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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 5/27/2025, Applicant, on 4/24/2026, amended claim 1. Claims 1-34 and 36-51 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
4/24/2026 has been entered.
Response to Arguments
Applicant’s arguments filed April 24, 2026 have been fully considered but they are not persuasive and/or are moot in view of revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed April 24, 2026.
On Pg. 12-13 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states the amended claim does not recite the mere concept of predicting outcomes or managing personal interactions. The claim defines a specific technical system that performs a defined sequence of operations that no human could perform mentally or through pen-and-paper methods. The amended claim requires the computing subsystem to synchronize data streams from heterogeneous source sensors to resolve timing mismatches between data signals received from different source sensors, and to apply a schema suitable for real-time or near real-time data transfer that reduces latency. In response, Examiner finds The claims primarily recite the additional element of using computer components to perform each step. The “computing subsystem” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
On Pg. 13 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states Amended Claim 1 also requires the computing subsystem to generate artificial animal data by executing simulations that utilize at least a portion of the collected animal data and historical data via artificial intelligence techniques or statistical models. A human mind cannot execute AI-driven simulations on real- time biomechanical sensor data to produce artificially-created biological data products and then run further simulations on those products. This is not the abstract concept of "making predictions from data." In response, the additional element of artificial intelligence - the specification discloses the artificial intelligence at a high-level of generality, providing examples of different techniques that may be applied. The general use of an artificial intelligence technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the artificial intelligence is solely used a tool to perform the instructions of the abstract idea. The additional element of “sensor” is MPEP 2106.05(h) – field of use.
On Pg. 13-14 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states Amended Claim 1 additionally requires synchronization of the predictive indicator and computed assets with live media content related to the sports activity to enable users to place wagers or create products while consuming that live media content. This is a temporal alignment of two independent real-time data flows-sensor-derived predictive outputs and a live broadcast stream-so that predictions are contextually relevant to what a user is viewing at a given moment. This is not the abstract concept of displaying data to a user. It requires the system to match the timing of its outputs to a separately-produced live media stream, a function that imposes meaningful technical constraints on the computing subsystem and wagering system. In response, the claim language of the computing subsystem and wagering system is extremely broad. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Applicants have not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. Examiner recommends including how It involves actively resolving temporal discrepancies between heterogeneous data streams from sensors operating at different sampling rates-a specialized data processing function rather than passive data reception and positively reciting that improvement in the claim language.
On Pg. 15-16 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 103, Applicant states cited references fail to disclose requires the computing subsystem to be (1) "configured to generate artificial animal data by executing one or more simulations that utilize at least a portion of the collected animal data and historical data via one or more artificial intelligence techniques or statistical models," where the artificial animal data is (2) "artificially-created data derived from or generated using, at least in part, real animal data or its one or more derivatives." The claim further requires the computing subsystem to (3) "use the artificial animal data to derive, modify, or enhance one or more predictions, probabilities, or possibilities based upon use of the artificial animal data in one or more simulated events. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Tran is now applied for Claims 1 to support analysis. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
On Pg. 17 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 103, Applicant states Amended Claim 1 requires that the wagering system or computing subsystem be "further operable to synchronize the predictive indicator and/or the at least one computed asset with live media content related to the sports activity to enable one or more users to place one or more wagers and/or create, modify, enhance, acquire, offer, or distribute one or more products while consuming the live media content.". In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Tran is now applied for Claims 1 to support analysis. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
On Pg. 17 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 103, Applicant states none of the cited references teach or suggest the claimed multi-sensor stream synchronization to resolve timing mismatches with a latency-reducing data transfer schema. Nor has the Examiner articulated a proper motivation to combine the references to arrive at the specific claimed invention. Amended Claim 1 requires the computing subsystem to be "configured to synchronize data streams from the one or more source sensors to resolve timing mismatches between data signals received from different source sensors, and to apply a schema suitable for real-time or near real-time data transfer that reduces latency.". I First, it argues against the references individually, and one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically, Tran is now applied for Claims 1 to support analysis. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever inv33ents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 34 and 36-51 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-34 and 36-51 are directed to an animal speculation system.
Claim 1 recites a system for providing animal data and predictive indicators, which include collecting animal data from one or more targeted individuals, the animal data including three dimensional tracking; receiving the animal data including a predictive indicator from the animal data to predict one or more outcomes; at least a portion of the animal data being transformed into at least one computed asset assigned to a selected targeted individual or group of targeted individuals engaged in a sports activity in a sporting venue ; receive animal data transforming the at least one computed asset into a predictive indicator; providing the predictive indicator, the at least one computed asset, and/or at least a portion of the animal data to one or more users; determine whether or not to place a bet, determine the probability of an outcome occurring for an event, revise previously determined probability for an event, or formulate a strategy upon which a market is created for individuals to place a wager on or upon which an action is taken, synchronizes, time-stamps, and tags the animal data with information related to the one or more targeted individuals; accept one or more wagers; and providing transmission of the animal data and sending at least a portion of the animal data to another location and to store the animal data for later use.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity”- managing personal interactions . The recitation of “sensor”, “computing subsystem”, “transmission subsystem”, and “wagering system”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing personal interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing subsystem”, “transmission subsystem”, and “wagering system” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, The “sensor”,” electronic signals”, ” receivers”; “ transmitters” “transceivers” and “antenna” are MPEP 2106.05{h)- field of use. Please review the 101 rejection below for additional analysis. Claim 1 recite using one or more neural network techniques. The specification discloses the artificial intelligence analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of an neural network does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “sensor”, “computing subsystem”, “transmission subsystem” and “wagering system” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Further the sensor is M2106.05(h)- field of use.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-34 and 36-51 further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claim 1. Regarding Claims, 2,12-13,15, 18-33, 36-40, 44-45, 48 and 51 and the additional elements of “ system”, “subsystem”, “aerial transreceiver”, “signals” and “sensor” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 42-43 and the additional element of artificial intelligence/ neural network - the specification discloses the artificial intelligence at a high-level of generality, providing examples of different techniques that may be applied. The general use of an artificial intelligence technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the artificial intelligence is solely used a tool to perform the instructions of the abstract idea. Regarding Claim 49 and 50 -51 and the additional element of “transreceiver” – it is M2106.05(h) field of use.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-34 and 36-48 are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al., US Publication No. 20160170996 A1, [hereinafter Frank], in view of Tran et al., US Publication No. 20170232300A1, [hereinafter Tran], in further view of Luinge et al., US Publication No. 20080285805A1, [hereinafter Luinge], and in further view of Guan, US Publication No. 2013/0217475A1, [hereinafter Guan].
