Patent Application
20250205581
The present disclosure generally relates to automated golf training applications, and more particularly, to an AI-based automated system and method for real-time video recommendations based on predictive analytics of golfer data.
Training people is critical for any sport activity. Training golfers using video is an effective way to improve their skills by providing visual feedback and detailed analysis. Conventional approach for incorporation of video into golf training may include a high-quality camera or smartphone with slow-motion capabilities. Tripods can ensure stability and consistent angles. The various angles for recording may be selected:
Down-the-line: Behind the golfer, aligned with the target line;
Face-on: Perpendicular to the golfer's chest, capturing body movement;
Overhead or Drone (optional): For advanced analysis of swing paths and course strategy.
The conventional applications employ recording of the swing using markers such as alignment sticks or contrasting markers to highlight stance, ball position, and swing path. Multiple swings are recorded with the same club for accurate comparisons. The video is analyzed using key areas to focus on:
Setup: Posture, grip, alignment, and ball position.
Backswing: Shoulder turn, hip rotation, and club angle.
Downswing: Weight transfer, arm position, and clubface orientation.
Impact: Ball contact, body alignment, and club position.
Follow-through: Balance and completion of the swing.
Various software applications exist, such as for example, V1 Golf, Coach's Eye, or Hudl Technique allowing for slow-motion, drawing, and side-by-side comparisons. The feedback may include visual comparisons-showing the golfer their swing alongside that of professionals to highlight differences. Key metrics include measurable improvements, such as swing speed, angle, or ball flight, etc. The application may suggest specific drills based on findings (e.g., weight transfer drills if there is an issue with balance) and encourage practice with feedback after every few swings to ensure corrections are applied. The application may record progress periodically to track improvement and refine techniques and compare before-and-after footage to highlight growth.
However, the existing systems use only the golfer's video and do not provide or generate video instructions based on predictive AI-based analytics of the golfer's non-video data.
Accordingly, AI-based automated system and method for real-time video recommendations based on predictive analytics of golfer data are desired.
This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter's scope.
One embodiment of the present disclosure provides a system for automated analytics of data related to a golfer and video recommendations including a processor of a golfer analytics server (GAS) node configured to host a machine learning (ML) module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a target golfer profile data comprising golfer-related performance metrics and ambient data associated with the performance metrics; parse the target golfer profile data to derive a plurality of key classifying features; query a local or remote database to retrieve local historical golfer-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical golfer-related data; provide the at least one classifier feature vector to the ML module configured to generate an instructional video predictive model for producing at least one instructional video recommendation parameter; and select an instructional video for rendering to the at least one user-entity node based on the at least one instructional video recommendation parameter.
Another embodiment of the present disclosure provides a method that includes one or more of: receiving a target golfer profile data comprising golfer-related performance metrics and ambient data associated with the performance metrics; parsing the target golfer profile data to derive a plurality of key classifying features; querying a local database to retrieve local historical golfer-related data based on the plurality of key classifying features; generating at least one classifier feature vector based on the plurality of key classifying features and the local historical golfer-related data; providing the at least one classifier feature vector to the ML module configured to generate an instructional video predictive model for producing at least one instructional video recommendation parameter; and selecting an instructional video for rendering to the at least one user-entity node based on the at least one instructional video recommendation parameter.
Another embodiment of the present disclosure provides a computer-readable medium including instructions for: receiving a target golfer profile data comprising golfer-related performance metrics and ambient data associated with the performance metrics; parsing the target golfer profile data to derive a plurality of key classifying features; querying a local database to retrieve local historical golfer-related data based on the plurality of key classifying features; generating at least one classifier feature vector based on the plurality of key classifying features and the local historical golfer-related data; providing the at least one classifier feature vector to the ML module configured to generate an instructional video predictive model for producing at least one instructional video recommendation parameter; and selecting an instructional video for rendering to the at least one user-entity node based on the at least one instructional video recommendation parameter.
Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings may contain representations of various trademarks and copyrights owned by the Applicant. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicant. The Applicant retains and reserves all rights in its trademarks and copyrights included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings:
As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.
Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.
Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.
Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such a term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term-differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.
Regarding applicability of 35 U.S.C. § 112, 16, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase “means for” or “step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.
Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”
The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subject matter disclosed under the header.
