. Using the techniques described herein with respect to FIG. 1 , the management application 316 can further normalize and / or otherwise modify the user biometric data. For example, the management application 326 can assign a score to the user biometric data and / or convert the value of the user biometric data from a first value to a different value.

[0082] In some examples, it is beneficial to normalize and / or modify user biometric data before transmitting the user biometric data to an Al model for standardization purposes, as thresholds can be altered and / or customized based on the user 102. Moreover, this user customization helps to keep individual medical data (such as if someone has a higher or lower heart rate) hidden in a more general sense, more so than just any individual data point. Furthermore, when compared to quantitative data comprised in Docket No. 531592.10050 user biometric data, normalized and / or modified biometric data may be more compatible with an Al model. For example, an Al model may be uncertain how to handle and / or use a biometric data point such as 120 bpm. However, that same Al model would generally understand, even without proper instruction, the meaning of and how to use a heart rate value of 3 on a scale of 0-3.

[0083] In managing communications between the central computing device 104 and the Al server 110, the management application 316 can receive, via the network interface 306, user queries from the central computing device 104 and transmit, via the network interface 306, the user queries to the Al server 110. In transmitting user queries to the Al server 110, the management application 316 can also transmit user biometric data and / or normalized user biometric data to the Al server 110. Moreover, the management application 316 can receive, via the network interface 306, responses to the user queries from the Al server 110 and transmit, via the network interface 306, the responses to the user queries to the central computing device 104.

[0084] As described herein, conversations and / or other interactions between a particular user 102 and the Al model hosted on the Al server 110 can be contained, or stored, within respective threads 318, where the threads 318 contain user queries, responses to user queries, user biometric data, and other information shared between the user 102 and the Al model during a user session. During a user session, the management application 316 can execute a “run’ command to add communications received from the central computing device 104 to a thread 318, thereby making the communications available to the Al model. In some examples, one or more threads 318 corresponding to one or more users 102 can be stored and / or maintained in the memory 310 of the central computing server 108. In some examples, the one or more threads 318 corresponding to one or more users 102 can be stored and / or maintained in the system disk 312 of the central computing server 108. In some examples, the one or more threads 318 corresponding to one or more users 102 are stored in the Al server 110.

[0085] In some examples, the management application 316 generates a new thread 318 each time a user 102 initiates a new user session with the Al model hosted on the Al server 110. For example, the management application 316 generates a new thread 318 when the user 102 opens the Al user application 226 and / or the digital content playback Docket No. 531592.10050 application 224 on the central computing device 104. In some examples, the management application 316 retrieves, using a thread id and / or an id of the user 102, an existing thread 318 associated with the user 102 when the use 102 initiates a new user session. Existing threads 318 can include, for example, previous user queries, responses to user queries, and / or other information included in previous conversations and / or other interactions between the user 102 and the Al model.

[0086] In some examples, the management application 316 deletes a thread 318 immediately after a user session is ended. For example, the management application 316 deletes a thread after a user 102 closes out of the Al user application 226 and / or the digital content playback application 224 on the central computing device 104. In some examples, the management application 316 deletes a thread 318 after a particular amount of time elapses (e.g., after an hour, after a day, after a week, etc.) after a user session has ended. In some examples, the management application 316 deletes a thread 318 immediately after a user session is ended. In some examples, the management application 316 deletes a thread 318 upon user request.

[0087] FIG. 4 is a block diagram of the Al server 110 that may be implemented in conjunction with the computing system 100, according to the present teachings. As described herein, the Al server 110 can be implemented as, for example, one or more servers, a cloud-based computing system, and / or any other suitable types of computing devices. Persons skilled in the art will understand that the Al server 110 shown in FIG. 3 provides just one non-limiting example architecture that can be used to implement the Al server 110 included in the computing system 100. Moreover, other suitable computing devices not described herein may be used to implement the Al server 110.

