DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 02, 2024 and March 26, 2025 is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-13 and 15-80 are is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sports Data Labs, Inc. (WO2020/214699).
Regarding Claim 1, Sports Data Labs '699 discloses an animal data-based identification and recognition system (system 10; Fig. 1: [0079], 'the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors') comprising:
one or more source sensors (sensors 18) that gather animal data (animal data 14) from an assumed or unknown subject (targeted individual 16) wherein the animal data (14) is transmitted electronically (Fig. 1; [0038], 'With reference to Figure 1, a schematic of a system for providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted electronically'; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number)'; the targeted individual may be assumed or unknown):
one or more computing devices (Computing subsystem 22) configured to collect the animal data (14) from the one or more source sensors (18; Fig. 1; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24'), wherein;
the one or more computing devices (22) are also configured to gather reference animal data related to a targeted subject, a targeted medical condition, or a targeted biological response (Fig. 1; [0041], 'Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data [reference data] is used for at least a targeted subject and medical condition);
the one or more computing devices (22) are also configured to create, modify, or enhance at least one unique asset related to the targeted subject, the targeted medical condition, or the targeted biological response based upon the reference animal data (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different
subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset); the one or more computing devices (22) are configured to perform a comparison by comparing the at least one created, modified, or enhanced unique asset with at least a portion of the animal data (14) derived from the one or more source sensors (18), or its one or more derivatives, from the assumed or unknown subject (16; Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users
for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16); and
the comparison between the at least one created, modified, or an enhanced unique asset and the animal data (14) derived from the one or more source sensors (18), or one or more derivatives thereof, identifies the targeted subject, the targeted medical condition, or the targeted biological response (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a
refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], The system may identity these requested parameters Within the data-sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16).
Regarding Claim 2, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the comparison to identify the targeted subject (16), the targeted medical condition, or the targeted biological response occurs between two or more unique assets, at least one of which is a created, modified, or the enhanced unique asset from the animal data (14) derived from the one or more source sensors (18; Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group
of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], "The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or
artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the
target subject'; [00112], 'the Output information may not be exhibited as a number (e.g. percentage). it may be shown in a number of ways including as a graph, a color (i.e, green might mean foil power; red might mean very fatigued and out of energy), or other indices. It may also be communicated to a user in a number of ways including visually (as described above, which also may be integrated into a virtual reality or augmented reality offering and overlaid on top of an athlete or team), verbally (e.g., a virtual assistant providing audio related to the information and whether or not to place a bet), or physically (e.g., a user may have a smart watch that provides a notification and
vibrates when the user receives the notification related to the data).'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16; identification output of multiple assets can be provided in two or more ways).
Regarding Claim 3, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 2, wherein the two or more unique assets identify the targeted subject, one or more medical conditions, or one or more biological responses (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. in a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history,
heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request
(which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; [00112], 'the Output information may not be exhibited as a number (e.g, percentage). it may be shown in a number of ways including as a graph, a color (i.e, green might mean foil power; red might mean very fatigued and out of energy), or other indices. It may also be communicated to a user in a number of ways including visually (as described above, which also may be integrated into a virtual reality or augmented reality offering and overlaid on top of an athlete or team), verbally (e.g., a virtual assistant providing audio related to the information and whether or not to place a bet), or physically (e.g., a user
may have a smart watch that provides a notification and vibrates when the user receives the notification related to the data).'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify the targeted individual 16 and predict at least a medical condition of the targeted individual 16; identification output of multiple assets can be provided in two or more ways).
Regarding Claim 4, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 2 wherein the two or more unique assets identify the targeted subject and one or more medical conditions, biological responses, or a combination thereof (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more
similar events-for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or
artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the
target subject; [00112], 'the Output information may not be exhibited as a number (e.g. percentage). it may be shown in a number of ways including as a graph, a color (i.e, green might mean foil power; red might mean very fatigued and out of energy), or other indices. It may also be communicated to a user in a number of ways including visually (as described above, which also may be integrated into a virtual reality or augmented reality offering and overlaid on top of an athlete or team), verbally (e.g., a virtual assistant providing audio related to the information and whether or not to place a bet), or physically (e.g., a user may have a smart watch that provides a notification and
vibrates when the user receives the notification related to the data).'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify the targeted individual 16 and predict at least a medical condition of the targeted individual 16; identification output of multiple assets can be provided in two or more ways).
Regarding Claim 5, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein an identification is characterized by at least one of a percentage match, possibility, probability, prediction, confidence indicator, score, or likelihood ([0036], The term "predictive indicator" refers to a metric or other indicator (e.g,, one or more colors, codes, numbers, values, graphs, charts, plots, readings, numerical representations, descriptions, text, physical responses, auditory responses, visual responses, kinesthetic responses) from which one of more forecasts, predictions, probabilities, possibilities, or recommendations related to one or more outcomes tor one or more future events that includes one or more targeted individuals, or one or more groups of targeted individuals, can be calculated, computed, derived, extracted, extrapolated, simulated, created, modified, enhanced, estimated, evaluated, inferred, established, determined, deduced, observed, communicated, or actioned upon. In a refinement, a predictive indicator is a calculated computed asset derived from at least a portion of the animal data or its one or more derivatives'; [0081], 'While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized').
Regarding Claim 6, Sports Data Labs '699 discloses the animal data-based identification and recognition system in claim 1 wherein the assumed subject is a known or presumed subject (Fig. 1; [0038], 'With reference to Figure 1, a schematic of a system for providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted electronically'; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number)'; the targeted individual may be assumed).
Regarding Claim 7, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein upon identification of the targeted subject, the targeted medical condition, or the targeted biological response by the one or more computing devices (22), the one or more computing devices make one or more verifications (Fig. 1; [0038], "Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive
indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the
one or more source sensors'; [0081], These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified'; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data).
Regarding Claim 8, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) verify an identity of the targeted individual (16), the targeted medical condition, or the targeted biological response (Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual"; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual,
multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual
from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of
the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified"; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data).
Regarding Claim 9, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) verify an association between the targeted individual (16) and the one or more source sensors (18; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data from the given source sensors 18).
Regarding Claim 10, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) verify that the one or more source sensors (18) are collecting data from the targeted individual (16; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g,, name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g,, name, weight, height, corresponding identification or reference number) While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data; the presence of data also verifies the data collection).
Regarding Claim 11, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) verify the one or more tags associated with the targeted individual, the one or more source sensors, the animal data, one or more medical conditions, one or more biological responses, or a combination thereof ([0079], 'computing subsystem 22 synchronizes, time-stamps, and tags the animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g. name, age, weight, height, activity, and/or associated groups) and
the one or more source sensors, which includes at least one characteristic of the one or more source sensors. The at least one characteristic includes at least the sensor type, one or more sensor settings, sensor brand, sensor model, sensor firmware, and the like. In a refinement, the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors'; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data).
Regarding Claim 12, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) verify an association between the targeted individual (16) and the animal data (14) from the one or more source sensors (18; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual,
multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078]. computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual
from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of
the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data from the given source sensors 18).
