DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-21 are pending and currently under consideration for patentability under 37 CFR 1.104
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under U.S.C. 120, 121, or 365 is acknowledged. The prior-filed application (PRO 63/450842 filed on March 8, 2023) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 1, 2024 has been 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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a concussion detection system. Thus, the claim is directed to a machine/apparatus, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“…apply the kinematic data to a strain prediction engine to generate a strain identifier associated with the head impact and a concussion risk assessment associated with the strain identifier… the concussion risk assessment configured to identify a concussion risk associated with the head impact.”
These limitations describe a mathematical calculation. When given their broadest reasonable interpretation in light of the specification, the limitations identified above including the strain prediction engine are mathematical calculations. The instant specification also discloses that the strain prediction engine is a deep learning model (see par.0027 “a strain prediction engine 40, such as a deep-learning model, to generate strain identifiers 42 over the duration of the impact”). See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible).
Furthermore, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human, or using simple pen/paper. This is also reinforced in the 2024 AI SME Update, which sets forth that a trained machine learning model/engine amounts to a mental process (claim 2 of example 47).
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“a kinematic detection device configured to be carried by a head of a user; and a concussion detection device disposed in electrical communication with the kinematic detection device, the concussion detection device comprising a controller having a memory and a processor, the controller configured to: receive kinematic data from the kinematic detection device, the kinematic data associated with a head impact of the user…and output the concussion risk assessment based upon the strain identifier…”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant extra-solution activity, i.e., mere data gathering steps necessary to perform the identified judicial exception and outputting the result thereafter does not integrate the claims into a practical application. See MPEP 2106.05(g).
The additional limitations also do not add significantly more to the identified judicial exception because they relate to widely-understood, routine, and conventional techniques for obtaining data. Examiner takes official notice that “a kinematic detection device” is widely known. Moreover, the claims fails to recite any particularity with respect to the structural components as they are recited at a high level of generality.
Independent claims 11 and 21 are also not patent eligible for substantially similar reasons.
Dependent claims 2-10 and 12-20 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea, recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above, and/or do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, claims 1-21 are not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 102(A)(1)
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ng et al. (US PG Pub. No. 2019/0320965 A1) (hereinafter “Ng”)
With respect to claims 1, 11, and 21, Ng teaches detection system, method, and device comprising: a kinematic detection device configured to be carried by a head of a user (Fig. 6 shows a device that is arranged/carried on a subject’s head 602 to obtain kinematics data; par.0041 “analyzes kinematics data”; par.0058 “sporting helmet, a combat helmet, or another type of head cover 604 may carry sensor devices 618, 620, 622 in a spatial configuration”); and a concussion detection device disposed in electrical communication with the kinematic detection device, the concussion detection device comprising a controller having a memory and a processor (par.0064+ “Local storage 120 may be used for storing data collected… smartphone 114, smartwatch 116, tablet computer 118 or another portable device… stored in approximately 10 kB of memory”; par.0078 “processing device 904… processing circuitry 902”), the controller configured to: receive kinematic data from the kinematic detection device (par.0042 “Head motion data 202 received at a mobile device may be abstracted, normalized and/or transformed to represent kinematics at the center of gravity of the brain for use by the concussion model 200”), the kinematic data associated with a head impact of the user (par.0008 “head acceleration event results from an impact to the subject’s head or exposure of the subject’s head to a blast”; see also par.0034 “process and analyze motion data corresponding to impacts or blasts affecting the head of the subject”; par.0041 “quantify concussion risk from complex head kinematics”), apply the kinematic data to a strain prediction engine to generate a strain identifier associated with the head impact and a concussion risk assessment associated with the strain identifier (par.0041 “mobile concussion model 122 analyzes kinematics data at the center of gravity of the brain that can relate external motion data to internal strain estimates in specific regions of the brain”; par.0055 “The local, fast concussion model permits external motion data to be expressed as internal axonal strains and axonal signaling dysfunction”; par.0074 “the representative axonal strain 804 is provided by the fast-running tissue-response model 704 to a micromechanics model 706 that generates estimates of the strain at the node of Ranvier (εNR) 806 for each angular component… to produce a probability of concussion 812”), and output the concussion risk assessment based upon the strain identifier, the concussion risk assessment configured to identify a concussion risk associated with the head impact (par.0041 “evaluate concussion risk by analyzing motion data captured by wearable sensors in the sensor platform 102 and to provide an individualized assessment of exposure conditions for a subject wearing components of the sensor platform 102”).