[0088] As shown, in FIG. 4, the Al server 110 includes, without limitation, a processor 402, an input / output (I / O) devices interface 404, a network interface 406, an interconnect 408, a system memory 410, and a system disk 412. The interconnect, or bus, 408 can include one or more wires, cables, traces, contacts, analog components, digital components, wireless connection components, and / or other suitable means for interconnecting hardware components of the Al server 110. Docket No. 531592.10050

[0089] The processor 402 is adapted to retrieve and execute programming instructions stored in the system memory 410. Similarly, the processor 402 is adapted to store application data (e.g., software libraries) and retrieve application data from the system memory 410. The interconnect 408 is adapted to facilitate transmission of data, such as programming instructions and application data, between the processor 402, I / O devices interface 404, the network interface 406, the system memory 410, and the system disk 412. The I / O devices interface 404 is adapted to receive input data from I / O devices 414 and transmit the input data to the processor 402 via the interconnect 408. For example, I / O devices 416 may include one or more buttons, a keyboard, a mouse, and / or other input devices. The I / O devices interface 404 is further adapted to receive output data from the processor 402 via the interconnect 408 and transmit the output data to the I / O devices 414.

[0090] The system disk 412 may include one or more hard disk drives, solid state storage devices, or similar storage devices. The system disk 412 is adapted to store non-volatile data such as files (e.g., audio files, video files, subtitles, application files, software libraries, etc.). For example, the system disk 412 can be adapted to store one or more Al models.

[0091] The system memory 410 includes a query handling application 416 and one or more Al models 418. When executed by the processor 402, the query handling application 416 implements one or more of the functions described herein with respect to FIG. 1 as being performed by the Al server 110. For example, the query handling application 416 receives, via the network interface 408, a plurality of user query transmitted by the central computing device 104 and / or the central computing server 108. The query handling application 416 can also receive the user biometric data and / or the normalized user biometric data transmitted by the central computing device 104 and / or the central computing server 108.

[0092] In response to receiving a user query and the user biometric data and / or the normalized user biometric data, the query handling application 416 then uses one or more of the Al models 418 to process and generate a response to the user query. The one or more Al models 418 can, for example, implement one or more NLP techniques, LLM techniques, deep learning techniques, machine learning techniques, generative Al Docket No. 531592.10050 techniques, model context protocol (MCP), and / or other suitable Al models, workflows, and / or other techniques to process and generate responses to user queries. In some examples, the one or more Al models 418 are implemented as one or more Al agents. As described herein, the query handling application 416 can use the user biometric data and / or the normalized user biometric data to generate a response to the user query that is tailored specifically to the user 102. In some examples, the response to the user query includes a written response, a digital content recommendation, a response to alter the bio state and / or mood of the user 102, modified digital content, and / or new digital content.

[0093] After a response to the user query is generated by one or more of the Al models 418, the query handling application 416 transmits, via the network interface 408, the response to the user query to the central computing device 104 and / or to the central computing server 108. For examples in which the query handling application 416 transmits the response to the user query to the central computing server 108, the central computing server 108 can forward the response to the user query to the central computing device 104.

[0094] In some examples, the functionality described herein as being performed by the Al server 110 can alternatively be performed by the central computing server 108. In such examples, the central computing server 108 can implement the query handling application 416 and one or more Al models 418 to generate responses to user queries.

[0095] FIG. 5 illustrates an example flow diagram of a process 500 for querying an artificial intelligence model, according to the present teachings. Although the interaction between the devices in process 500 are shown in an order, persons skilled in the art will understand that the interactions may be performed in a different order, interactions may be repeated or skipped, and / or may be performed by components other than those described in FIG. 5.

[0096] Process 500 begins at step 502 at which the central computing device 108 plays back digital content to the user 102. As described herein, the digital content can include any one or more of virtual reality (VR) content, augmented reality (AR) content, mixed reality (MR) content, audio content, spatial content, vibrational or haptic content, aroma Docket No. 531592.10050 content, video content, 2D video content, flat or non-spatial video content, and / or audiovisual content. In some examples, the digital content played back by the digital content playback application 224 includes meditative digital content.

[0097] At step 504, the user 102 initiates, via the central computing device 104, a user session with the Al model hosted on the Al server 110. For example, the user 102 initiates the user session by opening the Al user application 226 and / or by playing back digital content with the digital content playback application 224. In some examples, the user 102 uses a voice command to initiate the user session.

[0098] At step 506, the central computing server 108 generates a new thread 318 and / or retrieves an existing thread 318 in response to the user 102 initiating a user session with the Al model. For example, the management application 316 retrieves an existing thread 318 from memory 310.