Regarding Claim 13, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 12 wherein upon verification, at least a portion of the animal data (14) from the verified subject (16) is distributed by the one or more computing devices (22) to one or more other computing devices for consideration (Fig. 1; [0041], 'computing subsystem 22 communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localized or networked server/storage, localized storage device, distributed network of computing devices)'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'the computing subsystem is operable to record one or more characteristics of the animal data provided as part of its one or more distributions'; [0097], In another refinement, the one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in creating, collecting, modifying, enhancing, analyzing, offering, distributing. and or productizing the animal data').
Regarding Claim 15, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein a plurality of verifications occur based upon new animal data (14) entering the animal data-based identification and recognition system (10) via the one or more computing devices (22; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0066], 'For example, computing subsystem 22 may be operable to dynamically create, enhance, or modify at least one oil a wagering market or odds, a product that is acquired or consumed, art evaluation or calculation of a probability, a strategy, a prediction, a recommendation, or an action to mitigate or prevent risk based upon at least a portion of the one or more outputs from computing subsystem 22. Such creations, enhancements, or modification may result from one or more direct or indirect observations of user engagement with data collected by computing subsystem 22, or as new data is collected by the system'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more
source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data; new data may be verified as it is collected by the system 10).
Regarding Claim 16, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 7 wherein the one or more computing devices (22) generate one or more alerts based upon one or more identifications or verifications ([0090], 'In a refinement, the speculation system may be programmed to provide one or more alerts based on one or more readings related to the predictive indicator, computed asset, animal data, and/or its one or more derivatives. For example, alerts based on a subject achieving a maximum heart rate or reaching a pre-defined "energy level" that warrants an. alert, or the system detecting an irregularity in ECG data, in this example, detection of such anomalies from a subject can occur utilizing historical BCG information gathered from the subject by the system, as well as one or more subjects that share one or more characteristics with the subject (e.g., age, weight, height, medical conditions, and the like)'; [00136], 'Advantageously, the system may be programmed to identify one or more critical alerts 160 that requires attention from the one or more subject and/or the one or more users of the system (e.g., a medical professional utilizing the system and monitoring a targeted subject) based on the one or more outputs. The one or more critical alerts may be set with a predefined threshold by the individual or administrator (e g.. if the likelihood of something occurring is greater than n%, it is communicated as a critical alert) to alert one or more users of a potential issue related to one or more signals or readings. Characteristically, the system may be set up to utilize one or more artificial intelligence techniques to correlate data sets to identify known biological-related issues from one or more targeted individuals or groups of targeted individuals, as well as identify hidden patterns within the one or more data sets to identity biological-related issues based upon the collected data'; alerts can be generated based upon identifying or verifying characteristics of the targeted individual).
Regarding Claim 17, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein upon identification of the targeted subject (16), the targeted medical condition, or the targeted biological response by the animal data-based identification and recognition system (10), the animal data-based identification and recognition system (10) associates at least a portion of
the animal data (14) derived from the one or more source sensors (18), or the one or more derivatives thereof, with the targeted subject (16), the targeted medical condition, or the targeted biological response ([0079], 'computing subsystem 22 synchronizes, time-stamps, and tags the animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g, name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors. The at least one characteristic includes at least the sensor type, one or more sensor settings, sensor brand, sensor model, sensor firmware, and the like. In a refinement, the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors'; [0083], 'In some situations, computing
subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance
company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data).
Regarding Claim 18, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein upon identification of the targeted subject (16), the targeted medical condition, or the targeted biological response by the one or more computing devices (22), the one or more computing devices create, modify, assign, or a combination thereof, one or more tags ([0079], 'computing subsystem 22 synchronizes, time-stamps, and tags the animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g, name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors. The at least one characteristic includes at least the sensor type, one or more sensor settings, sensor brand, sensor model, sensor firmware, and the like. In a refinement, the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system
28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data). Regarding Claim 19, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the
comparison between the at least one created, modified, or the enhanced unique asset and the animal data (14) derived from the one or more source sensors (18), or one or more derivatives thereof, verifies an origin of the animal data (14) derived from the one or more source sensors (18), or one or more derivatives thereof (Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the
like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags le animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These
characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data from the given source sensors 18, thereby also determining which sensor [origin] each bit of information is obtained from).
Regarding Claim 20, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 19 wherein the origin is the targeted subject (16; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label il is merely an integer label from 1 to
imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], "computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated
groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g,, name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be
anonymized or de-identified"; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data from the given source sensors 18, thereby also determining which sensor [origin] each bit of information is obtained from; this means a targeted individual 16 and a particular sensor 18 both are the origin of the sensed data, i.e. a sensed heart rate on the individual).
Regarding Claim 21, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 19 wherein the origin is the one or more source sensors (18; Fig. 1; [0038], 'Characteristically, source 12 of animal data includes one or more sensors 18. Targeted individual or subject 16 is the subject from which corresponding animal data 14 is collected, Label i is merely an integer label from 1 to imax associated with each targeted individual'; [0072], 'one or more predictive indicators can be derived front or related to a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, and/or multiple targeted groups comprised of multiple targeted individuals. This includes being applicable to, associated with, assigned to, and the like'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags ie animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated
groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be
anonymized or de-identified; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data from the given source sensors 18, thereby also determining which sensor [origin] each bit of information is obtained from; this means a targeted individual 16 and a particular sensor 18 both are the origin of the sensed data, i.e. a sensed heart rate on the individual).
Regarding Claim 22, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the animal data is human data ([0038], 'the subject is a human (e.g., an athlete:, a soldier, a hospital patient or remote telehealth patient, a participant in a fitness class, a video gamer) and the animal data is human data.').
Regarding Claim 23, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset includes at least a portion of animal data ([0035], 'The term" "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights. graphs, charts, or plots that are derived from at least a portion of the animal data
or its one or more derivatives'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets').
Regarding Claim 24, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 23 wherein the at least one unique asset includes at least a portion of non-animal data ([0035], 'In a refinement, a computed asset can include one of more signals or readings from one or more non- animal data sources as one or more input in its one or more computations or calculations').
Regarding Claim 25, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset is one or more digital signatures, identifiers, patterns, trends, features, measurements, outliers, abnormalities, anomalies, characteristics, computed assets, insights, predictive indicators, or a combination thereof ([0035], 'The "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives').
Regarding Claim 26, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset uses animal data derived from two or more source sensors (18) to create, modify, or enhance the at least one unique asset ([0035], The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or
more electronic signals or its one or more derivatives. The computed asset describes 0 quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g,, heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; two or more sensors may be used).
Regarding Claim 27, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset uses two or more types of animal data to create, modify, or enhance the at least one unique asset ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots
that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes o quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g., heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; two or more sensors of different types may be used).
Regarding Claim 28, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 27 wherein the at least one unique asset includes at least a portion of non-animal data ([0035], 'In a refinement, a computed asset can include one of more signals or readings from one or more non- animal data sources as one or more input in its one or more computations or calculations').
Regarding Claim 29, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 27 wherein the at least one unique asset uses two or more types of animal data derived from the same source sensor (18) to create, modify, or enhance the at least one unique asset ([0038], The animal data can be obtained from a single source sensor on each targeted individual, or from multiple source sensors on each targeted individual. In some eases, a single source sensor can capture data from multiple individuals, a targeted group of multiple individuals, or multiple targeted groups of multiple individuals (e.g., an optical-based camera sensor that can locate and measure distance run for a target group of individuals). Each source sensor can provide a single type of animal data or multiple types of animal data. In a refinement, the one or more source sensors consist of at least one biosensor').