The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of instructional video predictive parameters, embodiments of the present disclosure are not limited to use only in this context.
The following definitions may be used in the present disclosure.
“A feature vector” refers to a mathematical representation of the key classifying features, typically in the form of an n-dimensional vector where each dimension corresponds to a specific feature. This vector is used as input for machine learning algorithms to categorize or analyze the digital campaign data.
“An instructional video predictive model” refers to a machine learning model trained on historical golfers'-related data to predict various outcomes or characteristics selection of instructional videos. This model takes the feature vector as input and outputs predictions about a set of video recommendation parameters.
“Pre-set threshold value” refers to a predetermined numerical value used as a decision boundary for triggering actions within the disclosed system. This value may be set based on historical data, expert knowledge, or specific data processing requirements.
The present disclosure provides a system, method and computer-readable medium for AI-based automated real-time video recommendations based on predictive analytics of golfer data. In one embodiment, the system overcomes the limitations of existing methods of video-based golfer training by employing fine-tuned models to extract and process the golfer information, irrespective of data format, style, or data type. By leveraging the capabilities of the pre-trained predictive models, the disclosed approach offers a significant improvement over existing solutions discussed above in the background section.
In one embodiment of the present disclosure, the system provides for an AI and machine learning (ML)-generated instructional video recommendation parameter based on analysis of golfer's profile data. In one embodiment, an automated instructional video predictive model may be generated to provide for instructional video recommendation parameters associated with the golfer being analyzed or trained. The automated instructional video predictive model may use historical golfer-related data collected at the current golfing facility location (or site) and at golfing/training facilities of the same type located within a certain range from the current location or even located globally. The relevant historical golfers'-related data may include data related to other golfers having the same parameters such as height, weight, age, gender, playing conditions, language of the jurisdiction of the golfing facility, locations, etc. The relevant golfer-related data may indicate successfully selected instructional videos based on analytics and associated results.
In one embodiment, to enhance this process, the system may integrate advanced technologies discussed above, such as Artificial Intelligence (AI) and machine-learning (ML) and Blockchain. The AI may be leveraged for several key functions discussed herein.
Additionally, the disclosed video recommendation system may incorporate Blockchain technology to ensure the transparency and immutability of transactions, providing a secure and trustworthy platform. By embedding these advanced technologies, the disclosed automated test/training system, advantageously, offers a sophisticated and secure solution.
As discussed above, in one disclosed embodiment, the AI/ML technology may be combined with a blockchain technology for secure use of the golfer-related data and video recommendations-related data. In one embodiment, the ML module may use instructional video predictive model(s) that use an artificial neural network (ANN) to generate predictive video recommendation parameters. The use of specially trained ANNs provides a number of improvements over traditional methods of analyzing data received from the golfer being trained, including more accurate prediction of what additional or leading video recommendations need to be generated in the future. The application further provides methods for training the ANN that leads to a more accurate instructional video predictive model(s).
In one embodiment, the ANN can be implemented by means of computer-executable instructions, hardware, or a combination of the computer-executable instructions and hardware. In one embodiment, neurons of the ANN may be represented by a register, a microprocessor configured to process input signals. Each neuron produces an output, or activation, based on an activation function that uses the outputs of the previous layer and a set of weights as inputs. Each neuron in a neuron array may be connected to another neuron via a synaptic circuit. A synaptic circuit may include a memory for storing a synaptic weight. A proposed ANN may be implemented as a Deep Neural Network having an input layer, an output layer, and several fully connected hidden layers. The proposed ANN may be particularly useful in video recommendation parameters generation because the ANN can effectively extract features from the golfer's profile data in linear and non-linear relationships. In some embodiments, the proposed ANN may be implemented by an application-specific integrated circuit (ASIC). The ASICs may be specially designed and configured for a specific AI application and provide superior computing capabilities and reduced electricity and computational resources consumption compared to the traditional CPUs.
Referring to
The GAS node 102 may query a local golfer database 103 for the historical local golfer-related data based on the target golfer profile data of the golfer 111 associated with the current user entity 101 node. The GAS node 102 may acquire relevant remote golfers'-related data from a remote database 106 residing on the cloud server 105. The golfers'-related data in the database 106 may be collected from other golf courses or golf training facilities. The remote golfers' data may be collected from the golfers of the same (or similar) level, age, gender, location, language, race, etc. as the local target golfer 111 based on the golfer 111 profile.