[0099] At step 508, the central computing device 104 receives a user query from the user 102. For example, the Al user application 226 receives, via one or more of the I / O devices 218, a user query from the user 102. At step 510, the central computing device 104 transmits the user query to the central computing server 108. For example, the Al user application 226 transmits the user query to the central computing server 108. In some examples, the central computing device 104 transmits the user query directly to the Al server 110. At step 512, the central computing server 108 receives the user query.

[0100] At step 514, the one or more biometric trackers 106 generate user biometric data. As described herein, the user biometric data can include, without limitation, heart rate, heart rate variability (HRV), blood volume controlled by the heart's pumping actions, blood oxygen saturation, blood pressure, the rise and fall of a user’s chest, temperature, skin conductance, brainwaves, fMRI, Vagus Nerve output and / or any other biometric data associated with the user 102. At step 516, the one or more biometric trackers 106 transmit the user biometric data to the central computing server 108. In some examples, the one or more biometric trackers 106 can additionally and / or alternatively transmit the user biometric data to the central computing device 104. Docket No. 531592.10050

[0101] At step 518, the central computing server 108 receives the user biometric data. Then, at step 520, the central computing server 108 normalizes the user biometric data. For example, using one or more of the techniques described herein, the management application 316 executing on the central computing server 108 normalizes the user biometric data. In some examples, normalizing the user biometric data by the includes filtering incoming sensor streams from the biometric trackers 106 to remove artifacts from the user biometric data. In some examples, normalizing the user biometric data includes segmenting the user biometric data into one or more epochs and computing features such as, but not limited to, HRV, galvanic skin response (GSR) peaks, EEG band-power, and / or others. In some examples, normalizing the user biometric data includes normalizing each feature in the biometric data relative to the user’s baseline biometric (e.g., z-score, min-max, etc.). In some examples, normalizing the user biometric data includes mapping each normalized feature of the user biometric data to a discrete performance bin (e.g., mapping to one of bins 1-5). In some examples, normalizing the user biometric data includes packaging the user biometric data into anonymized indices, not raw values, for transmission to one or more other computing devices.

[0102] At step 522, the central computing server 108 transmits the user query and the normalized user biometric data to the Al server 110. In some examples, the central computing server 108 also adds the user query and / or the normalized user biometric data to the thread 318 generated and / or retrieved at step 506.

[0103] At step 524, the Al server 110 receives the user query and the normalized user biometric data. At step 526, the Al model hosted on the Al server 110 uses one or more Al models to generate a response to the user query based in part on the normalized user biometric data. For example, the query handling application 416 uses the normalized user biometric data to generate a response to the user query that is tailored to the current bio state of the user 102. At step 528, the Al server 110 transmits the response to the user query to the central computing server 108. In some examples, the Al server 110 transmits the response to the user query directly to the central computing device 104. Docket No. 531592.10050

[0104] At step 530, the central computing server 108 receives the response to the user query. In some examples, the central computing server 108 also adds the response to the user query to the thread 318 generated and / or retrieved at step 506. At step 532, the central computing server 108 transmits the response to the user query to the central computing device 104.

[0105] At step 534, the central computing device 104 receives the response to the user query. At step 536, the central computing device 104 presents the response to the user query to the user 102. For example, the Al user application 226 presents, within a user interface of the Al user application 226 and / or within an interface of the digital content playback application 224 while the digital content playback application 224 presents digital content to the user 102, the response to the user query to the user 102.

[0106] In some examples, the process 500 further includes step 538, at which the central computing server 108 deletes the thread 318 that was generated and / or retrieved at step 506. For example, the management application 316 executing on the central computing server 108 deletes the thread immediately or a predetermined amount of time (e.g., 1 hour, 1 day, 1 week, etc.) after the user session is ended. However, in some examples, the process 500 does not include step 538. In some examples, the central computing server 108 retains the thread 318 in a Health Insurance Portability and Accountability Act (HIPAA) compliant environment.

[0107] FIG. 6 is a flow diagram of method steps for querying an artificial intelligence assistant, according to the present teachings. Although the method steps are described with reference to the systems and processes of FIGS. 1-5, persons skilled in the art will understand that any system adapted to implement the method steps, in any order, falls within the scope of the present invention.