Regarding Claim 1,
Frank teaches
A speculation system comprising: one or more source sensors that collect animal data from one or more targeted individuals engaged in a sports activity in a sporting venue wherein the animal data is transmitted electronically; (Frank Abstract-Some aspects of this disclosure include systems, methods, and/or computed readable media that may be used to generate crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who have a certain experience.; Par. 254-“In another example, a location may be an entertainment establishment that is one or more of the following: a club, a pub, a movie theater, a theater, a casino, a stadium, and a certain concert venue.”; Par.2412-2413“ As used herein, a sensor is a device that detects and/or responds to some type of input from the physical environment. Herein, “physical environment” is a term that includes the human body and its surroundings. In some embodiments, a sensor that is used to measure affective response of a user may include, without limitation, one or more of the following: a device that measures a physiological signal of the user, an image-capturing device (e.g., a visible light camera, a near infrared (NIR) camera, a thermal camera (useful for measuring wavelengths larger than 2500 nm), a microphone used to capture sound, a movement sensor, a pressure sensor, a magnetic sensor, an electro-optical sensor, and/or a biochemical sensor. When a sensor is used to measure the user, the input from the physical environment detected by the sensor typically originates and/or involves the user.”; Par 2416-“In some embodiments, a sensor may store data it collects and/processes (e.g., in electronic memory). Additionally or alternatively, the sensor may transmit data it collects and/or processes. Optionally, to transmit data, the sensor may use various forms of wired communication and/or wireless communication, such as Wi-Fi signals, Bluetooth, cellphone signals, and/or near-field communication (NFC) radio signals.”; Par. 4190- For example, a user may have recently suffered a sports-related injury. In this example, a factor corresponding to the user having the injury may be assigned to an event involving an experience such as playing football, but not to an event that involves viewing a movie. )
the one or more source sensors providing one or more electronic signals, …the one or more source sensors including at least one optical or translation sensor that provides biomechanical data… (Frank Par. 772- Some aspects of this disclosure involve collecting measurements of affective response of users who dined at restaurants. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).; Par. 2413; Par. 2764);
a computing subsystem that receives the animal data, the computing subsystem including a trained neural network model configured to generate a predictive indicator from the animal data to predict one or more outcomes, (Frank Par. 237-“Herein, “affect” and “affective response” refer to physiological and/or behavioral manifestation of an entity's emotional state. The manifestation of an entity's emotional state may be referred to herein as an “emotional response”, and may be used interchangeably with the term “affective response”. Affective response typically refers to values obtained from measurements and/or observations of an entity, while emotional states are typically predicted from models and/or reported by the entity feeling the emotions. “; Par. 1386; Par. 1397-“When the trained predictor is provided inputs indicative of the durations Δt1 and Δt2, the predictor predicts the values v1 and v2, respectively. Optionally, the model comprises at least one of the following: a regression model, a model utilized by a neural network, a nearest neighbor model, a model for a support vector machine for regression, and a model utilized by a decision tree. Optionally, the parameters 658 comprise the parameters of the model and/or other data utilized by the predictor.”)
at least a portion of the animal data being transformed by the computing subsystem or the one or more source sensors into at least one computed asset assigned to a selected targeted individual or group of targeted individuals, …; (Frank Par.1634-1635-“ A plurality of sensors may be used, in various embodiments described herein, to take the measurements 1501 of affective response of travelers belonging to the crowd 1500. Optionally, each measurement of a traveler is taken with a sensor coupled to the traveler, while the traveler travels in a vehicle. Optionally, each measurement of affective response of a traveler represents an affective response of the traveler to traveling in the vehicle. Each sensor of the plurality of sensors may be a sensor that captures a physiological signal and/or a behavioral cue of a user. Additional details about the sensors may be found in this disclosure at least in section 5—Sensors. Additional discussion regarding the measurements 1501 is given below. In some embodiments, the measurements 1501 of affective response may be transmitted via a network 112. Optionally, the measurements 1501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response; Par. 401; Par. 263; Par. 1386;Claim 1).
…the predictive indicator allowing the one or more users to do at least one of the following: determine whether or not to place a bet, determine the probability of an outcome occurring for an event, revise previously determined probability for an event, or formulate a strategy upon which a market is created for individuals to place a wager on or upon which an action is taken, (Frank Par. 2513-2514-“Affective values may have various meanings in different embodiments. In some embodiments, affective values may correspond to quantifiable measures related to an event (which may take place in the future and/or not always be quantifiable for every instance of an event). In one example, an affective value may reflect expected probability that the user corresponding to the event may have the event again (i.e., a repeat customer). In another example, an affective value may reflect the amount of money a user spends during an event (e.g., the amount of money spent during a vacation). Such values may be considered affective values since they depend on how the user felt during the event. Collecting such labels may not be possible for all events and/or may be expensive (e.g., since it may involve purchasing information from an external source).”; Par. 2875);
Frank discloses sensor data analysis and the feature is expounded upon by Tran:
the computing subsystem being configured to extract or derive the at least one computed asset from the one or more electronic signals, the computing subsystem being further configured to synchronize data streams from the one or more source sensors to resolve timing mismatches between data signals received from different source sensors, and to apply a schema suitable for real-time or near real-time data transfer that reduces latency, … (Tran Par. 98-103; Par. 215; Par. Par. 335- When the assistant operates in an environment equipped with a handheld computer which is adapted to work with a host computer, the assistant splits into two personalities, one residing on the handheld computer with an intelligent desktop assistant for interacting with the user and one residing on a host computer with an information locator for executing searches in the background. When results are found, the assistant running on the host computer prioritizes the retrieved documents. Further, the assistant on the host computer transforms the data designed to be sent to the handheld computer into an equivalent file optimized for fast and robust wireless transmission. The assistant then immediately transmits the transformed, high priority documents to the handheld computer through a wireless modem while withholding lower priority documents for transfer when the handheld computer docks with the host computer to minimize data transmission costs. Further, upon docking, the assistant on the handheld computer synchronizes its knowledge base with the assistant running on the host computer to ensure that the personalities on the handheld and host computers have consistent knowledge of their environments.; Par. 1040-1041)
the one or more source sensors or the computing subsystem being operable to transform the at least one computed asset into a predictive indicator, the computing subsystem being further configured to generate artificial animal data by executing one or more simulations that utilize at least a portion of the collected animal data and historical data via one or more artificial intelligence techniques or statistical models, the artificial animal data being artificially-created data derived from or generated using, at least in part, real animal data or its one or more derivatives, the computing subsystem being further configured to use the artificial animal data to derive, modify, or enhance one or more predictions, probabilities, or possibilities based upon use of the artificial animal data in one or more simulated events, the computing subsystem being further configured to provide the predictive indicator, the at least one computed asset, and/or at least a portion of the animal data to one or more users (Tran Par. 63-68;Par. 72-After a change in stress of the non-smart device is detected, the method 300 proceeds to block 310. In block 304, a stress report from a smart device such as the smart device 100 associated with the unit may be received. A smart device may be a device that detects stress and generates and transmits an stress report indicating the stress on the smart device. The stress report may indicate predicted future stress of the smart device. In some embodiments, a stress report may be received at set intervals from the smart device regardless of a change in the stress report. Alternately or additionally, a stress report may be received after a change in the stress of the smart device results in a change to the stress report. After a stress report is received from the smart device, the method 300 proceeds to block 310.; Par. 77-83-FIG. 5 schematically shows a method or app 52 to perform collaborative VR/AR gaming. The app 52 includes code for: (51) capture 360 degree view of the live event (52) detect head position of the viewer (53) adjust viewing angle on screen based on head position and user posture (54) render view to simulate action based on user control rather than what the professional is doing (55) augment view with a simulated object that is powered by viewer action as detected by sensors on viewer body (56) compare professional result with simulated result and show result to a crowd of enthusiasts for social discussion.”; Par. 1005)
wherein the computing subsystem is further operable to synchronize, time-stamp, and tag the animal data with information related to the one or more targeted individuals from which the animal data is collected and the one or more source sensors including at least one characteristic of the one or more source sensors (Tran 77-83; Par. 103-Impact sensors, or accelerometers, measure in real time the force and even the number of impacts that players sustain ; Par. 216; Par. 221; Par. 291; Par. Par.324”);
…the wagering system or the computing subsystem being further operable to synchronize the predictive indicator and/or the at least one computed asset with live media content related to the sports activity to enable one or more users to place one or more wagers and/or create, modify, enhance, acquire, offer, or distribute one or more products while consuming the live media content (Tran 77-83; Par. 103-Impact sensors, or accelerometers, measure in real time the force and even the number of impacts that players sustain ; Par.1004-1005-Augmented Reality/Virtual Reality Sports Gaming FIG. 15 shows an exemplary 360 degree camera on a helmet, for example, for augmenting reality of sport games. Using augmented reality, various ways may exist for a user to “participate” in a live event. Generally, augmented reality refers to a presentation of a real world environment augmented with computer-generated data (such as sound, video, graphics or other data). In some embodiments, augmented reality, implemented in conjunction with a live event, may allow a user to control a virtual object that appears to compete or otherwise interact with the participants of the live event. For example, an end user device, such as a mobile phone, tablet computer, laptop computer, or gaming console may be used to present a live video feed of an event to a user. This live video feed may be video of an event that is occurring in real-time, meaning the live event is substantially concurrently with the presentation to the user (for example, buffering, processing, and transmission of the video feed may result in a delay anywhere from less than a second to several minutes). The presentation of the live event may be augmented to contain one or more virtual objects that can be at least partially controlled by the user. For instance, if the live event is a stock car race, the user may be able to drive a virtual car displayed on the end user device to simulate driving in the live event among the actual racers. As such, the user may be able to virtually “compete” against the other drivers in the race. The virtual object, in this example a car, may be of a similar size and shape to the real cars of the video feed. The user may be able to control the virtual car to race against the real cars present in the video feed. The real cars appearing in the video feed may affect the virtual object. For example, the virtual object may not be allowed to virtually move through a real car on the augmented display, rather the user may need to drive the virtual object around the real cars. Besides racing, similar principles may be applied to other forms of live events; for example, track and field events (e.g., discus, running events, the hammer toss, pole vaulting), triathlons, motorbike events, monster truck racing, or any other form of event that a user can virtually participate in against the actual participants in the live event. In some embodiments, a user may be able to virtually replay and participate in past portions of a live event. A user that is observing a live event may desire to attempt to retry an occurrence that happened during the live event. While viewing the live event, the user may be presented with or permitted to select an occurrence that happened in the course of the live event and replay it such that the user's input affects the outcome of at least that portion of the virtualized live event.”; Par. 753-755);
Frank and Tran are directed to sensor data processing. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frank, as taught by Tran, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frank with the motivation of improving monitoring accuracy (Tran Par. 88).