The GAS node 102 may generate a feature vector or classifier data based on the target golfer profile data and the collected heuristics data (i.e., pre-stored local data 103 and remote data 106). The GAS node 102 may ingest the feature vector/classifier data into an AI/ML module 107. The AI/ML module 107 may generate an instructional video predictive model(s) 108 based on the feature vector/classifier data to predict instructional video recommendation parameters for automatically selecting an instructional video(s) for rendering to the user-entity node 101 associated with the target golfer 111. The instructional video recommendation parameters may be further analyzed by the GAS node 102 prior to selection of the video(s) to be rendered. Once the video selection is recorded, the entire or partial data may be analyzed to generate a feedback report by the AI/ML module 107 based on the outputs of the instructional video predictive model(s) 108.
Referring to
The GAS node 102 may query a local golfer database 103 for the historical local golfer-related data based on the target golfer profile data of the golfer 111 associated with the current user entity 101 node. The GAS node 102 may acquire relevant remote golfers'-related data from a remote database 106 residing on the cloud server 105. The golfers'-related data in the database 106 may be collected from other golf courses or golf training facilities. The remote golfers' data may be collected from the golfers of the same (or similar) level, age, gender, location, language, race, etc. as the local target golfer 111 based on the golfer 111 profile.
The GAS node 102 may generate a feature vector or classifier data based on the target golfer profile data and the collected heuristics data (i.e., pre-stored local data 103 and remote data 106). The GAS node 102 may ingest the feature vector/classifier data into an AI/ML module 107. The AI/ML module 107 may generate an instructional video predictive model(s) 108 based on the feature vector/classifier data to predict instructional video recommendation parameters for automatically selecting an instructional video(s) for rendering to the user-entity node 101 associated with the target golfer 111. The instructional video recommendation parameters may be further analyzed by the GAS node 102 prior to selection of the video(s) to be rendered. Once the video selection is recorded, the entire or partial data may be analyzed to generate a feedback report by the AI/ML module 107 based on the outputs of the instructional video predictive model(s) 108.
In one embodiment, the GAS node 102 may receive the instructional video recommendation parameters from a permissioned blockchain 110 ledger 109 based on a consensus from the user node(s) 101. Additionally, confidential historical golfer-related information and previous golfers-related metrics data may also be acquired from the permissioned blockchain 110. The newly acquired golfer performance-related data with corresponding predicted video recommendation parameters data may be also recorded on the ledger 109 of the blockchain 110 so it can be used as training data for the predictive instructional video predictive model(s) 108.
In this implementation the GAS node 102, the cloud server 105, the user entity nodes 101 may serve as blockchain 110 peer nodes. In one embodiment, local data from the database 103 and remote data from the database 106 may be duplicated on the blockchain ledger 109 for higher security of storage.
The AI/ML module 107 may generate a predictive model(s) 108 to predict the instructional video recommendation parameters in response to the specific relevant pre-stored golfer-related data acquired from the blockchain 110 ledger 109. This way, the current video recommendation parameters may be predicted based not only on the current user entity 101-related data, but also based on the previously collected heuristics. This way, the most optimal way of training the golfer 111, for the most likely progress of his game may be included into the feedback report. After the data processing and the feedback report generation is completed, the related documents may be converted into unique secure NFT assets to be recorded on the blockchain to be used for future predictive models' training.
In one embodiment, as a second round of approval, a blockchain consensus may be achieved among the user entities 101 in order to approve the feedback report generated by the GAS node 102.
Referring to
The GAS node 102 is configured to host an AI/ML module 107. As discussed above with respect to
The AI/ML module 107 may generate a predictive model(s) 108 based on the received target golfer profile data 202 provided by the GAS node 102. As discussed above, the AI/ML module 107 may provide predictive outputs data in the form of instructional video recommendation parameters for automatic selection of the instructional video(s). In one embodiment, the GAS node 102 may process the predictive outputs data received from the AI/ML module 107 to generate video generation or update recommendations.