Regarding Claim 30, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 27 wherein the at least one unique asset uses two or more types of animal data derived from two or more source sensors to create, modify, or enhance the at least one unique asset ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes o quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g,, heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; two or more sensors of different types may be used).
Regarding Claim 31, Sports Data Labs '699 discloses the animal data-based identification and recognition system in claim 1 wherein the one or more computing devices (22) dynamically create, modify, or enhance the at least one unique asset based upon the animal data (14) collected by the one or more source sensors (18; Fig. 1; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24. Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data. In a refinement, computing subsystem 22 is operable to receive a single type of animal data (e.g., heart rate data) and/or multiple types'; [0066], 'For example, computing subsystem 22 may be operable to dynamically create, enhance, or modify at least one oil a wagering market or odds, a product
that is acquired or consumed, art evaluation or calculation of a probability, a strategy, a prediction, a recommendation, or an action to mitigate or prevent risk based upon at least a portion of the one or more outputs from computing subsystem 22').
Regarding Claim 32, Sports Data Labs '699 discloses the animal data-based identification and recognition system in claim 1 wherein the one or more computing devices (22) dynamically create, modify, or enhance the at least one unique asset based upon new reference animal data collected by the animal data-based identification and recognition system (10; Fig. 1; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24 Computing subsystem 22 is operable to receive
the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data. In a refinement, computing subsystem 22 is operable to receive a single type of animal data (e.g., heart rate data) and/or multiple types'; [0066], For example, computing subsystem 22 may be operable to dynamically create, enhance, or modify at least one oil a wagering market or odds, a product that is acquired or consumed, art evaluation or calculation of a probability, a strategy, a prediction, a recommendation, or an action to mitigate or prevent risk based upon at least a portion of the one or more outputs from
computing subsystem 22. Such creations, enhancements, or modification may result from one or more direct or indirect observations of user engagement with data collected by computing subsystem 22, or as new data is collected by the system').
Regarding Claim 33, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein at least one of the one or more source sensors (18) is a biosensor that gathers physiological, biometric, chemical, biomechanical, location, environmental, genetic, genomic, or other biological data from one or more targeted individuals (Fig. 1; [0039], 'Biosensors collect biosignals which in the context of the present embodiment are any signals or properties in, or derived from, animals that can be continually or intermittently measured, monitored, observed, calculated, computed, inputted, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information, A biological sensor can gather biological data (e.g., including reading and signals) such as physiological, biometric, chemical, biomechanical, genetic, genomic, location or other biological data from one or more targeted individuals').
Regarding Claim 34, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 33 wherein the one or more source sensors include one or more biosensors that gather, or provide information that can be converted into, an animal data type selected from the group consisting of facial recognition data, eye tracking & recognition data, blood flow data, blood volume data,
blood pressure data, biological fluid data, body composition data, biochemical data, pulse data, oxygenation data, core body temperature data, skin temperature data, galvanic skin response data, perspiration data, location data, positional data, audio data, biomechanical data, hydration data, heart-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic
data, ear acoustic authentication data, finger vein recognition data, fingerprint recognition data, footprint and foot dynamics data, hand geometry data, body odor recognition data, palm print recognition data, palm vein recognition data, skin reflection data, thermography recognition data, keystroke dynamics data, signature recognition data, speaker recognition data, voice recognition data, gait recognition data, lip motion data, or a combination thereof ([0039], 'A biological sensor can gather biological data (e.g., including reading and signals) such as physiological, biometric, chemical, biomechanical, genetic, genomic, location or other biological data from one or
more targeted individuals. For example, some biosensors may pleasure, or provide information that can be converted into or derived from, biological: data such as eye tracking data (e.g, pupillary response, movement, EOG-related data), blood flow/ volume data (e.g., PPG data, pulse transit time, pulse arrival time), biological fluid data (e.g., analysis derived from blood, urine, saliva, sweat, cerebrospinal fluid), body composition data (e.g., BMI percent body fat, protein/muscle), biochemical composition data, biochemical structure data, pulse data, oxygenation data (e g, SpO2), core body temperature data, skin temperature data, galvanic skin response data, perspiration data (e,g., rate, composition),, blood pressure data (e.g,, systolic, diastolic, MAP), hydration data (e.g. fluid balance I/O), heart-based data (e.g. heart rate, average HR, HR range, heart rate variability, HKV time domain, HRV frequency domain, autonomic tone, BCG-related data including PR, QRS, QT, RR intervals), neurological-related data (e.g., EEG-related data), genetic-related data, genomic-related data, skeletal data, muscle data (e.g., EMG-related data including surface EMG, amplitude), respiratory data (e.g., respiratory rate, respiratory pattern, inspiration/expiration ratio, tidal volume, spirometry data), thoracic electrical bioimpedance data, or a combination thereof").
Regarding Claim 35, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 34 wherein the at least one unique asset is derived from at least a portion of animal data gathered from the one or more biosensors (Fig. 1; [0039], 'Biosensors collect biosignals which in the context of the present embodiment are any signals or properties in, or derived from, animals that
can be continually or intermittently measured, monitored, observed, calculated, computed, inputted, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information, A biological sensor can gather biological data (e.g., including reading and signals) such as physiological, biometric, chemical, biomechanical, genetic, genomic, location or other biological data from one or more targeted individuals').
Regarding Claim 36, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 34 wherein the at least one unique asset is derived from two or more types of animal data gathered from the one or more biosensors (Fig. 1; [0038], 'The animal data can be obtained from a single source sensor on each targeted individual, or from multiple source sensors on
each targeted individual. In some eases, a single source sensor can capture data from multiple individuals, a targeted group of multiple individuals, or multiple targeted groups of multiple individuals (e.g., an optical-based camera sensor that can locate and measure distance run for a target group of individuals). Each source sensor can provide a single type of animal data or multiple types of animal data. In a refinement, the one or more source sensors consist of at least one biosensor'; [0039], 'Biosensors collect biosignals which in the context of the present embodiment are any signals or properties in, or derived from, animals that can be continually or intermittently measured,
monitored, observed, calculated, computed, inputted, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information, A biological sensor can gather biological data (e.g., Including reading and signals) such as physiological, biometric, chemical, biomechanical, genetic, genomic, location or other biological data from one or more targeted individuals').
Regarding Claim 37, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 36 wherein the at least one unique asset is derived from animal data gathered from two or more biosensors (Fig. 1; [0038], 'The animal data can be obtained from a single source sensor on each targeted individual, or from multiple source sensors on each targeted individual. In some eases, a single source sensor can capture data from multiple individuals, a targeted group of multiple individuals, or multiple targeted groups of multiple individuals (e.g., an optical-based camera sensor that can locate and measure distance run for a target group of individuals). Each source sensor can provide a single type of animal data or multiple types of animal data. In a refinement, the one or more source sensors consist of at least one biosensor; [0039], 'Biosensors collect biosignals which in the context of the present embodiment are any signals or properties in, or derived from, animals that can be continually or intermittently measured, monitored, observed, calculated, computed, inputted, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information, A biological sensor can gather biological data (e.g., including reading and signals) such as physiological, biometric, chemical, biomechanical, genetic, genomic, location or other biological data from one or more targeted individuals'; two or more sensors of different types may be used).