Frank in view of Tran disclose using sensors to collect data and the feature is expounded upon by Luinge:
the animal data including three dimensional tracking and skeletal data, the one or more source sensors including at least one optical or translation sensor that provides biomechanical data that includes data selected from the group consisting of angular velocity, joint paths, gait description, step count, and bodily accelerations in various directions from which a targeted subject's movements may be characterized (Luinge Par. 9-“ Embodiments of the invention are used to provide a system for capturing motion of a moving animate object, such as a human body, via a body suit having a plurality of sensor modules placed on various body segments. In other embodiments, the sensor modules are not associated with a body suit, in which case the sensor modules are strapped down, taped, or otherwise individually affixed to the object's body. The sensor modules capture signals for estimating both three-dimensional (3D) position and 3D orientation data relating to their respective body segments, thereby gathering motion data having six degrees of freedom with respect to a coordinate system not fixed to the body. Each body sensor collects 3D inertial sensor data, such as via accelerometers and gyroscopes, and, optionally, magnetic field data via magnetometers.”; Par. 14; Fig. 1; Par. 31- angular velocity; Par. 32- count/ acceleration; Par. 33- joint paths; Par. 3- gait; Par. 44- gait; Par. 43-44- joints, vertebrae, neck, back)
and a transmission subsystem providing transmission of the animal data to the computing subsystem, the transmission subsystem including one or more receivers, transmitters, or transceivers having a single antenna or multiple antennas, the transmission subsystem being configured to enable the one or more source sensors to transmit data wirelessly for real-time or near real-time communication, the transmission subsystem being further configured to send at least a portion of the animal data to another location and to store the animal data for later use (Luinge Par. 47; Par. 54; Par. 55-“ Referring to FIG. 9, an embodiment of a motion capture system comprising a signal bus module for synchronization and wireless transmission of sensor signals is shown. The motion tracking system 900 includes a number of inertial and magnetic sensor modules 902 for each tracked body segment. The sensors 902 are connected to a bus module 904, which handles the power supply for all sensors, as well as synchronization of data sampling and wireless transmission of all sensor data and processed data to an external computer or logging device 906.; Par. 56-57-“In another embodiment illustrated in FIG. 10, to eliminate the use of cables 908 within the suit 910, each sensor 914 includes a battery 916 and a wireless communication module 918. In embodiments, the wireless communication module 918 employs Bluetooth, WiFi, UWB, or a similar wireless technology. The sensor sampling module 920 forwards the signals from each of the 3D gyro unit 922, 3D accelerometer unit 924, and 3D magnetometer unit 926 of the sensor unit 914 to the DSP module 928 for calculating the orientation and position estimates for the associated body segment. As further illustrated in FIG. 11, the wireless communication module 918, in turn, transmits the sensor data, as well as the calculated orientation and position estimates, to an external computer or logging device 930 for synchronization of sensor information between the sensors 914 and, optionally, additional processing of sensor data via a sensor fusion circuit described above in connection with FIG. 8, for example.”).
Frank, Tran and Luinge are directed to the collection of measurement data via sensors. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frank in view of Tran, as taught by Luinge, by utilizing additional sensor data collection and analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frank in view of Tran with the motivation of improving estimation accuracy (Luinge Abstract).
Frank in view of Tran in further view of Luinge disclose the speculations system of Claim 1 but fails to teach the following feature taught by Guan:
a wagering system configured to accept one or more wager… (Guan Abstract-A method of allowing a user to place a parlay wager via a mobile computing device is described herein. The method includes receiving, from the mobile computing device, a request to display information associated with a plurality of wagering events, and retrieving, from a database, an event list including a plurality of wagering events and displaying the list of wagering events on the mobile computing device. The method also includes receiving a request to generate a parlay wager, selecting a first wagering event and a second wagering event, and generating a parlay wager based on the selected first and second wagering events. The method also includes determining the outcome of each of the first and second wagering events, and providing an award to the user determined as a function of the first and second wagering event outcomes and the generated parlay wager.
Frank, Tran and Luinge is directed to human data collection analysis and Guan improves upon the analysis. It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the teaching of Frank in view of Luinge to include wherein the market or wager includes at least one of a bet disclosed by Guan, to gain the advantage of allowing user to place wagers that involve selecting a winning team and involve the score differential between winning and losing teams.
Regarding Claim 2,
The speculation system of claim I wherein the one or more source sensors include at least one biosensor; (Frank Par. 772- Some aspects of this disclosure involve collecting measurements of affective response of users who dined at restaurants. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).; Par. 2764)
Regarding Claim 3,
The speculation system of claim. I wherein the at least one computed asset includes one or more numbers, a plurality of numbers, metrics, insights, graphs, charts, or plots that are derived from at least a portion of the animal data; (Frank Par. 1420- In some embodiments, the method may optionally include Step 660 c that involves displaying the aftereffect function learned in Step 660 b on a display such as the display 252. Optionally, displaying the aftereffect function involves rendering a representation of the aftereffect function and/or its parameters. For example, the function may be rendered as a graph, plot, and/or any other image that represents values given by the function and/or parameters of the function.; Par. 2851)
Regarding Claim 4,
The speculation system of claim 3 wherein the at least one computed asset includes one or more signals or readings from non-animal data; (Frank Par. 347- The system may optionally include, in some embodiments, the location verifier module 505, which is configured to determine whether the user is likely in the seat or not (or is likely in the seat). In one embodiment, the location verifier module 505 is configured to determine whether the user is in a certain seat by receiving signals from the vehicle, e.g., an output generated by an entertainment system in the vehicle indicating to what seat a device of the user is paired. In another embodiment, location verifier module 505 is configured to determine, by receiving wireless transmissions (e.g., by identifying a network and/or using triangulation of wireless signals), in what seat or region of the vehicle the user is sitting.; Par.-[0780],[4545],[4546])
Regarding Claim 5,
The speculation system of claim 1. wherein the predictive indicator is a calculated computed asset from at least a portion of the animal data. (Frank Par.4547- In some embodiments, scores for experiences may be produced by a source (e.g., by a scoring module) in succession, each score corresponding to a certain period of time during which a set of measurements of affective response used to compute the score were taken. Each score may be associated with an average measurement time, which is the average time measurements belonging to its corresponding set were taken). Optionally, the periods corresponding to the scores may have the same length (e.g., one minute, five minutes, hour, day, or week). Optionally, the periods corresponding to the scores may have different lengths. In one embodiment, first and second scores produced by the source are considered in temporal proximity if there is no other score produced by the source with an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score. In another embodiment, first and second scores produced by the source are considered in temporal proximity if the number of scores produced by the source, having an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score, is below a certain threshold such as 2, 3, 5, 10, or 100.)