In one embodiment, the GAS node 102 may continually monitor the target golfer profile data and may detect a parameter that deviates from a previous recorded parameter (or from a median reading value) by a margin that exceeds a threshold value pre-set for this particular parameter. For example, if Golfer's 111 performance metrics change significantly, this may cause a change in instructional video recommendations for selection of the instructional video. Accordingly, once the threshold is met or exceeded by at least one parameter of the golfer-related data, the GAS node 102 may provide the currently acquired user golfer-related parameter to the AI/ML module 107 to generate an updated instructional video recommendation parameter(s) based on the current target golfer 111-related data.
While this example describes in detail only one GAS node 102, multiple such nodes may be connected to the network and to the blockchain 110. It should be understood that the GAS node 102 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the GAS node 102 disclosed herein. The GAS node 102 may be a computing device or a server computer, or the like, and may include a processor 204, which may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device. Although a single processor 204 is depicted, it should be understood that the GAS node 102 may include multiple processors, multiple cores, or the like, without departing from the scope of the GAS node 102 system.
The GAS node 102 may also include a non-transitory computer readable medium 212 that may have stored thereon machine-readable instructions executable by the processor 204. Examples of the machine-readable instructions are shown as 214-224 and are further discussed below. Examples of the non-transitory computer readable medium 212 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 212 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device.
The processor 204 may fetch, decode, and execute the machine-readable instructions 214 to receive a target golfer 111 profile data including golfer-related performance metrics and ambient data associated with the performance metrics (
The processor 204 may fetch, decode, and execute the machine-readable instructions 222 to provide the at least one classifier feature vector to the ML module 107 configured to generate an instructional video predictive model 108 for producing at least one instructional video recommendation parameter. The processor 204 may fetch, decode, and execute the machine-readable instructions 224 to select an instructional video for rendering to the at least one user-entity node 101 based on the at least one instructional video recommendation parameter.
As a non-limiting example, the consensual approval of the golfer performance feedback report may be associated with a request for additional data such as proof of the instructional video viewing and golf course completion, etc. The permissioned blockchain 110 may be configured to use one or more smart contracts that manage transactions for multiple participating nodes and for recording the transactions on the ledger 109.
Referring to
With reference to
At block 308, the processor 204 may generate at least one classifier feature vector based on the plurality of key classifying features and the local historical golfer-related data. At block 310, the processor 204 may provide the at least one classifier feature vector to the ML module configured to generate an instructional video predictive model for producing at least one instructional video recommendation parameter. At block 312, the processor 204 may select an instructional video for rendering to the at least one user-entity node based on the at least one instructional video recommendation parameter.
Note that ambient data may include any of: golf course geometry data; weather at a time of a game; latitude and longitude of a golf shot and wind at a time of a game. The local historical golfer-related data may include any of: video efficacy and golfer engagement metrics based on historical video viewing and subsequent golfer performance metrics; golfer performance changes based on recorded golfer profiles; and historical golfers' profiles of golfers having the same physical golfer characteristics as the target golfer.
Referring to
With reference to
At block 318, the processor 204 may retrieve remote historical golfers'-related data from at least one remote database based on the plurality of key classifying features, wherein the remote historical golfers'-related data is collected at other golf courses from golfers having the same characteristics as the target golfer. At block 319, the processor 204 may generate the at least one classifier feature vector based on the plurality of key classifying features and the local historical golfer-related data combined with the remote historical golfers'-related data. At block 320, the processor 204 may continuously monitor target golfer engagement metrics data to determine if at least one value of target golfer engagement metrics data parameters deviates from a previous value of a corresponding target golfer engagement metrics data parameter value by a margin exceeding a pre-set threshold value. At block 321, the processor 204 may, responsive to the at least one value of the target golfer engagement metrics data parameters deviating from the previous value of the corresponding target golfer engagement metrics data parameter value by a margin exceeding a pre-set threshold value, generate an updated classifier feature vector and select an instructional video for rendering to the at least one user-entity node based on the at least one at least one instructional video recommendation parameter produced by the instructional video predictive model in response to the updated classifier feature vector.
At block 322, the processor 204 may record the target golfer profile data and at least one corresponding instructional video recommendation parameter on a permissioned blockchain ledger. At block 323, the processor 204 may retrieve the at least one instructional video recommendation parameter from the permissioned blockchain responsive to a request from at least one user-entity node onboarded onto the permissioned blockchain.
At block 324, the processor 204 may execute a smart contract to generate at least one NFT including data corresponding to the at least one instructional video recommendation parameter along with the target golfer profile data on the permissioned blockchain.