Regarding Claim 38, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 36 wherein the at least one unique asset includes at least a portion of non-animal data ([0035], 'In a refinement, a computed asset can include one of more signals or readings from one or more animal data sources as one or more input in its one or more computations or calculations').
Regarding Claim 39, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 36 wherein the at least one unique asset incorporates at least one of or any combination of: name, age, weight, height, eye color, skin color, hair color, birthdate, race, reference identification, country of origin, area of origin, ethnicity, current residence, addresses, phone number, gender, data quality assessment, information gathered from medication history, medical history, medical records, medical conditions, traits, health risks, inherited conditions, drug responses, DNA sequences, protein sequences and structures, drug/prescription records, allergies, family history, health history, blood analysis, physical shape, manually-inputted personal data, historical personal data, the one or more activities the targeted individual is engaged in while the animal data is collected, ambient temperature data related to the animal data, humidity data related to the animal data, barometric pressure data related to the animal data, elevation data related to the animal data, one or more
associated groups, one or more nutritional habits, one or more activity habits, one or more health habits, one or more social habits, education records, criminal records, financial information, social data, employment history, marital history, relatives or kin history, relatives or kin medical history, relatives or kin health history, manually inputted personal data, historical personal data, or individual-generated data ([0081], "These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height, corresponding identification or reference number)').
Regarding Claim 40, Sports Data Labs '699 discloses the animal data-based identification and recognition system in claim 33 wherein the biosensor is affixed to, are in contact with, or send one or more electronic communications in relation to or derived from, one or more targeted individuals including one or more of a targeted subject's body, eyeball, vital organ, muscle, hair, veins, biological fluid, blood vessels, tissue, or skeletal system, embedded in the one or more targeted individuals, lodged or implanted in one or more targeted individuals, ingested by the one or more targeted individuals, integrated to comprise at least a portion of the one or more targeted individuals, or integrated into or as part of, affixed to, or embedded within, a fabric, textile, cloth, material, fixture, object, or apparatus that contacts or is in communication with one or more targeted individuals, either directly or via one or more intermediaries ([0039], 'Biosensors collect biosignals which in the context of the present embodiment are any signals or properties in, or derived from, animals that can be continually or intermittently measured, monitored, observed, calculated, computed, inputted, or interpreted, including both electrical and non-electrical signals, measurements, and artificially-generated information'; [0040], 'The at least one sensor 18 and/or its one or more
appendices can be affixed to, in contact with, or send one or more electronic communications in relation to or derived from, the subject including a subject's skin, eyeball, vital organ, muscle, hair, veins, biological fluid, blood vessels, tissue, or skeletal system, embedded in a subject, lodged or implanted in a subject, ingested by a subject, or integrated to comprise at least a portion of a subject...the one or more sensors 18 is integrated into of as paid of, affixed to or embedded within, a textile, fabric, cloth, material, fixture, object, or apparatus that contacts or is in communication with a targeted individual either directly or via one or more intermediaries or interstitial items').
Regarding Claim 41, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein at least one sensor of the one or more source sensors (18) captures two or more types of animal data ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes 0 quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram
readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e.g., heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; two or more sensors of different types may be used).
Regarding Claim 42, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein at least one sensor of the one or more source sensors (18) is comprised of two or more sensors ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a
portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes 0 quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g,, heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can he derived fro temperature sensors'; two or more sensors may be used).
Regarding Claim 43, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein at least a portion of the animal data (14) from an identified targeted subject or one or more derivatives thereof is distributed by the one or more computing devices (22) to one or more other computing devices for consideration (Fig. 1; [0041], 'computing subsystem 22
communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localised or networked server/storage, localized storage device, distributed network of computing devices)'; [0081], 'the computing subsystem is operable to record one or more characteristics of the animal data provided as part of its one or more distributions'; [0097], 'In another refinement, the one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in creating, collecting, modifying, enhancing, analyzing, offering, distributing, and or productizing the animal data').
Regarding Claim 44, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein at least a portion of the animal data (14) is distributed to one or more computing devices (22) for consideration (Fig. 1; [0041], 'computing subsystem 22 communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localised or networked server/storage, localized storage device, distributed network of computing devices)'; [0081], the computing subsystem is operable to record one or more characteristics of the animal data provided as part of its one or more distributions'; [0097], 'In another refinement, the
one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in creating, collecting, modifying, enhancing, analyzing, offering, distributing, and or productizing the animal data').
Regarding Claim 45, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 44 wherein the animal data (14) is distributed as part of an animal data consideration system (Fig. 1; [0041], 'computing subsystem 22 communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localised or networked server/storage, localized storage device,
distributed network of computing devices): [0081], 'the computing subsystem is operable to record one or more characteristics of the animal data provided as part of its one or more distributions'; [0097], 'In another refinement, the one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in creating, collecting, modifying, enhancing, analyzing, offering, distributing, and or productizing the animal data').
Regarding Claim 46, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the animal data includes metadata that incorporates one or more attributes related to targeted individual ([0072], 'For example, the one or more predictive indicators can be attributed to a targeted individual'; [0079], 'the animal data include metadata that identifies one or more
characteristics of the animal data and the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals').
Regarding Claim 47, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the reference animal data includes previously collected animal data ([0087], 'simulated data that incorporates at least a portion of animal data may be utilized to create one or more prop bets for a simulated event. For example, if a system has previously collected Team A 's heart
rate vs Team B, the system could create one or more new bets that utilize previously collected data incorporated as part of one or more simulations').
Regarding Claim 48, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 47 wherein at least a portion of previously collected animal data is derived from one or more sensors ([0035], 'The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or
its one or more derivatives. The computed asset describes 0 quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g,, heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from
temperature sensors'; [0087], 'simulated data that incorporates at least a portion of animal data may be utilized to create one or more prop bets for a simulated event. For example, if a system has previously collected Team heart rate vs Team B, the system could create one or more new bets that utilize previously collected data incorporated as part of one or more simulations'; the heart rate sensors collect the data).
Regarding Claim 49, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 47 wherein the reference animal data includes at least a portion of non-animal data ([0035], 'In a refinement, a computed asset can include one of more signals or readings from one or more non- animal data sources as one or more input in its one or more computations or calculations';
[0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any
given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived
data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historical data can include non-animal data).
Regarding Claim 50, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the reference animal data includes animal data (14) that is derived directly from the targeted individual (16), indirectly from the targeted individual, or a combination thereof (Fig. 1; [0041], 'Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data'; [0083], "In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history,
prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time': historic data [reference data] is used for at least a targeted subject and medical condition).
Regarding Claim 51, Sports Data Labs "699 discloses the animal data-based identification and recognition system of claim 1 wherein the reference animal data includes data that is not derived directly or indirectly from the targeted individual but shares at least one attribute with the targeted individual, medical condition, or biological response ([0090], 'in this example, detection of such anomalies from a subject can occur utilizing historical BCG information gathered from the subject by the system, as well as one or more subjects that share one or more characteristics with the subject (e.g., age, weight, height, medical conditions, and the like)'; historical data [reference] can be created from different subjects than the targeted individual 16, but share attributes like an age, weight and/or height).