Regarding Claim 6,
The speculation system of claim. 5 wherein the predictive indicator includes one or more signals or readings from non-animal data, (Frank Par. 347; Par. 780; Par.4547- In some embodiments, scores for experiences may be produced by a source (e.g., by a scoring module) in succession, each score corresponding to a certain period of time during which a set of measurements of affective response used to compute the score were taken. Each score may be associated with an average measurement time, which is the average time measurements belonging to its corresponding set were taken). Optionally, the periods corresponding to the scores may have the same length (e.g., one minute, five minutes, hour, day, or week). Optionally, the periods corresponding to the scores may have different lengths. In one embodiment, first and second scores produced by the source are considered in temporal proximity if there is no other score produced by the source with an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score. In another embodiment, first and second scores produced by the source are considered in temporal proximity if the number of scores produced by the source, having an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score, is below a certain threshold such as 2, 3, 5, 10, or 100.)
Regarding Claim 7,
The speculation system of claim 5 wherein the predictive indicator is comprised of a plurality of predictive indictors. (Frank Par.4547- In some embodiments, scores for experiences may be produced by a source (e.g., by a scoring module) in succession, each score corresponding to a certain period of time during which a set of measurements of affective response used to compute the score were taken. Each score may be associated with an average measurement time, which is the average time measurements belonging to its corresponding set were taken). Optionally, the periods corresponding to the scores may have the same length (e.g., one minute, five minutes, hour, day, or week). Optionally, the periods corresponding to the scores may have different lengths. In one embodiment, first and second scores produced by the source are considered in temporal proximity if there is no other score produced by the source with an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score. In another embodiment, first and second scores produced by the source are considered in temporal proximity if the number of scores produced by the source, having an average measurement time that is between the average measurement time of the first score and the average measurement time of the second score, is below a certain threshold such as 2, 3, 5, 10, or 100.)
Regarding Claim 8,
The speculation system of claim 5 wherein at least a portion of the predictive indicator is derived from or related to, at least in part, a group consisting of a targeted individual, multiple targeted individuals, a targeted group comprised of multiple targeted individuals, or multiple targeted groups that include multiple targeted individuals. (Refer to Claim 1)
Regarding Claim 9,
The speculation system of claim 5 wherein the predictive indicator is a composite calculated from two or more signals or readings from one or more source sensors. (Frank Par2980- In some embodiments, when t1 and t2 denote different times to which scores correspond, and t2 is after t1, the difference between t2 and t1 may be fixed. In one example, this may happen when scores for experiences may be computed periodically, after elapsing of a certain period. For example, a new score is computed every minute, every ten minutes, every hour, or every day. In other embodiments, the difference between t2 and t1 is not fixed. For example, a new score may be computed after a certain condition is met (e.g., a sufficiently different composition of users who contribute measurements to computing a score is obtained). In one example, a sufficiently different composition means that the size of the overlap between the set of users who contributed measurements to computing the score S1 corresponding to t1 and the set of users who contributed measurements to computing the score S2 corresponding to t2 is less than 90% of the size of either of the sets. In other examples, the overlap may be smaller, such as less than 50%, less than 15%, or less than 5% of the size of either of the sets.)
Regarding Claim 10,
The speculation system of claim 5 wherein the predictive indicator is calculated from the at least one computed asset that includes biological data selected from the group consisting of: facial recognition data, eye tracking data, blood flow data, blood volume data, blood pressure data, biological fluid data, body composition data, biochemical composition data, biochemical structure data, pulse data, oxygenation data, core body temperature data, skin temperature data, galvanic skin response data, perspiration data, location data, positional data, audio data, hydration data, heart-based data, neurological data, genetic data, genomic data, skeletal data, muscle data, respiratory data, kinesthetic data, thoracic electrical bioimpedance data, or a combination thereof. (Frank Par2434- In some embodiments, a measurement of affective response of a user may include a physiological signal derived from a biochemical measurement of the user. For example, the biochemical measurement may be indicative of the concentration of one or more chemicals in the body of the user (e.g., electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity). In one example, a measurement of affective response may describe the glucose level in the bloodstream of the user. In another example, a measurement of affective response may describe the concentration of one or more stress-related hormones such as adrenaline and/or cortisol. In yet another example, a measurement of affective response may describe the concentration of one or more substances that may serve as inflammation markers such as C-reactive protein (CRP). In one embodiment, a sensor that provides a biochemical measurement may be an external sensor (e.g., a sensor that measures glucose from a blood sample extracted from the user). In another embodiment, a sensor that provides a biochemical measurement may be in physical contact with the user (e.g., contact lens in the eye of the user that measures glucose levels). In yet another embodiment, a sensor that provides a biochemical measurement may be a sensor that is in the body of the user (an “in vivo” sensor). Optionally, the sensor may be implanted in the body (e.g., by a chirurgical procedure), injected into the bloodstream, and/or enter the body via the respiratory and/or digestive system.; Par. [2428],[3923) )
Regarding Claim 11,
The speculation system of claim 5 wherein at least a portion of the predictive indicator is used either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) as one or more readings utilized in one or more simulations, computations, or analyses; (8) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (9) to recommend one or more actions; (10) as one or more core components or supplements to one or more mediums of consumption; (11) in one or more promotions; or (12) a combination thereof (Frank Par2728- In some embodiments, a software agent operating on behalf of an entity is implemented, at least in part, via a computer program that is executed in order to advance a goal of the entity, protect an interest of the entity, and/or benefit the entity. In one example, a software agent may seek to identify opportunities to improve the well-being of the entity, such as identifying and/or suggesting activities that may be enjoyable to a user, recommending food that may be a healthy choice for the user, and/or suggesting a mode of transportation and/or route that may be safe and/or time saving for the user. In another example, a software agent may protect the privacy of the entity it operates on behalf of, for example, by preventing the sharing of certain data that may be considered private data with third parties. In another example, a software agent may assess the risk to the privacy of a user that may be associated with contributing private information of the user, such as measurements of affective response, to an outside source. Optionally, the software agent may manage the disclosure of such data, as described in more detail elsewhere in this disclosure.; Par. [2762],[2793} )
Regarding Claim 12,
The speculation system of claim 1 wherein one or more outputs from the computing subsystem are used to either directly or indirectly; Frank [0263],[2737] (1) as a market upon which one or more wagers are placed or accepted; (2) to accept one or more wagers; (31) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (5) to formulate one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks: (8) as one or more signals or readings utilized in one or mare simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) to recommend one or more actions; (11) as one or more core components or supplements to one or more mediums of consumption; (12) in one or more promotions; or (13) a combination thereof. (Frank Par2728- In some embodiments, a software agent operating on behalf of an entity is implemented, at least in part, via a computer program that is executed in order to advance a goal of the entity, protect an interest of the entity, and/or benefit the entity. In one example, a software agent may seek to identify opportunities to improve the well-being of the entity, such as identifying and/or suggesting activities that may be enjoyable to a user, recommending food that may be a healthy choice for the user, and/or suggesting a mode of transportation and/or route that may be safe and/or time saving for the user. In another example, a software agent may protect the privacy of the entity it operates on behalf of, for example, by preventing the sharing of certain data that may be considered private data with third parties. In another example, a software agent may assess the risk to the privacy of a user that may be associated with contributing private information of the user, such as measurements of affective response, to an outside source. Optionally, the software agent may manage the disclosure of such data, as described in more detail elsewhere in this disclosure.; Par.[2762],[2793] )
Regarding Claim 13,
The speculation system of claim 12 wherein the one or more direct or indirect uses by the computing subsystem are dynamic, at least in part, and based upon one or more user interactions with the one or more Outputs from the computing subsystem. (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par.,[0558],[2855] )
Regarding Claim 14,
Frank discloses the speculations system of Claim 12 but fails to teach the following feature taught by Guan:
wherein the market or wager includes at least one of; a proposition bet, spread bet, a line bet, a future bet, a parlay bet, a round-robin bet, a handicap bet, an over/under bet, a full cover bet, or a teaser bet (Guan Abstract-A method of allowing a user to place a parlay wager via a mobile computing device is described herein. The method includes receiving, from the mobile computing device, a request to display information associated with a plurality of wagering events, and retrieving, from a database, an event list including a plurality of wagering events and displaying the list of wagering events on the mobile computing device. The method also includes receiving a request to generate a parlay wager, selecting a first wagering event and a second wagering event, and generating a parlay wager based on the selected first and second wagering events. The method also includes determining the outcome of each of the first and second wagering events, and providing an award to the user determined as a function of the first and second wagering event outcomes and the generated parlay wager.