The instructional video recommendation parameters used in training data sets may be stored in a centralized local database (such as one used for storing local data 103 depicted in
In another embodiment, the AI/ML module 107 may use a decentralized storage such as a blockchain 110 (see
This application utilizes a permissioned (private) blockchain that operates arbitrary, programmable logic, tailored to a decentralized storage scheme and referred to as “smart contracts” or “chaincodes.” In some cases, specialized chaincodes may exist for management functions and parameters which are referred to as system chaincodes. The application can further utilize smart contracts that are trusted distributed applications which leverage tamper-proof properties of the blockchain database and an underlying agreement between nodes, which is referred to as an endorsement or endorsement policy. Blockchain transactions associated with this application can be “endorsed” before being committed to the blockchain while transactions, which are not endorsed, are disregarded. An endorsement policy allows chaincodes to specify endorsers for a transaction in the form of a set of peer nodes that are necessary for endorsement. When a client sends the transaction to the peers specified in the endorsement policy, the transaction is executed to validate the transaction. After a validation, the transactions enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed transactions grouped into blocks.
In the example depicted in
This can significantly reduce the collection time needed by the host platform 420 when performing predictive model training. For example, using smart contracts, data can be directly and reliably transferred straight from its place of origin (e.g., from the GAS node 102 or from the databases 103 and 106 depicted in
Furthermore, training of the machine learning model on the collected data may take rounds of refinement and testing by the host platform 420. Each round may be based on additional data or data that was not previously considered to help expand the knowledge of the machine learning model. In 402, the different training and testing steps (and the data associated therewith) may be stored on the blockchain 110 by the host platform 420. Each refinement of the machine learning model (e.g., changes in variables, weights, etc.) may be stored on the blockchain 110. This, advantageously, provides verifiable proof of how the model was trained and what data was used to train the model. Furthermore, when the host platform 420 has achieved a finally trained model, the resulting model itself may be stored on the blockchain 110.
After the model has been trained, it may be deployed to a live environment where it can make recommendation-related predictions/decisions based on the execution of the final trained machine learning model using the prediction parameters. In this example, data fed back from the asset 430 may be input into the machine learning model and may be used to make event predictions such as instructional video recommendation parameters based on the recorded golfer-related data. Determinations made by the execution of the machine learning model (e.g., approval of feedback reports, etc.) at the host platform 420 may be stored on the blockchain 110 to provide auditable/verifiable proof. As one non-limiting example, the machine learning model may predict a future change of a part of the asset 430 (the instructional video recommendation parameters). The data behind this decision may be stored by the host platform 420 on the blockchain 110.
As discussed above, in one embodiment, the features and/or the actions described and/or depicted herein can occur on or with respect to the blockchain 110. The above embodiments of the present disclosure may be implemented in hardware, in computer-readable instructions executed by a processor, in firmware, or in a combination of the above. The computer computer-readable instructions may be embodied on a computer-readable medium, such as a storage medium. For example, the computer computer-readable instructions may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory (“CD-ROM”), or any other form of storage medium known in the art.
An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (“ASIC”). In the alternative embodiment, the processor and the storage medium may reside as discrete components. For example,
Mobile computing device, such as, but is not limited to, a laptop, a tablet, a smartphone, a drone, a wearable, an embedded device, a handheld device, an Arduino, an industrial device, or a remotely operable recording device;
A supercomputer, an exa-scale supercomputer, a mainframe, or a quantum computer;
A minicomputer, wherein the minicomputer computing device comprises, but is not limited to, an IBM AS500/iSeries/System I, A DEC VAX/PDP, a HP3000, a Honeywell-Bull DPS, a Texas Instruments TI-990, or a Wang Laboratories VS Series;
A microcomputer, wherein the microcomputer computing device comprises, but is not limited to, a server, wherein a server may be rack mounted, a workstation, an industrial device, a raspberry pi, a desktop, or an embedded device;
The GAS node 102 (see
Embodiments of the present disclosure may comprise a computing device having a central processing unit (CPU) 520, a bus 530, a memory unit 550, a power supply unit (PSU) 550, and one or more Input/Output (I/O) units. The CPU 520 coupled to the memory unit 550 and the plurality of I/O units 560 via the bus 530, all of which are powered by the PSU 550. It should be understood that, in some embodiments, each disclosed unit may actually be a plurality of such units for the purposes of redundancy, high availability, and/or performance. The combination of the presently disclosed units is configured to perform the stages of any method disclosed herein.