Regarding Claim 52, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 51 wherein the at least one attribute includes at least one of or any combination of: name, age, weight, height, eye color, hair color, skin color, birthdate, race, reference identification, country of origin, area of origin, ethnicity, current residence, addresses, phone number, gender, data quality
assessment, information gathered from medication history, medical history, medical records, medical conditions, traits, health risks, inherited conditions, drug responses, DNA sequences, protein sequences and structures, drug/prescription records, allergies, family history, health history, blood analysis, physical shape, manually-inputted personal data, historical personal data, activities, ambient temperature data related to the animal data, humidity data related to the animal data, barometric pressure data related to the animal data, elevation data related to the animal data, one or more associated groups, one or more nutritional habits, one or more activity habits, one or more health habits, one or more social habits, education records, criminal records, financial information, social data, employment history, marital history, relatives or kin history, relatives or kin medical history, relatives or kin health history, manually inputted personal data,
historical personal data, or individual-generated data ([0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g., name, weight, height,
corresponding identification or reference number)').
Regarding Claim 53, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein creation, modification, or enhancement of the at least one unique asset occurs utilizing at least a portion of artificial data ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more
derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision
data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator').
Regarding Claim 54, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 53 wherein the artificial data is generated utilizing one or more artificial intelligence techniques ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and Can include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer').
Regarding Claim 55, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein creation, modification, or enhancement of the animal data or one or more derivatives thereof utilizes at least a portion of artificial data ([0037], The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more signals or readings fro one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator').
Regarding Claim 56, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 55 wherein the artificial data is generated utilizing one or more artificial intelligence techniques ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and Can include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], The new one or more artificial data sets may be created by application of one or more artificial intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer').
Regarding Claim 57, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset or derivative of the animal data is created, modified, or enhanced utilizing one or more artificial intelligence techniques ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more signals or readings fro one or more non-animal data sources as
one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer').
Regarding Claim 58, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 57 wherein the one or more artificial intelligence techniques includes execution of one or more trained neural networks ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It
can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and can include one or more signals or readings fro one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial intelligence
techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer'; [0089], 'More specifically, one or more neural networks may be trained with one or more of these data sets to understand biological functions of Athlete A and how one or more variables can affect any given biological function, The neural network can be further trained to understand what outcome (or outcomes) occurred based on the one or more biological functions and the impact of the one or more variables, enabling correlative and causative analysis').
Regarding Claim 59, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 58 wherein the one or more trained neural networks utilized to generate the at least one unique asset consists of one or more of the following types of neural networks: Feedforward, Perceptron, Deep Feedforward, Radial Basis Network, Gated Recurrent Unit, Autoencoder (AE), Variational AE, Denoising AE, Sparse AE, Markov Chain, Hopfield Network, Boltzmann Machine, Restricted BM, Deep Belief Network, Deep Convolutional Network, Deconvolutional Network, Deep Convolutional Inverse Graphics Network, Liquid State Machine, Extreme Learning Machine, Echo State Network, Deep Residual Network, Kohenen Network, Support Vector Machine, Neural Turing Machine, Group Method of Data Handling, Probabilistic, Time delay, Convolutional, Deep Stacking Network, General Regression Neural Network, Self-Organizing Map, Learning Vector Quantization, Simple Recurrent, Reservoir Computing, Echo State, Bi-Directional, Hierarchal, Stochastic, Genetic Scale, Modular, Committee of Machines, Associative, Physical, Instantaneously Trained, Spiking, Regulatory Feedback, Neocognitron, Compound Hierarchical-Deep Models, Deep Predictive Coding Network, Multilayer Kernel Machine, Dynamic, Cascading, Neuro-Fuzzy, Compositional Pattern-Producing, Memory Networks, One-shot Associative Memory, Hierarchical Temporal Memory, Holographic Associative Memory, Semantic Hashing, Pointer Networks, Encoder-Decoder Network, Recurrent Neural Network, Long Short-Term Memory Recurrent Neural Network, or Generative Adversarial Network ([0089], 'More specifically, one or more neural networks may be trained with one or more of these data sets to understand biological functions of Athlete A and how one or more variables can affect any given biological function, The neural network can be further trained to understand what outcome (or outcomes) occurred
based on the one or more biological functions and the impact of the one or more variables, enabling correlative and causative analysis'; [0091], 'For example, if a computing system utilizes a statistical model or a neural network like Long Short-Terra.
Regarding Claim 60, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein gathered animal data (14) from the one or more source sensors (18) or one or more derivatives thereof are compared against the at least one unique asset by the one or more computing devices (22) when executing one or more artificial intelligence techniques to identify the targeted subject, a medical condition, or a biological response ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more
simulations utilizing one or more artificial intelligence techniques or statistical models, and include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g. artificially-created vision data, artificially-created movement data)'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In
another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific
medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted
individual 16).
Regarding Claim 61, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the comparison between the at least one unique asset and gathered animal data or one or more derivatives thereof occurs once, intermittently, or regularly to verify the targeted individual (16), the targeted medical condition, or the targeted biological response (Fig. 1; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24 Computing subsystem 22 is
operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data. In a refinement, computing subsystem 22 is operable to receive a single type of animal data (e.g., heart rate data) and/or multiple types'; [0066], 'For example, computing subsystem 22 may be operable to dynamically create, enhance, or modify at least one oil a wagering market or odds, a product that is acquired or consumed, art evaluation or calculation of a probability, a strategy, a prediction, a recommendation, or an action to mitigate or prevent risk based upon at least a portion of the one or
more outputs from computing subsystem 22. Such creations, enhancements, or modification may result from one or more direct or indirect observations of user engagement with data collected by computing subsystem 22, or as new data is collected by the system'; [0078], 'computing subsystem 22 synchronizes, time-stamps, and tags i.e. animal data with information: (e.g., characteristics) related to the one or
more targeted individual from which the animal data is collected (e.g., name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least one characteristic of the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals (e.g,, name, weight, height, corresponding identification or reference number). While the animal data is oftentimes associated with an identifiable one or more targeted individuals or groups of targeted individuals, it should be appreciated that one or more inputs or outputs associated with the animal data and its derivatives (which can include computed assets and predictive indicators) can be anonymized or de-identified"; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data at least once, and possibly as new data is retrieved).
Regarding Claim 62, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the comparison between the at least one unique asset and gathered animal data (14) or one or more derivatives thereof identifies multiple medical conditions or biological responses (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals"; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a
refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0090], 'a remote patient monitoring or; telehealth platform may want to provide both the medical professional (e.g., doctor) and patient: with the
likelihood of patient experiencing any future medical condition (e,g. flu. heart attack, diabetes, stroke) based upon their one of more real-time vitals provide to the application via one or more source sensors'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict
multiple medical conditions of the targeted individual 16).
Regarding Claim 63, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the comparison between the at least one unique asset and gathered animal data (14) or one or more derivatives thereof identifies multiple targeted subjects (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one
or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or
artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the
target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of multiple targeted individual 16).
Regarding Claim 64, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset is created, modified, or enhanced from two or more types of animal data (14) that are captured across one or more time periods and one or more activities ([0082], 'In another refinement, computing subsystem 22 and/or the wagering system and/or the probability assessment system are operable to allow a user to select at least one characteristic upon which the predictive indicator, computed asset, animal data and/or its one or more derivatives arc provided. Moreover, computing subsystem 22 can be operable to allow
users to select one or more parameters such as latency (e,g., real-time or near real-time VS not) and time period that enable a user to maximize the value of any given data for their specific use case A characteristic may include the one or more sources of the animal data, specific personal attributes of the one or more individuals or groups of individuals, type of sensor used, sensor properties, classifications, specific sensor configurations, location, activity').