Frank in view Tran in further view of Luinge is directed to human data collection analysis and Guan improves upon the gaming analysis. It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the teaching of Frank in view Tran in further view of Luinge to include wherein the market or wager includes at least one of a bet disclosed by Guan, to gain the advantage of allowing user to place wagers that involve selecting a winning team and involve the score differential between winning and losing teams.
Regarding Claim 15,
The speculation system of claim 12 wherein the one or more outputs from the computing subsystem are dynamically created, modified, or enhanced by the computing subsystem (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par.,[0509],[738] )
Regarding Claim 16,
The speculation system of claim 15 wherein creation, modification, or enhancement of the one or more outputs is based on or derived from, at least in part, one or more user interactions with the predictive indicator, the at least one computed asset, and/or the animal data (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par.,[0738],[2579] )
Regarding Claim 17,
The speculation system of claim 15 wherein at least a portion of dynamically created, modified, or enhanced one or more outputs are utilized either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to formulate one or more strategies; (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) as one or more signals or readings utilized in one or more simulations, computations, or analyses: (8) as part of one or more simulations, an output of which directly or indirectly engages with one or more users: (9) to recommend one or more actions; (10) as one or more core components or supplements to one or more mediums of consumption: (1 1) in one or more promotions; or (12) a combination thereof (Frank Par2762- Various embodiments described herein utilize systems whose architecture includes a plurality of sensors and a plurality of user interfaces. This architecture supports various forms of crowd-based recommendation systems in which users may receive information, such as suggestions and/or alerts, which are determined based on measurements of affective response collected by the sensors. In some embodiments, being crowd-based means that the measurements of affective response are taken from a plurality of users, such as at least three, ten, one hundred, or more users. In such embodiments, it is possible that the recipients of information generated from the measurements may not be the same users from whom the measurements were taken..; Par.,[2768],[4664] )
Regarding Claim 18,
The speculation system of claim 1 wherein the computing subsystem provides one or more data outputs to one or more systems, (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par.,[2684],[2737] )
Regarding Claim 19,
The speculation system of claim 18 wherein the predictive indicator is created, modified, enhanced by the one or more systems (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response). )
Regarding Claim 20,
The speculation system. of claim 18 wherein the one or more systems are operable to utilize at least a portion of the one or more data outputs either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to accept one or more wagers: (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (5) to formulate one or more strategies (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) to recommend one or more actions: (1 1) as one or more core components or supplements to one or more mediums of consumption; ( 12) in one or more promotions; or (13) a combination thereof (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par.,[0260],[4664],[4678] )
Regarding Claim 21,
The speculation system of claim 18 wherein the computing subsystem provides the same or substantially similar one or more outputs to a plurality of users. (Frank Par270- In step 506 b, receiving data describing the location score; the location score is computed based on the measurements of the at least ten users and represents an affective response of the at least ten users to being at the certain location.)
Regarding Claim 22,
The speculation system of claim 18 wherein the one or more data outputs of the computing subsystem are synchronized with one or more non-animal data readings . (Frank Par347- The system may optionally include, in some embodiments, the location verifier module 505, which is configured to determine whether the user is likely in the seat or not (or is likely in the seat). In one embodiment, the location verifier module 505 is configured to determine whether the user is in a certain seat by receiving signals from the vehicle, e.g., an output generated by an entertainment system in the vehicle indicating to what seat a device of the user is paired. In another embodiment, location verifier module 505 is configured to determine, by receiving wireless transmissions (e.g., by identifying a network and/or using triangulation of wireless signals), in what seat or region of the vehicle the user is sitting.; Par. 2621;2623)
Regarding Claim 23,
The speculation system of claim 18 wherein the one or more data outputs of the computing subsystem are synchronized with one or more mediums of consumption. (Frank Par350- In one embodiment, a profile of a user may include information that describes one or more of the following: the age of the user, the gender of the user, the height of the user, the weight of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, and a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay. Optionally, the profile may include information regarding seats the user occupied in vehicles when traveling.; Par. 407; 1901)
Regarding Claim 24,
The speculation system of claim I wherein the computing subsystem is operable to receive groups of animal data from a single targeted individual or multiple targeted individuals. (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Abstract)
Regarding Claim 25,
The speculation system of claim 1 wherein the computing subsystem is operable to gather information from one or more source sensors by communicating directly with the one or more source sensors, its associated cloud, or a native application associated with the one or more source sensors. (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par. 2740; 2767;)
Regarding Claim 26,
The speculation system of claim 25 wherein the computing subsystem is operable to manage the one or more source sensors, and one or more data streams from the one or more source sensors, by at least one characteristic from the group consisting of: organization, sensor type, sensor parameter, data type, data quality, time stamp, location, activity, a targeted individual, groupings of targeted individuals, and data reading. (Frank Par2838- In one embodiment, the collection module receives at least some of the measurements directly from the users of whom the measurements are taken. In one example, the measurements are streamed from devices of the users as they are acquired (e.g., a user's smartphone may transmit measurements acquired by one or more sensors measuring the user). In another example, a software agent operating on behalf of the user may routinely transmit descriptions of events, where each event includes a measurement and a description of a user and/or an experience the user had.; Par. 2751)
Regarding Claim 27,
The speculation system of claim I wherein the computing subsystem is operable to communicate with a plurality of source sensors on the one or more targeted individuals or one or more source sensors on multiple targeted individuals simultaneously. (Frank Par263- In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).; Par. 2515; 2560;2751)
Regarding Claim 28,
The speculation system of claim 1 wherein the transmission subsystem enables the one or more source sensors to transmit data wirelessly for real-time or near real-time communication, (Frank Par2416- In some embodiments, a sensor may store data it collects and/processes (e.g., in electronic memory). Additionally or alternatively, the sensor may transmit data it collects and/or processes. Optionally, to transmit data, the sensor may use various forms of wired communication and/or wireless communication, such as Wi-Fi signals, Bluetooth, cellphone signals, and/or near-field communication (NFC) radio signals.; Par. 2765; 2838)
Regarding Claim 29,
The speculation system of claim 1 wherein the transmission subsystem communicates with the one or more source sensors utilizing one or more transmission protocols.. (Frank Par2416- In some embodiments, a sensor may store data it collects and/processes (e.g., in electronic memory). Additionally or alternatively, the sensor may transmit data it collects and/or processes. Optionally, to transmit data, the sensor may use various forms of wired communication and/or wireless communication, such as Wi-Fi signals, Bluetooth, cellphone signals, and/or near-field communication (NFC) radio signals.; Par. 2765)
Regarding Claim 30,
The speculation system of claim I wherein the computing subsystem synchronizes communication with one or more data signals or readings from multiple sensors that are in communication with the computing subsystem, (Frank Par2494-In another embodiment, a measurement of affective response corresponding to an event is a value that is a weighted average of a plurality of values obtained utilizing a sensor that measured the user corresponding to the event. Herein, a weighted average of values may be any linear combination of the values. Optionally, each of the plurality of values was acquired at a different time during the instantiation of the event (and/or shortly after it), and may be assigned a possible different weight for the computing of the weighted average.; Par. 2497; 2503)
