DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
This communication is in response to Application No. 19/063,165 filed on 2/25/2025.
Claims 1-36 are currently pending and have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/25/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents 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 1-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, 13, 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a method, computer program product and system for predicting fatigue and injury risk.
The limitations of capturing motions performed by a user; predicting fatigue and injury risk of said user […] by comparing said captured motions to predefined thresholds of range of motion constraints based on biomechanical parameters of said user from a […] twin model of said user; and providing feedback based on said predicted fatigue and injury risk of said user., as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a program product comprising one or more computer readable storage mediums, a memory, a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the program product comprising one or more computer readable storage mediums, a memory, a processor, this claim encompasses a person looking at motion of a user, predicting fatigue by comparing motions and providing feedback in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a digital, program product comprising one or more computer readable storage mediums, a memory, a processor, that implements the identified abstract idea. The digital, program product comprising one or more computer readable storage mediums, a memory, a processor is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using the trained machine learning model to predict. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of digital, program product comprising one or more computer readable storage mediums, a memory, a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to predict was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible..
Dependent Claims
Claims 2-12, 14-24, 26-36 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claims 2, 14, 26 merely describes feedback. Claims 6, 18, 30 merely describes motions. Claims 7, 19, 31 merely describes biomechanical parameters. Claims 8, 20, 32 merely describes comparison. Claim 10, 22, 34 merely describes predefined thresholds of range of motion constraints. Claim 11, 23, 35 merely describes saving motions, identifying trends, modifying baselines based on trends. Claims 12, 24, 36 merely describes feedback.
Claims 3, 15, 27 also includes the additional element of “a camera-based optical tracking device.” Claims 4, 16, 28 also includes the additional element of “an inertia measurement unit.” Claims 5, 17, 29 also includes the additional element of “electromyography sensor.” These additional elements taken in turn are analyzed the same as a computer in the independent claim (mere instructions to apply the exception using a generic computer component or merely using a computer as a tool to apply the abstract idea) and does not provide a practical application or significantly more for the same reasons. Claims 3, 15, 27 merely describes motion capture using a camera-based optical tracking device. Claims 4, 16, 28 merely describes using a inertia measurement unit. Claims 5, 17, 29 merely describes using an electromyography sensor.
Claims 3, 15, 27 also includes the additional element of “a machine learning model” which is analyzed the same as in the independent claim (Implementing an abstract idea using a generic computer or components) and does not provide a practical application or significantly more for the same reasons. Claims 8, 20, 32 merely describes comparison using the ml model. Claim 9, 21, 33 merely describes the model.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6, 8, 10, 12-15, 18, 20, 22, 24-27, 30, 32, 34, 36 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam (US 20240070854) in view of Theimer (US 11172818) in view of Ring (WO 2016135560)
CLAIM 1, 13, 25
El-Sallam teaches A computer-implemented method for predicting fatigue and injury risk to users, the method comprising: (El-Sallam para 6 teaches systems and methods for the capture, tracking, analysis and assessment of the human movements. Para 66-67 teaches memory, system and computing mean analyzing for human movement.)
capturing motions performed by a user; (El-Sallam para 32 teaches motion analysis using sensor, videos, and stream of movement data)
[…] based on biomechanical parameters of said user from a digital twin model of said user; and (El-Sallam para 77 teaches initializing a valid 3D anatomical skeleton. Para 78 teaches dividing body into different nodes, links, segments, 3D segments, surfaces to model digital twin. Para 81 teaches joint, height, weight, and mass distribution. See also 47, 107, 108, 115-122)
providing feedback […] (El-Sallam para 29 teaches real-time feedback including visual and haptic cues including change of direction, timing and action. Para 39 teaches 3D animations for visual feedback.)
El-Sallam does not teach
predicting fatigue and injury risk of said user using a trained machine learning model by comparing said captured motions to predefined thresholds […] based on biomechanical parameters of said user from a digital twin model of said user; and
providing feedback based on said predicted fatigue and injury risk of said user.
Theimer does teach
predicting fatigue and injury risk of said user using a trained machine learning model by comparing said captured motions to predefined thresholds […] based on biomechanical parameters of said user from a digital twin model of said user; and (Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds )
providing feedback based on said predicted fatigue and injury risk of said user. (Theimer col 3 lines 5-14 teach predicting injury risk using a trained model and thresholds )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk using a trained machine learning model by comparing motions to thresholds as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
El-Sallam in view of Theimer does not teach predicting fatigue and injury risk of said user using a trained machine learning model by comparing said captured motions to predefined thresholds of range of motion constraints based on biomechanical parameters of said user from a digital twin model of said user; and
Ring does teach predicting fatigue and injury risk of said user using a trained machine learning model by comparing said captured motions to predefined thresholds of range of motion constraints based on biomechanical parameters of said user from a digital twin model of said user; and (Ring para 27 teaches threshold of range of motion)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin prediction of fatigue and injury as taught by El-Sallam in view of Theimer with the range of motion constraint as taught by Ring. It would be beneficial to predict fatigue and injury risk based on range of motion constraint as taught by Ring para 14.