Regarding Claim 65, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 64 wherein the at least one unique asset is created, modified, or enhanced using two or more types of animal data, collected across two or more time periods, collected when the targeted subject is engaged in one or more activities, or a combination thereof ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes o quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g., heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; [0082], 'In another refinement, computing subsystem 22 and/or the wagering system and/or the probability assessment system are operable to allow a user to select at least one characteristic upon which the predictive indicator, computed asset, animal data and/or its one or more derivatives arc provided. Moreover, computing subsystem 22 can be operable to allow users to select one or more parameters such as latency (e,g., real-time or near real-time vs not) and time period that enable a user to maximize the value of any given data for their specific use case. A characteristic may include the one or more sources of the animal data,
specific personal attributes of the one or more individuals or groups of individuals, type of sensor used, sensor properties, classifications, specific sensor configurations, location, activity'; two or more sensors of different types sensing during an activity at multiple times may be used).
Regarding Claim 66, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1, wherein the at least one unique asset is created, modified, or enhanced using one or more artificial intelligence techniques that produce one or more biological representations of the targeted individual (16) to understand one or more biological functions or processes of the targeted individual (16) based upon their animal data (14) to create, modify, or enhance the at least one unique asset ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial
intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer'; [0089], 'More specifically, one or more neural networks may be trained with one or more of these data sets to understand biological functions of Athlete A and how one or more variables can affect any given biological function, The neural network can be further trained to understand what outcome (or outcomes) occurred based on the one or more biological functions and the impact of the one or more variables, enabling correlative and causative analysis').
Regarding Claim 67, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein a biological response is an activity, biological state, or medical event ([0071], "In a healthcare scenario, a user (e.g., a patient) may accept to pa for the cost of a medication or prescription written by a medical professional (e.g., doctor), with the prescription or medication being
prescribed based upon the predictive indicator (e.g., the predictive indicator may indicate that there may be a n percent chance of the patient experiencing a medical condition; therefore, the doctor prescribes pill X to reduce the likelihood of the medical event based upon the indicator)').
Regarding Claim 63, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the comparison between the at least one unique asset and gathered animal data (14) or one or more derivatives thereof identifies multiple targeted subjects (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one
or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or
artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the
target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of multiple targeted individual 16).
Regarding Claim 64, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset is created, modified, or enhanced from two or more types of animal data (14) that are captured across one or more time periods and one or more activities ([0082], 'In another refinement, computing subsystem 22 and/or the wagering system and/or the probability assessment system are operable to allow a user to select at least one characteristic upon which the predictive indicator, computed asset, animal data and/or its one or more derivatives arc provided. Moreover, computing subsystem 22 can be operable to allow
users to select one or more parameters such as latency (e,g., real-time or near real-time VS not) and time period that enable a user to maximize the value of any given data for their specific use case A characteristic may include the one or more sources of the animal data, specific personal attributes of the one or more individuals or groups of individuals, type of sensor used, sensor properties, classifications, specific sensor configurations, location, activity').
Regarding Claim 65, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 64 wherein the at least one unique asset is created, modified, or enhanced using two or more types of animal data, collected across two or more time periods, collected when the targeted subject is engaged in one or more activities, or a combination thereof ([0035], 'The term "computed asset" refers to one or more numbers, a plurality of numbers, values, metrics, readings, insights, graphs, charts, or plots that are derived from at least a portion of the animal data or its one or more derivatives. The one or more sensors used herein initially provide
an electronic signal. The computed asset is extracted or derived, at least in part, from the one or more electronic signals or its one or more derivatives. The computed asset describes o quantities an interpretable property of the one or more targeted individuals. For example, electrocardiogram readings can be derived from analog front end signals (the electronic signal from the sensor), heart rate data (e,g., heart rate beats per minute) can he derived from electrocardiogram or PPG season», body temperature data can be derived from temperature sensors'; [0082], 'In another refinement, computing subsystem 22 and/or the wagering system and/or the probability assessment system are operable to allow a user to select at least one characteristic upon which the predictive indicator, computed asset, animal data and/or its one or more derivatives arc provided. Moreover, computing subsystem 22 can be operable to allow users to select one or more parameters such as latency (e,g., real-time or near real-time vs not) and time period that enable a user to maximize the value of any given data for their specific use case. A characteristic may include the one or more sources of the animal data,
specific personal attributes of the one or more individuals or groups of individuals, type of sensor used, sensor properties, classifications, specific sensor configurations, location, activity'; two or more sensors of different types sensing during an activity at multiple times may be used).
Regarding Claim 66, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the at least one unique asset is created, modified, or enhanced using one or more artificial intelligence techniques that produce one or more biological representations of the targeted individual (16) to understand one or more biological functions or processes of the targeted individual (16) based upon their animal data (14) to create, modify, or enhance the at least one unique asset ([0037], 'The term "artificial data" refers to artificially-created data that is derived from or generated using, at least in part, real animal data or its one or more derivatives. It can be created by running one or more simulations utilizing one or more artificial intelligence techniques or statistical models, and include one or more signals or readings from one or more non-animal data sources as one or more inputs. Artificial data also includes any artificially-created data that shares at least one biological function with a human or other animal (e g, artificially-created vision data, artificially-created movement data)'; [0085], 'The artificial data output may be, for example, artificial animal data, a computed asset, and/or a predictive indicator'; [0088], 'The new one or more artificial data sets may be created by application of one or more artificial
intelligence techniques that can analyze one or more previously captured data sets that match at least one of the characteristics required by the acquirer'; [0089], 'More specifically, one or more neural networks may be trained with one or more of these data sets to understand biological functions of Athlete A and how one or more variables can affect any given biological function, The neural network can be further trained to understand what outcome (or outcomes) occurred based on the one or more biological functions and the impact of the one or more variables, enabling correlative and causative analysis').
Regarding Claim 67, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein a biological response is an activity, biological state, or medical event ([0071], "In a healthcare scenario, a user (e.g., a patient) may accept to pa for the cost of a medication or prescription written by a medical professional (e.g, doctor), with the prescription or medication being
prescribed based upon the predictive indicator (e.g., the predictive indicator may indicate that there may be a n percent chance of the patient experiencing a medical condition; therefore, the doctor prescribes pill X to reduce the likelihood of the medical event based upon the indicator)').
Regarding Claim 68, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the one or more computing devices (22) create, modify, or enhance the at least one unique asset from animal data (14) that is both reference animal data and animal data gathered by the one or more source sensors (18) from the targeted subject (16; Fig. 1: [0042], 'Still referring
to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance
company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16; historical data can also be sensor data).
Regarding Claim 69, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein two or more unique assets are created that enable one or more targeted individuals, medical conditions, or biological responses to be identified in two or more ways (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history,
prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create
one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; [00112], 'the Output information may not be exhibited as a number (e.g. percentage). it may be shown in a number of ways including as a graph, a color (i.e., green might mean foil power; red might mean very fatigued and out of energy), or other indices. It may also be communicated to a user in a number of ways including visually (as described above, which also may be integrated into a virtual reality or augmented reality offering and overlaid on top of an athlete or team), verbally (e.g., a virtual assistant providing audio related to the information and whether or not to place a bet), or physically (e.g., a user may have a smart watch that provides a notification and vibrates when the user receives the notification related to the data).'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16; identification output of multiple assets can be provided in two or more ways).