Regarding Claim 31,
The speculation system of claim I wherein the transmission subsystem includes a transmitter and a receiver, or a combination thereof (Frank Par2765- The network 112 represents one or more networks used to carry the measurements 110 and/or crowd-based results 115 computed based on measurements. It is to be noted that the measurements 110 and/or crowd-based results 115 need not be transmitted via the same network components. Additionally, different portions of the measurements 110 (e.g., measurements of different individual users) may be transmitted using different network components or different network routes. In a similar fashion, the crowd-based results 115 may be transmitted to different users utilizing different network components and/or different network routes.; Par. 2767)
Regarding Claim 32,
The speculation system of claim 1 wherein the transmission subsystem includes an. on-body or aerial transceiver that optionally acts as another sensor on or above the one or more targeted individuals, the on-body or aerial transceiver being operable to communicate with other one or more sensors on one or more targeted individuals. (Frank Par1894- As used herein, the term “electronic device” may refer to any object the uses electricity to operate and/or utilizes electronic circuitry for its operation. In some examples, electronic devices may include a processor (e.g., processor 401). Some non-limiting examples of electronic devices include: phones, smartphones, laptops, tablets, smart watches, head-mounted displays, wearable electronic devices, gaming systems, desktop computers, home theatre systems, and implanted electronic devices. Some electronic devices receive input from the environment or a user utilizing them. For example, a sensor that measures the user, such as an EEG headset, may be considered an electronic device. Some electronic devices produce an output. For example, a television and a stereo system may be considered electronic devices. Many electronic devices both receive inputs and produce outputs. Some of the user interfaces mentioned in this disclosure may also be considered electronic devices. In some embodiments, a user may interact with an electronic device utilizing an operating system of the electronic device and/or via a software agent operating on behalf of the user.; Par. [2384], [(2765]; [2389], [2416])
Regarding Claim 33,
The speculation system of claim wherein the animal data is synchronized, tine- stamped, and tagged with information related to the one or more targeted individuals from which the animal data is collected and the one or more source sensors., which includes at least one characteristic of the one or more source sensors. (Frank Par2515- In some embodiments, labels corresponding to affective values may be acquired when the user is measured with an extended set of sensors. This may enable the more accurate detection of the emotional state of the user. For example, a label for a user may be generated utilizing video images and/or EEG, in addition to heart rate and GSR. Such a label is typically more accurate than using heart rate and GSR alone (without information from EEG or video). Thus, an accurate label may be provided in this case and used to train a predictor that is given an affective value based on heart rate and GSR (but not EEG or video images of the user).; Par. [2593], [2619], [2644];[780])
Regarding Claim 34,
The speculation system of claim 1 wherein the animal data includes metadata that identifies one or more characteristics of the animal data and the one or more source sensors. (Frank Par2593- I In some embodiments, events are identified by a module referred to herein as an event annotator. Optionally, an event annotator is a predictor, and/or utilizes a predictor, to identify events. Optionally, the event annotator generates a description of an event, which may be used for various purposes such as assigning factors to an event, as described in section 24—Factors of Events. Identifying an event, and/or factors of an event, may involve various computational approaches applied to data from various sources, which are both elaborated on further in section 9—Identifying Events.; Par. [2639])
Regarding Claim 35, - Cancelled
Regarding Claim 36,
The speculation system of claim 1 wherein the computing. subsystem or the wagering system execute one or more actions on. the animal data selected from the group consisting of normalizing, time stamping, aggregating, tagging, storing, manipulating, denoising, productizing, enhancing, organizing, visualizing, analyzing, summarizing, replicating, synthesizing, anonymizing, synchronizing, or distributing the animal data. (Frank Par1278- In some embodiments, in order to compute an aftereffect score, the aftereffect scoring module 302 may utilize prior measurements of affective response in order to normalize subsequent measurements of affective response. Optionally, a subsequent measurement of affective response of a user (taken after leaving a location) may be normalized by treating a corresponding prior measurement of affective response the user as a baseline value (the prior measurement being taken before leaving the location or before arriving at it). Optionally, a score computed by such normalization of subsequent measurements represents a change in the emotional response due to being at the location to which the prior and subsequent measurements correspond. Optionally, normalization of a subsequent measurement with respect to a prior measurement may be performed by the baseline normalizer 124 or a different module that operates in a similar fashion.)
Regarding Claim 37,
The speculation system of claim 1 wherein the computing subsystem or the wagering system: (1) communicates directly with one or more systems to monitor, receive, and record at least one request for the predictive indicator, the at least one computed asset, and/or the animal data; (2) provides one or more users requesting access to the predictive indicator, the at least one computed asset, and/or the animal data with an ability to make one or more requests for data; and (3) is operable to send and/or receive data. (Frank Par2466- In some embodiments, measurements may be taken in order to gauge the affective response of users to certain events. Optionally, a protocol may dictate that measurements to certain experiences are to be taken automatically. For example, a protocol governing the operation of a sensor may dictate that every time a user exercises, certain measurements of physiological signals of the user are to be taken throughout the exercise (e.g., heart rate and respiratory rate), and possibly a short duration after that (e.g., during a recuperation period). Alternatively or additionally, measurements of affective response may be taken “on demand”. For example, a software agent operating on behalf of a user may decide that measurements of the user should be taken in order to establish a baseline for future measurements. In another example, the software agent may determine that the user is having an experience for which the measurement of affective response may be useful (e.g., in order to learn a preference of the user and/or in order to contribute the measurement to the computation of a score for an experience). Optionally, an entity that is not a user or a software agent operating on behalf of the user may request that a measurement of the affective response of the user be taken to a certain experience (e.g., by defining a certain window in time during which the user should be measured). Optionally, the request that the user be measured is made to a software agent operating on behalf of the user. Optionally, the software agent may evaluate whether to respond to the request based on an evaluation of the risk to privacy posed by providing the measurement and/or based on the compensation offered for the measurement.; Par. [2755])
Regarding Claim 38,
The speculation system of claim 1 wherein the computing subsystem or the wagering system associates at least one request for the predictive indicator, the at least one computed asset, and/or the animal data with at least one user, group of users, or class of users. (Frank Par2755- In one embodiment, the software agent provides information as a response to a request. For example, the software agent may receive a request for a measurement of the user on behalf whom it operates. In another example, the request is a general request sent to multiple agents, which specifies certain conditions. For example, the request may specify a certain type of experience, time, certain user demographics, and/or a certain situation which the user is in. Optionally, the software responds to the request with the desired information if doing so does not violate a restriction dictated by a policy according to which the software agent operates. For example, the software agent may respond with the information if the risk associated with doing so does not exceed a certain threshold and/or the compensation provided for doing so is sufficient.)