Consistent with an embodiment of the disclosure, the aforementioned CPU 520, the bus 530, the memory unit 550, a PSU 550, and the plurality of I/O units 560 may be implemented in a computing device, such as computing device 500. Any suitable combination of hardware, software, or firmware may be used to implement the aforementioned units. For example, the CPU 520, the bus 530, and the memory unit 550 may be implemented with computing device 500 or any of other computing devices 500, in combination with computing device 500. The aforementioned system, device, and components are examples and other systems, devices, and components may comprise the aforementioned CPU 520, the bus 530, the memory unit 550, consistent with embodiments of the disclosure.
At least one computing device 500 may be embodied as any of the computing elements illustrated in all of the attached figures, including the GAS node 102 (
With reference to
A system consistent with an embodiment of the disclosure the computing device 500 may include the clock module 510 may be known to a person having ordinary skill in the art as a clock generator, which produces clock signals. Clock signal is a particular type of signal that oscillates between a high and a low state and is used like a metronome to coordinate actions of digital circuits. Most integrated circuits (ICs) of sufficient complexity use a clock signal in order to synchronize different parts of the circuit, cycling at a rate slower than the worst-case internal propagation delays. The preeminent example of the aforementioned integrated circuit is the CPU 520, the central component of modern computers, which relies on a clock. The only exceptions are asynchronous circuits such as asynchronous CPUs. The clock 510 can comprise a plurality of embodiments, such as, but not limited to, single-phase clock which transmits all clock signals on effectively 1 wire, two-phase clock which distributes clock signals on two wires, each with non-overlapping pulses, and four-phase clock which distributes clock signals on 5 wires.
Many computing devices 500 use a “clock multiplier” which multiplies a lower frequency external clock to the appropriate clock rate of the CPU 520. This allows the CPU 520 to operate at a much higher frequency than the rest of the computer, which affords performance gains in situations where the CPU 520 does not need to wait on an external factor (like memory 550 or input/output 560). Some embodiments of the clock 510 may include dynamic frequency change, where the time between clock edges can vary widely from one edge to the next and back again.
A system consistent with an embodiment of the disclosure the computing device 500 may include the CPU unit 520 comprising at least one CPU Core 521. A plurality of CPU cores 521 may comprise identical CPU cores 521, such as, but not limited to, homogeneous multi-core systems. It is also possible for the plurality of CPU cores 521 to comprise different CPU cores 521, such as, but not limited to, heterogeneous multi-core systems, big.LITTLE systems and some AMD accelerated processing units (APU). The CPU unit 520 reads and executes program instructions which may be used across many application domains, for example, but not limited to, general purpose computing, embedded computing, network computing, digital signal processing (DSP), and graphics processing (GPU). The CPU unit 520 may run multiple instructions on separate CPU cores 521 at the same time. The CPU unit 520 may be integrated into at least one of a single integrated circuit die and multiple dies in a single chip package. The single integrated circuit die and multiple dies in a single chip package may contain a plurality of other aspects of the computing device 500, for example, but not limited to, the clock 510, the CPU 520, the bus 530, the memory 550, and I/O 560.
The CPU unit 520 may contain cache 522 such as, but not limited to, a level 1 cache, level 2 cache, level 3 cache or combination thereof. The aforementioned cache 522 may or may not be shared amongst a plurality of CPU cores 521. The cache 522 sharing comprises at least one of message passing and inter-core communication methods may be used for the at least one CPU Core 521 to communicate with the cache 522. The inter-core communication methods may comprise, but not limited to, bus, ring, two-dimensional mesh, and crossbar. The aforementioned CPU unit 520 may employ symmetric multiprocessing (SMP) design.
The plurality of the aforementioned CPU cores 521 may comprise soft microprocessor cores on a single field programmable gate array (FPGA), such as semiconductor intellectual property cores (IP Core). The plurality of CPU cores 521 architecture may be based on at least one of, but not limited to, Complex instruction set computing (CISC), Zero instruction set computing (ZISC), and Reduced instruction set computing (RISC). At least one of the performance-enhancing methods may be employed by the plurality of the CPU cores 521, for example, but not limited to Instruction-level parallelism (ILP) such as, but not limited to, superscalar pipelining, and Thread-level parallelism (TLP).