[0108] As shown, a method 600 begins at step 602, where a user query is received. For example, the central computing device 104 and / or the central computing server 108 receives a user query. In some examples, receiving the user query includes detecting a user interaction with the central computing device 104 and / or the central computing server 108. In some examples, the user query is a prompt or question input by a user. Docket No. 531592.10050

[0109] In some examples, the user query comprises a conversation or some other type of interaction (e.g., guided meditation) between a user and an Al model.

[0110] At step 604, user biometric data is generated. For example, one or more biometric trackers 106 and / or cameras generate user biometric data.

[0111] At step 606, the user biometric data is normalized. For example, the central computing device 104 and / or the central computing server 108 normalize the user biometric data using one or more of the techniques described herein. In some examples, normalizing the user biometric data can include using the variation in the data to place user biometric data values onto a standard bell curve that is specific to the user. For example, normalizing heart rate data associated with the user can include fitting values of user hear rate data onto a standard bell curve for heart rate data associated with the user.

[0112] In some examples, normalizing the user biometric data by the includes filtering incoming sensor streams from the biometric trackers 106 to remove artifacts from the user biometric data. In some examples, normalizing the user biometric data includes segmenting the user biometric data into one or more epochs and computing features such as, but not limited to, HRV, galvanic skin response (GSR) peaks, EEG bandpower, and / or others. In some examples, normalizing the user biometric data includes normalizing each feature in the biometric data relative to the user’s baseline biometric (e.g., z-score, min-max, etc.). In some examples, normalizing the user biometric data includes mapping each normalized feature of the user biometric data to a discrete performance bin (e.g., mapping to one of bins 1-5). Mapping the normalized data to discrete performance bins serves not only to further obscure data, but also to put the data in a format that can be more efficiently processed by an LLM or some other type of Al model. In some examples, normalizing the user biometric data includes packaging the user biometric data into anonymized indices, not raw values, for transmission to one or more Al models and / or the Al server 1 10.

[0113] In some examples, after the user biometric data is normalized, the normalized biometric data and / or the user biometric data can be added to an electronic health record (EHR) of the user 102. Docket No. 531592.10050

[0114] At step 608, the user query and the normalized user biometric data are transmitted to an Al model. For example, the central computing device 104 and / or the central computing server 108 transmit the user query and the normalized user biometric data to the Al model hosted on the Al server 110.

[0115] In some examples, prior to transmitting the user query and the normalized user biometric data to the Al server 110, the central computing device 104 and / or the central computing server 108 preprocesses the user query and the normalized user biometric data to a create an input that is compatible with the Al model.

[0116] At step 610, a response to the user query that was generated based in part on the normalized user biometric data is received. For example, the central computing device 104 and / or the central computing server 108 receives the response to the user query from the Al server 110, wherein the Al model hosted on the Al server 110 generated the response based in part on the normalized user biometric data.

[0117] At step 612, the response to the user query is presented to the user. For example, the central computing device 104 presents the response to the user query to the user 102. In some examples, the central computing device presents the response to the user 102 while the user 102 is viewing digital content played back on the central computing device 104.

[0118] 1. According to some embodiments, a system for implementing an artificial intelligence (Al) model in conjunction with biometric data. The system comprises one or more biometric trackers adapted to generate biometric data associated with a user and a computing system adapted to receive a query from the user; the computing system adapted to normalize the biometric data; the computing system adapted to transmit the query and the normalized biometric data to the Al model; the computing system adapted to receive a response to the query from the Al model, the response generated by the Al model based in part on the normalized biometric data; and the computing system adapted to present the response to the user query to the user. Docket No. 531592.10050

[0119] 2. The system according to clause 1 , wherein to normalize the biometric data, the computing system is adapted to assign a score to the biometric data based in part on a value of the biometric data.

[0120] 3. The system according to clause 1 or clause 2, wherein to normalize the biometric data, the computing system is adapted to map a normalized feature of the biometric data to a discrete performance bin.

[0121] 4. The system according to any of clauses 1-3, wherein to normalize the biometric data, the computing system is adapted to fit a value of the biometric data onto a standard bell curve associated with the user.

[0122] 5. The system according to any of clauses 1-4, wherein the biometric data includes one or more of heart rate, heart rate variability (HRV), blood volume controlled by a heart's pumping actions, blood oxygen saturation, blood pressure, a rise and fall of a chest of the user.