Regarding Claim 39,
The speculation system of claim 1 wherein the computing subsystem or the wagering system is operable to allow one or more users to select at least one characteristic upon which animal data, the at least one computed asset, and/or the predictive indicator is provided. (Frank Par2755- In one embodiment, the software agent provides information as a response to a request. For example, the software agent may receive a request for a measurement of the user on behalf whom it operates. In another example, the request is a general request sent to multiple agents, which specifies certain conditions. For example, the request may specify a certain type of experience, time, certain user demographics, and/or a certain situation which the user is in. Optionally, the software responds to the request with the desired information if doing so does not violate a restriction dictated by a policy according to which the software agent operates. For example, the software agent may respond with the information if the risk associated with doing so does not exceed a certain threshold and/or the compensation provided for doing so is sufficient.)
Regarding Claim 40,
The speculation system of claim 1 wherein the computing subsystem or the wagering system generates simulated data derived from at least a portion of the predictive indicator, the at least one computed asset, and/or the animal data of the one or more targeted individuals or groups of targeted individuals (Frank Par4415-4416- In order to estimate what knowledge the adversary may have from disclosed information, the adversary model learner 838 may comprise and/or utilize an adversary emulator. The adversary emulator is configured to emulate a process performed by an adversary in which the adversary learns one or more models (which are the adversary bias model 835), from disclosed information such as a set of disclosed scores and/or contribution information represented by a contribution matrix C and/or a matrix of factor vectors F. Optionally, the adversary bias model 835 includes information that may be considered private information of users. Optionally, the adversary bias model 835 includes parameters that are bias values of users. Optionally, training the one or more models may be done under the assumption that an adversary has full information (e.g., the scores S and the correct matrices C and/or F). Alternatively, training the one or more models may be done under the assumption that the adversary has partial information (e.g., only some of the scores in S, only some of the values in C and/or F, and/or values of S and C with only limited accuracy).)
Regarding Claim 41,
The speculation system of claim 40 wherein the simulated data is generated utilizing one or more signals or readings from non-animal data as one or more inputs. (Frank Par4415-4416- In order to estimate what knowledge the adversary may have from disclosed information, the adversary model learner 838 may comprise and/or utilize an adversary emulator. The adversary emulator is configured to emulate a process performed by an adversary in which the adversary learns one or more models (which are the adversary bias model 835), from disclosed information such as a set of disclosed scores and/or contribution information represented by a contribution matrix C and/or a matrix of factor vectors F. Optionally, the adversary bias model 835 includes information that may be considered private information of users. Optionally, the adversary bias model 835 includes parameters that are bias values of users. Optionally, training the one or more models may be done under the assumption that an adversary has full information (e.g., the scores S and the correct matrices C and/or F). Alternatively, training the one or more models may be done under the assumption that the adversary has partial information (e.g., only some of the scores in S, only some of the values in C and/or F, and/or values of S and C with only limited accuracy).)
Regarding Claim 42,
The speculation system of claim 40 wherein simulated data is generated utilizing an artificial intelligence technique. (Frank Par. 2726;Par4415-4416- In order to estimate what knowledge the adversary may have from disclosed information, the adversary model learner 838 may comprise and/or utilize an adversary emulator. The adversary emulator is configured to emulate a process performed by an adversary in which the adversary learns one or more models (which are the adversary bias model 835), from disclosed information such as a set of disclosed scores and/or contribution information represented by a contribution matrix C and/or a matrix of factor vectors F. Optionally, the adversary bias model 835 includes information that may be considered private information of users. Optionally, the adversary bias model 835 includes parameters that are bias values of users. Optionally, training the one or more models may be done under the assumption that an adversary has full information (e.g., the scores S and the correct matrices C and/or F). Alternatively, training the one or more models may be done under the assumption that the adversary has partial information (e.g., only some of the scores in S, only some of the values in C and/or F, and/or values of S and C with only limited accuracy).)
Regarding Claim 43,
The speculation system of claim 42 wherein the artificial intelligence technique includes one or more trained neural networks. (Frank Par. 3721- In one embodiment, the function learning module 348 utilizes the machine learning-based trainer 286 to learn parameters of the function 349. Optionally, the machine learning-based trainer 286 utilizes the measurements of the at least ten users to train a model for a predictor that is configured to predict a value of affective response of a user based on an input indicative of an extent to which the user had already experienced the experience. In one example, each measurement of the user taken while having the experience again, after having experienced it before to an extent e, is converted to a sample (e,v), which may be used to train the predictor; where v is an affective value determined based on the measurement. Optionally, when the trained predictor is provided inputs indicative of the extents e1 and e2 (mentioned above), the predictor utilizes the model to predict the values v1 and v2, respectively. Optionally, the model comprises at least one of the following: a regression model, a model utilized by a neural network, a nearest neighbor model, a model for a support vector machine for regression, and a model utilized by a decision tree. Optionally, the parameters of the function 349 comprise the parameters of the model and/or other data utilized by the predictor.)
Regarding Claim 44,
The speculation system of claim 40 wherein the computing subsystem or the wagering system utilizes at least a portion of the simulated data either directly or indirectly: (1) as a market upon which one or more wagers are placed or accepted; (2) to accept one or more wagers; (3) to create, enhance, modify, acquire, offer, or distribute one or more products; (4) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (5) to formulate one or more strategies; (6) to take one or more actions; (7) to mitigate or prevent one or more risks; (8) as one or more signals or readings utilized in one or more simulations. computations, or analyses: (9) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (10) to recommend one or more actions; (11) as one or more core components or supplements to one or more mediums of consumption: (12) in one or more promotions; or (13) a combination thereof (Frank Par. 260- Various embodiments described herein utilize systems whose architecture includes a plurality of sensors and a plurality of user interfaces. This architecture supports various forms of crowd-based recommendation systems in which users may receive information, such as suggestions and/or alerts, which are determined based on measurements of affective response to experiences involving locations. In some embodiments, being crowd-based means that the measurements of affective response are taken from a plurality of users, such as at least three, ten, one hundred, or more users. In such embodiments, it is possible that the recipients of information generated from the measurements may not be the same users from whom the measurements were taken..; Par.[4415],[4664],[4678])
Regarding Claim 45,
The speculation system of claim 40 wherein the computing subsystem or the wagering system applies at least a portion of the simulated data, either directly or indirectly, to create, enhance, or modify the predictive indicator, at least. one computed asset, and/or animal data. (Frank Par. 4415-4417- In order to estimate what knowledge the adversary may have from disclosed information, the adversary model learner 838 may comprise and/or utilize an adversary emulator. The adversary emulator is configured to emulate a process performed by an adversary in which the adversary learns one or more models (which are the adversary bias model 835), from disclosed information such as a set of disclosed scores and/or contribution information represented by a contribution matrix C and/or a matrix of factor vectors F. Optionally, the adversary bias model 835 includes information that may be considered private information of users. Optionally, the adversary bias model 835 includes parameters that are bias values of users. Optionally, training the one or more models may be done under the assumption that an adversary has full information (e.g., the scores S and the correct matrices C and/or F). Alternatively, training the one or more models may be done under the assumption that the adversary has partial information (e.g., only some of the scores in S, only some of the values in C and/or F, and/or values of S and C with only limited accuracy).)