With respect to claims 2 and 12, Ng teaches wherein the kinematic detection device comprises: at least one accelerometer configured to generate a linear acceleration signal (par.0061); at least one gyroscope configured to generate a rotational velocity signal (par.0061); and a transceiver disposed in electrical communication with the at least one accelerometer and the at least one gyroscope, the transceiver configured to transmit the linear acceleration signal and the rotational velocity signal as kinematic data to the concussion detection device (par.0077).
With respect to claims 3 and 13, Ng teaches wherein the kinematic detection device comprises a mouthguard (par.0058).
With respect to claims 4 and 14, Ng teaches wherein when generating the strain identifier associated with the head impact, the controller is configured to generate a tissue strain value and a tissue strain rate value by a brain region of the user (par.0044).
With respect to claims 5 and 15, Ng teaches wherein in response to generating the tissue strain value and the tissue strain rate value for brain region of the head, the controller is configured to output a brain image identifying a predicted displacement field and the corresponding tissue strain value and tissue strain rate value associated with the brain region of the head (par.0009, 0084, 0093).
With respect to claims 6 and 16, Ng teaches wherein when outputting the brain image identifying the predicted displacement field and the corresponding tissue strain value and tissue strain rate value associated with the brain region of the head, the controller is configured to output a consecutive set of brain images; each brain image of the consecutive set of brain images associated with a corresponding time point of a set of time points during the head impact; and each brain image of the consecutive set of brain images identifying the predicted displacement field and the corresponding tissue strain value and tissue strain rate value associated with the brain region of the head at the corresponding time point (par.0009, 0084, 0093).
With respect to claims 7 and 17, Ng teaches wherein, when generating the concussion risk assessment associated with the strain identifier, the controller is configured to: compare the strain identifier to an injury threshold value; and when the strain identifier meets the injury threshold value, generate the concussion risk assessment identifying a concussion associated with the head impact (par.0015, 0099).
With respect to claims 8 and 18, Ng teaches wherein, when generating the concussion risk assessment associated with the strain identifier, the controller is configured to: compare the strain identifier to an injury threshold value; and when the strain identifier falls below the injury threshold value: identify a brain region of the head associated with the strain identifier, identify the brain region of the head as having a previous strain identifier, and following identification of the brain region as having the strain identifier and the previous strain identifier, generate the concussion risk assessment identifying a concussion associated with the head impact (par.0015, 0099).
With respect to claims 9 and 19, Ng teaches wherein: when receiving kinematic data from the kinematic detection device, the controller is configured to further receive head size data associated with the head of the user; and when applying the kinematic data to the strain prediction engine to generate the strain identifier associated with the head impact and the concussion risk assessment associated with the strain identifier, the controller is configured to apply the kinematic data and the head size data to the strain prediction engine to generate the strain identifier associated with the head impact and the concussion risk assessment associated with the strain identifier (par.0015, 0041, 0044, 0055, 0074, 0099).
With respect to claims 10 and 20, Ng teaches wherein: when receiving kinematic data from the kinematic detection device, the controller is configured to further receive brain size data associated with the head of the user; and when applying the kinematic data to the strain prediction engine to generate the strain identifier associated with the head impact and the concussion risk assessment associated with the strain identifier, the controller is configured to apply the kinematic data and the brain size data to the strain prediction engine to generate the strain identifier associated with the head impact and the concussion risk assessment associated with the strain identifier (par.0015, 0041, 0044, 0055, 0074, 0099).
Conclusion
No claim is allowed.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791