CLAIM 2, 14, 26
El Sallam teaches wherein said feedback comprises an indication […] in response to a captured motion of said captured motions […] (El-Sallam para 29 teaches real-time feedback including visual and haptic cues including change of direction, timing and action. Para 39 teaches 3D animations for visual feedback.)
El-Sallam does not teach wherein said feedback comprises an indication of fatigue and/or injury risk of said user in response to a captured motion of said captured motions […]
Theimer does teach wherein said feedback comprises an indication of fatigue and/or injury risk of said user in response to a captured motion of said captured motions […] (Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
El-Sallam in view of Theimer does not teach wherein said feedback comprises an indication of fatigue and/or injury risk of said user in response to a captured motion of said captured motions exceeding a predefined threshold of said predefined thresholds of range of motion constraints.
Ring does teach wherein said feedback comprises an indication of fatigue and/or injury risk of said user in response to a captured motion of said captured motions exceeding a predefined threshold of said predefined thresholds of range of motion constraints. (Ring para 27 teaches threshold of range of motion and providing a warning)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin prediction of fatigue and injury as taught by El-Sallam in view of Theimer with the range of motion constraint as taught by Ring. It would be beneficial to predict fatigue and injury risk based on range of motion constraint as taught by Ring para 14.
CLAIM 3, 15, 27
El-Sallam teaches wherein said motions performed by said user are captured using a camera-based optical tracking device. (El-Sallam para 75 teaches camera or cameras to record info and track subject )
CLAIM 6, 18, 30
wherein said motions comprise lifting, carrying, and manipulating objects. (El-Sallam Fig. 3C teaches a throwing motion, Fig. 7 teaches manipulating a pole. Fig. 8A teaches carrying motion. )
CLAIM 8, 20, 32
El-Sallam teaches The method as recited in Claim 1, […] (See claim 1 above)
El-Sallam does not teach wherein said comparison is performed using said machine learning model trained by a machine learning algorithm.
Theimer does teach wherein said comparison is performed using said machine learning model trained by a machine learning algorithm. (Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk using a trained machine learning model by comparing motions to thresholds as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
CLAIM 10, 22, 34
El-Sallam teaches The method as recited in Claim 1, […] (See claim 1 above)
El-Sallam does not teach wherein said predefined thresholds of range of motion constraints are based on baseline fatigue levels and baseline motions of said user.
Theimer does teach wherein said predefined thresholds of range of motion constraints are based on baseline fatigue levels and baseline motions of said user. (Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds and baseline data . Col 5, lines 40-50 teach fatigue metrics)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk using predefined threshold based on baseline fatigue and baseline motions of a user as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
CLAIM 12, 24, 36
El-Sallam teaches wherein said feedback comprises one or more of the following: visual, […], and haptic alerts. (El-Sallam para 29 teaches real-time feedback including visual and haptic cues including change of direction, timing and action. Para 39 teaches 3D animations for visual feedback. Para 29 teaches haptic feedback. Additional limitations interpreted as optional due to claim language “one or more of …”)
Claims 4-5, 16-17, 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam (US 20240070854) in view of Theimer (US 11172818) Ring (WO 2016135560) in view of Kim (KR 20240068819)
CLAIM 4, 16, 28
El-Sallam teaches wherein said motions performed by said user are captured (El-Sallam para 32 teaches motion analysis using sensor, videos, and stream of movement data)
El-Sallam does not teach wherein said motions performed by said user are captured using an inertia measurement unit.
Kim does teach wherein said motions performed by said user are captured using an inertia measurement unit. (Kim para 35 teaches capturing data from a wearable inertia measurement unit)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin prediction of fatigue and injury as taught by El-Sallam in view of Theimer in view of Ring with the inertia measurement unit as taught by Kim. It would be beneficial to simulate fatigue of a human digital twin using inertia measurement unit as taught by Kim para 5-9.
CLAIM 5, 17, 29
El-Sallam teaches wherein said motions performed by said user […] (El-Sallam para 32 teaches motion analysis using sensor, videos, and stream of movement data)
El-Sallam does not teach wherein said motions performed by said user are captured by an electromyography sensor worn by said user.