Regarding Claim 46,
The speculation system of claim 45 wherein at least a portion of created, enhanced, or modified predictive indicator, the at least one computed asset, and/or animal data is utilized either directly or indirectly: (J) as a market upon which one or more wagers are placed or accepted: (2) to create, modify, enhance, acquire, offer, or distribute one or more products; (3) to evaluate, calculate, derive, modify, enhance, or communicate one or more predictions, probabilities, or possibilities; (4) to Formulate one or more strategies: (5) to take one or more actions; (6) to mitigate or prevent one or more risks; (7) as one or more signals or readings utilized in one or more simulations, computations, or analyses; (8) as part of one or more simulations, an output of which directly or indirectly engages with one or more users; (9) to recommend one or more actions; (10) as one or more core components or supplements to one or more mediums of consumption; (1 1) in one or more promotions; or (12) a combination thereof. (Frank Par. 260- Various embodiments described herein utilize systems whose architecture includes a plurality of sensors and a plurality of user interfaces. This architecture supports various forms of crowd-based recommendation systems in which users may receive information, such as suggestions and/or alerts, which are determined based on measurements of affective response to experiences involving locations. In some embodiments, being crowd-based means that the measurements of affective response are taken from a plurality of users, such as at least three, ten, one hundred, or more users. In such embodiments, it is possible that the recipients of information generated from the measurements may not be the same users from whom the measurements were taken..; Par. [4415],[4664],[4678)
Regarding Claim 47,
The speculation system of claim 1, wherein the animal data is grouped into one or more classifications with each classification having an associated computed asset or value. (Frank Par. 3845- In another embodiment, the first output and/or the second output may involve clustering of profiles. For example, generating the first output in Step 4 may involve the performing the following steps: (i) clustering the at least some of the users into clusters based on similarities between the profiles of the at least some of users, with each cluster comprising a single user or multiple users with similar profiles; (ii) selecting, based on the profile of the certain first user, a subset of clusters comprising at least one cluster and at most half of the clusters, on average, the profile of the certain first user is more similar to a profile of a user who is a member of a cluster in the subset, than it is to a profile of a user, from among the at least ten users, who is not a member of any of the clusters in the subset; and (iii) selecting at least eight users from among the users belonging to clusters in the subset. Here, the first output is indicative of the identities of the at least eight users. Generating the second output in Step 8 may involve similar steps, mutatis mutandis, to the ones described above.; Par. 4702)
Regarding Claim 48,
The speculation system of claim I wherein upon sending the predictive indicator, the at least one computed asset, and/or animal data to another source, the computing subsystem records one or more characteristics of the predictive indicator, the at least one computed asset, and/or animal data provided as part. of its one or more distributions. (Frank Par. 579-582- In some embodiments, a score computed based on measurements of affective response of users that are logged into a server hosting a virtual environment is indicative of an emotional state of users who are logged into the server. In one example, the score may be indicative of the average mood of the users who are logged in. In another example, the score may be indicative of the average level of enjoyment, happiness, excitement, and/or degree of engagement of the user. Optionally, the score may be based on measurements of one or more sensors that measure physiological signals such as heart rate, heart rate variability, skin conductance, skin temperature, and/or brainwave activity. Optionally, the score may be based on measurements of one or more sensors that measure behavioral cues such as yawning, smiling, and/or frowning.)
Claims 49-51 are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al., US Publication No. 20160170996 A1, [hereinafter Frank], in view of Tran et al., US Publication No. 20170232300A1, [hereinafter Tran] , in further view of Luinge et al., US Publication No. 20080285805A1, [hereinafter Luinge] in further view of Guan, US Publication No. 2013/0217475A1, [hereinafter Guan] and in further view of Brown, US Publication No. 20210302621A1, [hereinafter Brown].
Regarding Claim 49, Frank in view of Tran in further view of Luinge in further view of Guan teach The speculation system of claim 1…
Frank in view of Tran in further view of Luinge in further view of Guan fail to teach the following feature taught by Brown:
wherein the transmission subsystem includes an aerial transceiver for continuous streaming and/or intermittent communication from the one or more source sensors, the aerial transceiver including one or more communication satellites or unmanned aerial vehicles with attached transceiver (Brown Par. 91-“ The data recorded from events of interest as well as the programming steps and results that can be triggered by the event are streamed after the event occurs to the cloud where existing AI tools, using much more powerful servers and memory, can refine those triggers and programming steps to reduce false positive events and refine responses in the street light level programming.”; Par. 90-95).
Frank, Tran, and Luinge are directed to the collection of measurement data via sensors. Guan and Brown improve upon the analysis It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frank in view of Tran in further view of Luinge in further view of Guan, as taught by Brown, by utilizing additional sensor data collection and analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frank in view of Tran in further view of Luinge in further view of Guan with the motivation of sensing and responding to detected activity or an event in a region (Brown Abstract).
Regarding Claim 50, Frank in view of Tran in further view of Luinge in further view of Guan teach The speculation system of claim 1…
Frank in view of Tran in further view of Luinge in further view of Guan fail to teach the following feature taught by Brown:
wherein the transmission subsystem includes a transceiver embedded or integrated as part of a floor or ground, with transmission occurring via direct contact with a surface (Brown Par. 120-“ For instance, the installed location may be a centerline or a point on the centerline of the base station at a bottom surface of the base station when installed on a support member (such as at or near an upper surface of a streetlight). The precise location identifier may identify a location on the base station where a charging signal for use by an unmanned aerial vehicle may be provided. The precise location identifier may be determined, for example, by a surveying operation (such as a laser survey) and may be accurate to a higher degree of accuracy than is possible with global positioning system information.”;).
Frank, Tran, and Luinge are directed to the collection of measurement data via sensors. Guan and Brown improve upon the analysis It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frank in view of Luinge in further view of Guan, as taught by Brown, by utilizing additional sensor data collection and analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frank in view of Tran in further view of Luinge in further view of Guan with the motivation of sensing and responding to detected activity or an event in a region (Brown Abstract).
Regarding Claim 51, Frank in view of Tran in further view of Luinge in further view of Guan teach The speculation system of claim 1…
Frank in view of Tran in further view of Luinge in further view of Guan fail to teach the following feature taught by Brown:
wherein the transmission subsystem includes at least one aerial transceiver configured to relay biometric signals from the one or more source sensors to the computing subsystem (Brown Par. 184-“ In addition, by using artificial intelligence, machine learning, and/or data mining techniques to do for example, facial recognition and other pattern recognition, visual, audio and otherwise, key information may be extracted from data collected by the sensors and other detection components..”).
Frank, Tran, and Luinge are directed to the collection of measurement data via sensors. Guan and Brown improve upon the analysis It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frank in view of Tran in further view of Luinge in further view of Guan, as taught by Brown, by utilizing additional sensor data collection and analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frank in view of Tran in further view of Luinge in further view of Guan with the motivation of sensing and responding to detected activity or an event in a region (Brown Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20180001184A1 to Tran et al.- Par. 2-4-“ In one aspect, an Internet of Thing (IoT) device includes sensors such as a camera; a processor coupled to the light source and the sensor; and a wireless transceiver coupled to the processor. In another aspect, systems and methods disclosed for recommending lifestyle modification for a subject by using a DNA sequencer to generate genetic information; aggregating genetic information, environmental information, treatment data, and treatment response from a patient population; deep learning with a computer to generate at least one computer implemented classifier that predicts disease risks based on the aggregated genetic information, treatment data, and treatment response from a patient population; and recommending lifestyle modification to mitigate the disease risks. In another aspect, a system includes a substance to be consumed by a subject and one or more indicia labeling the substance with: genomic biomarkers; drug exposure and clinical response variability; risk for adverse events; genotype-specific dosing; polymorphic drug target and disposition genes; and treatment based on the biomarker.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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