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ a communication system that transfers data between components inside the aforementioned computing device 500, and/or the plurality of computing devices 500. The aforementioned communication system will be known to a person having ordinary skill in the art as a bus 530. The bus 530 may embody internal and/or external plurality of hardware and software components, for example, but not limited to a wire, optical fiber, communication protocols, and any physical arrangement that provides the same logical function as a parallel electrical bus. The bus 530 may comprise at least one of, but not limited to a parallel bus, wherein the parallel bus carry data words in parallel on multiple wires, and a serial bus, wherein the serial bus carry data in bit-serial form. The bus 530 may embody a plurality of topologies, for example, but not limited to, a multidrop/electrical parallel topology, a daisy chain topology, and a connected by switched hubs, such as USB bus. The bus 530 may comprise a plurality of embodiments, for example, but not limited to:
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ hardware integrated circuits that store information for immediate use in the computing device 500, known to the person having ordinary skill in the art as primary storage or memory 550. The memory 550 operates at high speed, distinguishing it from the non-volatile storage sub-module 561, which may be referred to as secondary or tertiary storage, which provides slow-to-access information but offers higher capacities at lower cost. The contents contained in memory 550, may be transferred to secondary storage via techniques such as, but not limited to, virtual memory and swap. The memory 550 may be associated with addressable semiconductor memory, such as integrated circuits consisting of silicon-based transistors, used for example as primary storage but also other purposes in the computing device 500. The memory 550 may comprise a plurality of embodiments, such as, but not limited to volatile memory, non-volatile memory, and semi-volatile memory. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned memory:
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the communication sub-module 562 as a subset of the I/O 560, which may be referred to by a person having ordinary skill in the art as at least one of, but not limited to, computer network, data network, and network. The network allows computing devices 500 to exchange data using connections, which may be known to a person having ordinary skill in the art as data links, between network nodes. The nodes comprise network computer devices 500 that originate, route, and terminate data. The nodes are identified by network addresses and can include a plurality of hosts consistent with the embodiments of a computing device 500. The aforementioned embodiments include, but not limited to personal computers, phones, servers, drones, and networking devices such as, but not limited to, hubs, switches, routers, modems, and firewalls.
Two nodes can be networked together, when one computing device 500 is able to exchange information with the other computing device 500, whether or not they have a direct connection with each other. The communication sub-module 562 supports a plurality of applications and services, such as, but not limited to World Wide Web (WWW), digital video and audio, shared use of application and storage computing devices 500, printers/scanners/fax machines, email/online chat/instant messaging, remote control, distributed computing, etc. The network may comprise a plurality of transmission mediums, such as, but not limited to conductive wire, fiber optics, and wireless. The network may comprise a plurality of communications protocols to organize network traffic, wherein application-specific communications protocols are layered, may be known to a person having ordinary skill in the art as carried as payload, over other more general communications protocols. The plurality of communications protocols may comprise, but not limited to, IEEE 802, Ethernet, Wireless LAN (WLAN/Wi-Fi), Internet Protocol (IP) suite (e.g., TCP/IP, UDP, Internet Protocol version 4 [IPv4], and Internet Protocol version 6 [IPv6]), Synchronous Optical Networking (SONET)/Synchronous Digital Hierarchy (SDH), Asynchronous Transfer Mode (ATM), and cellular standards (e.g., Global System for Mobile Communications [GSM], General Packet Radio Service [GPRS], Code-Division Multiple Access [CDMA], and Integrated Digital Enhanced Network [IDEN]).
The communication sub-module 562 may comprise a plurality of size, topology, traffic control mechanism and organizational intent. The communication sub-module 562 may comprise a plurality of embodiments, such as, but not limited to:
The aforementioned network may comprise a plurality of layouts, such as, but not limited to, bus network such as ethernet, star network such as Wi-Fi, ring network, mesh network, fully connected network, and tree network. The network can be characterized by its physical capacity or its organizational purpose. Use of the network, including user authorization and access rights, differ accordingly. The characterization may include, but not limited to nanoscale network, Personal Area Network (PAN), Local Area Network (LAN), Home Area Network (HAN), Storage Area Network (SAN), Campus Area Network (CAN), backbone network, Metropolitan Area Network (MAN), Wide Area Network (WAN), enterprise private network, Virtual Private Network (VPN), and Global Area Network (GAN).