Regarding Claim 70, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein once the animal data is verified and included as part of the reference animal data, one or more tags are created related to the targeted subject, medical condition, biological response, or a combination thereof ([0079], 'computing subsystem 22 synchronizes, time-stamps, and tags the animal data with information: (e.g., characteristics) related to the one or more targeted individual from which the animal data is collected (e.g. name, age, weight, height, activity, and/or associated groups) and the one or more source sensors, which includes at least
one characteristic of the one or more source sensors. The at least one characteristic includes at least the sensor type, one or more sensor settings, sensor brand, sensor model, sensor firmware, and the like. In a refinement, the animal data include metadata
that identifies one or more characteristics of the animal data and the one or more source sensors'; the identity of the targeted individual 16 is verified when it is associated and tagged as part of the animal data).
Regarding Claim 71, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 1 wherein the reference animal data is gathered from one or more other external sources ([0083], "computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. For example, if a gym equipment manufacturer wants to create a predictive indicator aimed at predicting fatigue (or the likelihood that fatigue will occur at an give time based on exercise patterns) for users
of their product (e.g., in-home cycling equipment) as part of its platform subscription offering, using historical animal data derive from users in cycling- focused fitness classes may be useful in enabling the manufacturer to create a predictive indicator for any given user in order to predict current or future biological performance while using their equipment'; the data can come from a variety of external sources at
various locations).
Regarding Claim 72, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 71 wherein the reference animal data is gathered from one or more computing devices (22) and has attached metadata that enables the reference animal data to be associated with one or more subjects, medical conditions, biological responses, or a combination thereof ([0072], 'For example,
the one or more predictive indicators can be attributed to a targeted individual'; [0079], 'the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors'; [0081], 'These characteristics of the animal data may include at least one of sources of the animal data, specific personal attributes of the one or more individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event In another
refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics
with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data related to at least the subject includes metadata).
Regarding Claim 73, Sports Data Labs '699 discloses an animal data-based identification and recognition system (system 10; Fig. 1; [0079], 'the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors') comprising:
one or more computing devices (Computing subsystem 22) that gather reference animal data (historical animal data 14) derived from, at least in part, one or more sensors (sensors 18) wherein the one or more computing devices (22) are operable to create, modify, or enhance at least one unique asset from the reference animal data for one or more known subjects (targeted individuals 16) that identify each of the one or more known subjects (Fig. 1; [0038], 'With reference to Figure 1, a schematic of a system for providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted
electronically'; [0041], "Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data'; [0083], 'In some situations,
computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics
with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data [reference data] is used for at least a targeted subject and medical condition);
one or more source sensors (sensors 18) that gather animal data (animal data 14) from a targeted subject (targeted individual 16) wherein the animal data (14) is transmitted electronically (Fig. 1; [0038], "With reference to Figure 1, a schematic of a system for
providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted electronically'; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that
gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through transmission subsystem 24');
a collecting computing device (computing device 26 and/or a computer in the computing subsystem 22) that is configured to (1) gather the animal data (14) from the targeted subject (16) via the one or more source sensors (18), (2) create, modify, or enhance at least one unique asset from at least a portion of the animal data (14) derived from the targeted subject (16) via the one or more source sensors (18) for identifying the targeted subject as a known subject (Fig. 1; [0041], transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For
example, computing device 26 can he a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of
the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset related to the target individual 16, which can occur at the computing device 26 before it is sent to the Computing subsystem 22), and the collecting computing device (26) is configured to either (i) gather the at least one created, modified, or enhanced unique asset derived
from the reference animal data for the one or more known subjects, or (ii) provide the at least one unique asset derived from the targeted subject (16) vía the one or more source sensors (18), at least in part, to the one or more computing devices (22; Fig. 1; [0041], 'transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing device 26 can he a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group
of targeted individuals, although not a requirement for the present invention"; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0084], In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one
or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject: the sensor data is stored and turned into a computed asset, which can occur at the computing device 26 before it is sent to the Computing subsystem 22), wherein; the collecting computing device or the one or more computing devices (22) are configured to perform a comparison by comparing the at least one created, modified, or the enhanced unique asset from the one or more known subjects with the at least one created, modified, or the enhanced unique asset from the targeted subject (16; Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the
insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different
subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16); and the comparison between two or more unique assets enables the collecting computing device or the one or more computing devices (22) to identify the targeted subject as a known subject (Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more
sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time': [0084], 'In particular, computing subsystem 22 or the wagering system
28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or
artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the
target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify the targeted individual 16).
Regarding Claim 74, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 73 wherein the one or more computing devices (22) include the collecting computing device ([0041], 'transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing device 26 can he a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a
requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals In another refinement, transmission subsystem 24 includes direct communication links, Therefore, in this refinement computing subsystem 22 communicates directly with the source of animal data as shown by communication links 34 with sensor 18 or by communication link 36 with computing device 26'; the computing device 26 may be considered to be part of the computer subsystem 22, or the computer subsystem 22 may directly collect the sensor data).
Regarding Claim 75, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 73 wherein the collecting computing device (22) is configured to the reference animal data (Fig. 1; [0041], 'Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g.,
manipulated) animal data, transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing device 26 can be a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals. In another refinement, transmission subsystem 24 includes direct communication links, Therefore, in this refinement computing subsystem 22 communicates directly with the source of animal data as shown by communication links 34 with sensor 18 or by communication link 36 with computing device 26'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event in another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data [reference data] is used for at least a targeted subject and medical condition; the computing device 26 may be considered to be part of the computer subsystem 22, or the computer subsystem 22 may directly collect the sensor data, which can be later used as historic data).
Regarding Claim 76, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 73 wherein at least a portion of the animal data (14) from an identified targeted subject or its one or more derivatives is distributed by one or more computing devices (22) to one or more other computing devices for consideration (Fig. 1; [0041], 'computing subsystem 22 communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localised or networked server/storage, localized storage device, distributed network of computing devices)'; [0081], 'the computing subsystem is operable to record one or more characteristics of
the animal data provided as part of its one or more distributions'; [0097], 'In another refinement, the one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in modifying, enhancing, analyzing, offering, distributing, and or productizing the animal data').