[0123] 6. The system according to any of clauses 1-5, wherein the computing system is further adapted to present digital content to the user; and wherein the user query is received while the computing system presents digital content to the user.

[0124] 7. The system according to any of clauses 1-6, wherein to present the response to the user query to the user, the computing system is adapted to present the response while digital content is presented to the user.

[0125] 8. The system according to any of clauses 1-7, wherein the one or more biometric trackers include at least one of a camera, an electroencephalogram (EEG) monitor, an electrocardiogram (ECG) monitor, a heart rate monitor, a respiratory monitor, a blood pressure monitor, a blood oxygen saturation monitor, a temperature sensor, a skin conductance monitor, a functional magnetic resonance imaging (fMRI) monitor, or a near-infrared spectroscopy (NIRS) monitor.

[0126] 9. The system according to any of clauses 1-8, wherein the computing system is further adapted to generate a communication thread; and wherein the computing Docket No. 531592.10050 system is adapted to log the query and the response to the query in the communication thread.

[0127] 10. The system according to any of clauses 1-9, wherein the user query comprises at least one of a prompt input by the user, a question input by the user, an interaction between the user and the computing system, or a conversation between the user and the Al model.

[0128] 1 1 . The system according to any of clauses 1 -10, wherein the computing system comprises a plurality of processors in communication over a network.

[0129] 12. According to some embodiments, a method comprising generating biometric data associated with a user; normalizing the biometric data; detecting a user interaction; preprocessing the user interaction and the normalized biometric data to create an input that is compatible with an artificial intelligence (Al) model; transmitting the input to the Al model; receiving an output generated by the Al model, the output generated by the Al model based in part on the input; and presenting the output to the user.

[0130] 13. The method according to clause 12, wherein normalizing the biometric data includes assigning a score to the biometric data based in part on a value of the biometric data.

[0131] 14. The method according to clause 12 or clause 13, wherein normalizing the biometric data includes mapping a normalized feature of the biometric data to a discrete performance bin.

[0132] 15. The method according to any of clauses 12-14, wherein normalizing the biometric data includes fitting a value of the biometric data onto a standard bell curve associated with the user.

[0133] 16. The method according to any of clauses 12-15, wherein the biometric data includes one or more of heart rate, heart rate variability (HRV), blood volume controlled by a heart's pumping actions, blood oxygen saturation, blood pressure, a rise and fall of a chest of the user. Docket No. 531592.10050

[0134] 17. The method according to any of clauses 12-16, wherein generating the biometric data includes using one or more of a camera, an electroencephalogram (EEG) monitor, an electrocardiogram (ECG) monitor, a heart rate monitor, a respiratory monitor, a blood pressure monitor, a blood oxygen saturation monitor, a temperature sensor, a skin conductance monitor, a functional magnetic resonance imaging (fMRI) monitor, or a near-infrared spectroscopy (NIRS) monitor.

[0135] 18. The method according to any of clauses 12-17 further comprising presenting digital content to the user; and detecting the user interaction while presenting the digital content to the user.

[0136] 19. The method according to any of clauses 12-18 further comprising presenting the output to the user while presenting the digital content to the user.

[0137] 20. According to some embodiments, one or more non-transitory computer- readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any of clauses 12-19.

[0138] Any and all combinations of any of the claim elements recited in any of the claims and / or any elements described in this application, in any fashion, fall within the contemplated scope of the present disclosure and protection.

[0139] The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

[0140] Aspects of the present embodiments may be embodied as a system, method, or computer program product. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “module” or “system.” Furthermore, aspects of the present disclosure may take the form of a Docket No. 531592.10050 computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

[0141] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0142] Aspects of the present disclosure are described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / acts specified in the flowchart and / or block diagram block or blocks. Such processors may be, without limitation, general purpose processors, special-purpose processors, application-specific processors, or field-programmable processors.

[0143] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and Docket No. 531592.10050 computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0144] While the present teachings have been described above in terms of specific embodiments, it is to be understood that they are not limited to these disclosed embodiments. Many modifications and other embodiments will come to mind to those skilled in the art to which this pertains, and which are intended to be and are covered by both this disclosure and the appended claims. It is intended that the scope of the present teachings should be determined by proper interpretation and construction of the appended claims and their legal equivalents, as understood by those of skill in the art relying upon the disclosure in this specification and the attached drawings.