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the sensors sub-module 563 as a subset of the I/O 560. The sensors sub-module 563 comprises at least one of the devices, modules, and subsystems whose purpose is to detect events or changes in its environment and send the information to the computing device 500. Sensors are sensitive to the measured property, are not sensitive to any property not measured, but may be encountered in its application, and do not significantly influence the measured property. The sensors sub-module 563 may comprise a plurality of digital devices and analog devices, wherein if an analog device is used, an Analog to Digital (A-to-D) converter must be employed to interface the said device with the computing device 500. The sensors may be subject to a plurality of deviations that limit sensor accuracy. The sensors sub-module 563 may comprise a plurality of embodiments, such as, but not limited to, chemical sensors, automotive sensors, acoustic/sound/vibration sensors, electric current/electric potential/magnetic/radio sensors, environmental/weather/moisture/humidity sensors, flow/fluid velocity sensors, ionizing radiation/particle sensors, navigation sensors, position/angle/displacement/distance/speed/acceleration sensors, imaging/optical/light sensors, pressure sensors, force/density/level sensors, thermal/temperature sensors, and proximity/presence sensors. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned sensors:
Chemical sensors, such as, but not limited to, breathalyzer, carbon dioxide sensor, carbon monoxide/smoke detector, catalytic bead sensor, chemical field-effect transistor, chemiresistor, electrochemical gas sensor, electronic nose, electrolyte-insulator-semiconductor sensor, energy-dispersive X-ray spectroscopy, fluorescent chloride sensors, holographic sensor, hydrocarbon dew point analyzer, hydrogen sensor, hydrogen sulfide sensor, infrared point sensor, ion-selective electrode, nondispersive infrared sensor, microwave chemistry sensor, nitrogen oxide sensor, olfactometer, optode, oxygen sensor, ozone monitor, pellistor, pH glass electrode, potentiometric sensor, redox electrode, zinc oxide nanorod sensor, and biosensors (such as nano-sensors).
Automotive sensors, such as, but not limited to, air flow meter/mass airflow sensor, air-fuel ratio meter, AFR sensor, blind spot monitor, engine coolant/exhaust gas/cylinder head/transmission fluid temperature sensor, hall effect sensor, wheel/automatic transmission/turbine/vehicle speed sensor, airbag sensors, brake fluid/engine crankcase/fuel/oil/tire pressure sensor, camshaft/crankshaft/throttle position sensor, fuel/oil level sensor, knock sensor, light sensor, MAP sensor, oxygen sensor (02), parking sensor, radar sensor, torque sensor, variable reluctance sensor, and water-in-fuel sensor.
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the peripherals sub-module 562 as a subset of the I/O 560. The peripheral sub-module 565 comprises ancillary devices used to put information into and get information out of the computing device 500. There are 3 categories of devices comprising the peripheral sub-module 565, which exist based on their relationship with the computing device 500, input devices, output devices, and input/output devices. Input devices send at least one of data and instructions to the computing device 500. Input devices can be categorized based on, but not limited to:
Output devices provide output from the computing device 500. Output devices convert electronically generated information into a form that can be presented to humans. Input/output devices that perform both input and output functions. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting embodiments of the aforementioned peripheral sub-module 565:
Output Devices may further comprise, but not be limited to:
Printers, such as, but not limited to, inkjet printers, laser printers, 3D printers, solid ink printers and plotters.
Input/Output Devices may further comprise, but not be limited to, touchscreens, networking device (e.g., devices disclosed in network 562 sub-module), data storage device (non-volatile storage 561), facsimile (FAX), and graphics/sound cards.
All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
While the specification includes examples, the disclosure's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as examples for embodiments of the disclosure.
Insofar as the description above and the accompanying drawing disclose any additional subject matter that is not within the scope of the claims below, the disclosures are not dedicated to the public and the right to file one or more applications to claims such additional disclosures is reserved.
This application claims priority to Provisional Patent Application No. 63/613,246 entitled “Video Recommendation based on Dynamic Golf Swing Analytics” filed on Dec. 20, 2023 and incorporated herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63613246 | Dec 2023 | US |