Regarding Claim 77, Sports Data Labs '699 discloses an animal data-based identification and recognition system (system 10; Fig. 1; [0079], 'the animal data include metadata that identifies one or more characteristics of the animal data and the one or more source sensors') comprising:
one or more computing devices (Computing subsystem 22) that gather reference animal data (historical animal data 14) derived from, at least in part, one or more sensors (sensors 18) wherein the one or more computing devices (22) are operable to create, modify, or enhance at least one unique asset for one or more known medical conditions or biological responses from the reference animal data that identify each of the one or more known medical conditions or biological responses (Fig. 1; [0038], 'With reference to Figure 1, a schematic of a system for providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted electronically'; [0041], 'Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g., manipulated) animal data';
[0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived
data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data [reference data] is used for at least a targeted subject and medical condition);
one or more source sensors (sensors 18) that gather animal data (animal data 14) from a targeted subject (targeted individual 16) wherein the animal data (14) is transmitted electronically (Fig. 1; [0038], "With reference to Figure 1, a schematic of a system for providing animal data and predictive indicators thereof is provided. Speculation system 10 include a source 12 of animal data 14 that can be transmitted electronically'; [0041], 'In the variation depicted in Figure 1, each individual 16 has at least one sensor 18 that gathers animal data 14 from the targeted individual 16. Computing subsystem 22 receives and collects the animal data 14 through
transmission subsystem 24');
a collecting computing device (computing device 26 and/or a computer in the computing subsystem 22) configured to (1) gather the animal data (14) from the targeted subject (16) via the one or more source sensors (18), (2) create, modify, or enhance at least one unique asset from at least a portion of the animal data (14) derived from the one or more source sensors (18) for identifying one or more medical
conditions or biological responses associated with the targeted subject (16; Fig. 1; [0041], 'transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing device 26 can he a smartphone or a computer. However, computing device 26 can be any
computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested
parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset related to the target individual 16, which can occur at the computing device 26 before it is sent to the Computing
subsystem 22), and the collecting computing device (26) is further configured to either (i) gather the at least one created, modified, or enhanced unique asset derived from the reference animal data for the one or more known medical conditions or known biological responses, or (ii) provide the at least one unique asset derived from the targeted subject (16) via the one or more source sensors (18), at least in part, to the one or more
computing devices (22; Fig. 1; [0041], transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing
device 26 can he a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0084]. 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject; the sensor data is stored and turned into a computed asset, which can occur at the computing device 26 before it is sent to the Computing subsystem 22), wherein; the collecting computing device or the one or more computing devices (22) are configured to perform a comparison by comparing the at least one created, modified, or the enhanced unique asset for the one or more known medical conditions or biological responses with the at least one unique asset from the targeted subject (16; Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height,
personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, data history) to run one or more simulations in order to determine a likely genomic/genetic history, biological fluid-derived outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more
artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request (which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject'; the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify and predict at least a medical condition of the targeted individual 16); and
the comparison between two or more unique assets enables the collecting computing device or the one or more computing devices (22) to identify one or more of the known medical conditions or biological responses associated with the targeted subject (16; Fig. 1; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; [0084], 'In particular, computing subsystem 22 or the wagering system 28 or the probability assessment system 30 can create one or more artificially-generated animal data sets, computed assets'; [0092], 'The system may identity these requested parameters Within the data sets and across data sets and run one or more simulations to create one or more new artificial data sets that fulfil) the user's request
(which may be, for example, a predictive indicator, computed asset, or artificial animal data) based on these dissimilar sets of data in a variation, the dissimilar sets of data that are used to create or re-create one or more new data sets may feature one or more different subjects that share at least one common characteristic with the target subject: the sensor data is stored and turned into a computed asset that includes historical data; this historical data is compared to the animal data 14 from sensors 18 to identify the targeted individual 16).
Regarding Claim 78, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 77 wherein the one or more computing devices (22) include the collecting computing device ([0041], 'transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing
subsystem 22, For example, computing device 26 can he a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals. In another refinement, transmission subsystem 24 includes direct communication links, Therefore, in this refinement computing subsystem 22 communicates directly with the source of animal data as shown by communication links 34 with sensor 18 or by communication link 36 with computing device 26'; the computing device 26 may be considered to be part of the computer subsystem 22, or the computer subsystem 22 may directly collect the sensor data).
Regarding Claim 79, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 77 wherein the collecting computing device is configured to source the reference animal data (Fig. 1; [0041], "Computing subsystem 22 is operable to receive the animal data or groups of animal data from a single targeted individual or multiple targeted individuals as raw or processed (e.g.,
manipulated) animal data transmission subsystem 24 includes computing device 26 which mediates the sending of animal data 14 to intermediate server.22, i.e. it collects the data and transmits it to computing subsystem 22, For example, computing device 26 can be a smartphone or a computer. However, computing device 26 can be any computing device. Typically, computing device 26 is local to the targeted individual or group of targeted individuals, although not a requirement for the present invention'; [0042], 'Still referring to Figure 1, computing subsystem 22 and/or one or more sensors 18 transform at least a portion of the animal data into at least one computed asset assigned to a selected targeted individual or a group of targeted individuals In another refinement, transmission subsystem 24 includes direct communication links, Therefore, in this refinement computing subsystem 22 communicates directly with the source of animal data as shown by communication links 34 with sensor 18 or by communication link 36 with computing device 26'; [0083], 'In some situations, computing subsystem 22 provides and or uses historical animal data. In a refinement, historical data from one or more similar events for an individual or similar individual(s) may be useful to a user for predicting performance related to any given event. In another refinement, historical data from one or more similar individuals may be useful to users for predicting performance for any given subject. For example, if an insurance company wants to understand the likelihood of any given subject having a specific medical condition (e.g., heart attack) within a predefined period of time, the insurance company may utilize data from individuals that share one or more characteristics with the individual (e.g., age, height, personal history, social habits, blood type, medical history, prescription history, BCG data history, heart rate history, blood pressure history, genomic/genetic history, biological fluid-derived data history) to run one or more simulations in order to determine a likely outcome for whether or not that subject will experience the medical condition within the requisite period of time'; historic data [reference data] is used for at least a targeted subject and medical condition; the computing device 26 may be considered to be part of the computer subsystem 22, or the computer subsystem 22 may directly collect the sensor data, which can be later used as historic data).
Regarding Claim 80, Sports Data Labs '699 discloses the animal data-based identification and recognition system of claim 77 wherein at least a portion of the animal data (14) or its one or more derivatives from the identified one or more medical conditions or biological responses is distributed by one or more computing devices (22) to one or more other computing devices for consideration (Fig. 1; [0041], "computing subsystem 22 communicates with the source 12 of animal data through cloud 40 or a local server (e.g., a localised or networked server/storage, localized storage device, distributed network of computing devices); [0081], 'the computing subsystem is
operable to record one or more characteristics of the animal data provided as part of its one or more distributions'; [0097], 'In another refinement, the one or more product subsystems may be operable to provide one or more products and/or at least a portion of the output information to one or more users. Finally, Figure 3 illustrates revenue reconciliation feature 90 in which consideration can be distributed to one or more stakeholders for their contribution in creating, collecting, modifying, enhancing, analyzing, offering, distributing, and or productizing the animal data').
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sports Data Labs '699 in view of Sports Data Labs, Inc. (WO 2020/214730).
Regarding Claim 14, Sports Data Labs '699 disclose the instant claimed invention except for the animal data being distributed as part of an animal data monetization system. Sports Data Labs Inc. '730 is in the field of animal data (Title and Abstract) and teaches wherein an animal data is distributed as part of an animal data monetization system (Monetization system 10; Fig. 1; [0049], 'With reference to Figure 1, a schematic of a system for monetizing animal data is provided. Monetization system: 10 includes a source 12 of animal data 14 that can be transmitted electronically'). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to utilize to modify Sports Data Labs '699 with the animal data monetization system of Sports Data Labs '730 for the purpose of selling animal data, thereby allowing the purchasers of data to use it or wagering, medical, commerce, and other purposes (Sports Data Labs '730; [0008]-[0009]).
Conclusion
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/TAI T NGUYEN/Primary Examiner, Art Unit 2685 June 23, 2026
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