Claims

Docket No. 531592.10050CLAIMSWhat is claimed is:1 . A system for implementing an artificial intelligence (Al) model in conjunction with biometric data, comprising: one or more biometric trackers adapted to generate biometric data associated with a user; and a computing system adapted to receive a query from the user; the computing system adapted to normalize the biometric data; the computing system adapted to transmit the query and the normalized biometric data to the Al model; the computing system adapted to receive a response to the query from the Al model, the response generated by the Al model based in part on the normalized biometric data; and the computing system adapted to present the response to the user query to the user.

2. The system of claim 1 , wherein to normalize the biometric data, the computing system is adapted to assign a score to the biometric data based in part on a value of the biometric data.

3. The system of claim 1 , wherein to normalize the biometric data, the computing system is adapted to map a normalized feature of the biometric data to a discrete performance bin.

4. The system of claim 1 , wherein to normalize the biometric data, the computing system is adapted to fit a value of the biometric data onto a standard bell curve associated with the user.

5. The system of claim 1 , wherein the biometric data includes one or more of heart rate, heart rate variability (HRV), blood volume controlled by a heart's pumping actions, blood oxygen saturation, blood pressure, a rise and fall of a chest of the user.Docket No. 531592.100506. The system of claim 1 , wherein the computing system is further adapted to present digital content to the user; and wherein the user query is received while the computing system presents digital content to the user.

7. The system of claim 4, wherein to present the response to the user query to the user, the computing system is adapted to present the response while digital content is presented to the user.

8. The system of claim 1 , wherein the one or more biometric trackers include at least one of a camera, an electroencephalogram (EEG) monitor, an electrocardiogram (ECG) monitor, a heart rate monitor, a respiratory monitor, a blood pressure monitor, a blood oxygen saturation monitor, a temperature sensor, a skin conductance monitor, a functional magnetic resonance imaging (fMRI) monitor, or a near-infrared spectroscopy (NIRS) monitor.

9. The system of claim 1 , wherein the computing system is further adapted to generate a communication thread; and wherein the computing system is adapted to log the query and the response to the query in the communication thread.

10. The system of claim 9, wherein the user query comprises at least one of a prompt input by the user, a question input by the user, an interaction between the user and the computing system, or a conversation between the user and the Al model.11 .The system of claim 1 , wherein the computing system comprises a plurality of processors in communication over a network.

12. A method, comprising: generating biometric data associated with a user; normalizing the biometric data; detecting a user interaction; preprocessing the user interaction and the normalized biometric data to create an input that is compatible with an artificial intelligence (Al) model;Docket No. 531592.10050 transmitting the input to the Al model; receiving an output generated by the Al model, the output generated by the Al model based in part on the input; and presenting the output to the user.

13. The method of claim 12, wherein normalizing the biometric data includes assigning a score to the biometric data based in part on a value of the biometric data.

14. The method of claim 12, wherein normalizing the biometric data includes mapping a normalized feature of the biometric data to a discrete performance bin.

15. The method of claim 12, wherein normalizing the biometric data includes fitting a value of the biometric data onto a standard bell curve associated with the user.

16. The method of claim 12, wherein the biometric data includes one or more of heart rate, heart rate variability (HRV), blood volume controlled by a heart's pumping actions, blood oxygen saturation, blood pressure, a rise and fall of a chest of the user.

17. The method of claim 12, wherein generating the biometric data includes using one or more of a camera, an electroencephalogram (EEG) monitor, an electrocardiogram (ECG) monitor, a heart rate monitor, a respiratory monitor, a blood pressure monitor, a blood oxygen saturation monitor, a temperature sensor, a skin conductance monitor, a functional magnetic resonance imaging (fMRI) monitor, or a near-infrared spectroscopy (NIRS) monitor.

18. The method of claim 12, further comprising: presenting digital content to the user; and detecting the user interaction while presenting the digital content to the user.

19. The method of claim 18, further comprising presenting the output to the user while presenting the digital content to the user.Docket No. 531592.1005020. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform the method of claim 12.

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