Kim does teach wherein said motions performed by said user are captured by an electromyography sensor worn by said user. (Kim para 27 teaches capturing electromyography signals from sensor )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin prediction of fatigue and injury as taught by El-Sallam in view of Theimer in view of Ring with the electromyography sensor as taught by Kim. It would be beneficial to simulate fatigue of a human digital twin as taught by Kim para 5-9.
Claims 7, 19, 31 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam (US 20240070854) in view of Theimer (US 11172818) Ring (WO 2016135560) in view of Luinge (US 20080285805)
CLAIM 7, 19, 31
wherein said biomechanical parameters comprise height, limb length, and […]. (El-Sallam para 68 teaches height. Para 78 teaches dividing body into nodes, links, segments and 3D segments to model digital twin based on subject.)
El-Sallam does not teach wherein said biomechanical parameters comprise height, limb length, and torque limits.
Luinge does teach wherein said biomechanical parameters comprise height, limb length, and torque limits. (Luinge para 41 teaches biomechanical constraints is forces or torques)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the biomechanical parameters as taught by El-Sallam in view of Theimer in view of Ring with the torque limits as taught by Luinge. It would be beneficial to include constraints in biomechanical parameters to be more accurate as taught by Luinge para 11.
Claims 9, 21, 33 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam (US 20240070854) in view of Theimer (US 11172818) Ring (WO 2016135560) in view of Erivantcev (US 20190339766)
CLAIM 9, 21, 33
El-Sallam teaches The method as recited in Claim 1, […] (See claim 1 above)
El-Sallam does not teach wherein said machine learning algorithm comprises a […] neural network
Theimer does teach wherein said machine learning algorithm comprises a […] neural network
(Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds. Col 7, lines 9-31 teach neural network. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk using a trained machine learning model by comparing motions to thresholds as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
Theimer does not teach wherein said machine learning algorithm comprises a long-short-term memory recurrent neural network.
Erivantcev does teach wherein said machine learning algorithm comprises a long-short-term memory recurrent neural network. (Erivantcev para 15 teaches an RNN, para 97 teaches the RNN may be a LSTM. Para 90 teaches tracking motions.)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the machine learning neural network as taught by El-Sallam in view of Theimer in view of Ring with the LSTM RNN as taught by Erivantcev. It would be beneficial to use a LSTM RNN to selectively remember histories of states based on which predictions are made as taught by Erivantcev para 97.
Claims 11, 23, 35 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam (US 20240070854) in view of Theimer (US 11172818) Ring (WO 2016135560) in view of Bender (US 20200118665)
CLAIM 11, 23, 35
El-Sallam teaches The method as recited in Claim 1, saving said captured motions; (El-Sallam para 32 teaches storing movement data)
identifying trends from said saved captured motions; and […] (El-Sallam para 27 teaches identify and extract movement patterns)
El-Sallam does not teach […] said baseline fatigue levels and said baseline motions of said user […]
Theimer does teach[…] said baseline fatigue levels and said baseline motions of said user […] (Theimer col 3 lines 5-14 and Col 7 lines 2-9 teach predicting injury risk and fatigue using a trained model and thresholds and baseline data . Col 5, lines 40-50 teach fatigue metrics)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the tracking and analysis of a human digital twin as taught by El-Sallam with the predicting fatigue and injury risk using predefined threshold based on baseline fatigue and baseline motions of a user as taught by Theimer. It would be beneficial to predict fatigue and injury risk as taught by Theimer col 1 lines 6-18 and col 2 lines 63-67.
El-Sallam in view of Theimer does not teach modifying said baseline fatigue levels and said baseline motions of said user based on said identified trends.
El-Sallam in view of Theimer does not teach modifying said baseline fatigue levels and said baseline motions of said user based on said identified trends.
Bender does teach modifying said baseline fatigue levels and said baseline motions of said user based on said identified trends. (Bender para 45 teaches updating baseline based on changes such as updated data including habits of the individual. Changes also include threshold changes over time in quantity or degree)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the baseline as taught by El-Sallam in view of Theimer in view of Ring with the updating baseline as taught by Bender. It would be beneficial to update baseline to keep the model updated as taught by Bender para 45.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170277852 Reynolds
Para 43 teaches measuring data before or after meal times. See also para 57 for teaching logging information over a period of time before each visit to analyze performance of the medical device.
Bansback (US 20180279919)
Bansback para 143 teaches capturing data from a wearable inertia measurement unit
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687