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 .
Applicant’s arguments filed in the reply on July 9, 2026 were received and fully considered. Claims 1, 6, 8, 11, 16, 18, and 21 were amended. Claims 4, 5, 14, and 15 were cancelled. Claims 22-24 are new. The current action is FINAL. Please see corresponding rejection headings and response to arguments section below for more detail.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 9, 2026 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 1-3, 6-13, and 16-24 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, when generating the strain identifier associated with the head impact, generate a tissue strain value and a tissue strain rate value for a brain region of the head… 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…in response to generate the tissue strain value and the tissue strain rate value for the brain region of the head, 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, 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. Furthermore, the output a brain image, as set forth in the current amendment, relates to mere extra-solution activity (MPEP 2106.05g), which does not integrate the claims into a practical application; and does not amount to significantly more.
Independent claims 11 and 21 are also not patent eligible for substantially similar reasons.
Dependent claims 2, 3, 6-10, 12, 13, and 16-24 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 1-3, 6-13, and 16-24 are not patent eligible under 35 USC 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-13, and 16-24 are rejected under 35 U.S.C. 103 as being unpatentable over Ji et al. (US PG Pub. No. 2016/0321425 A1) (hereinafter “Ji”) in view of Ng et al. (US PG Pub. No. 2019/0320965 A1) (hereinafter “Ng”).
Ji was cited in applicant’s IDS, recently filed on July 9, 2026.
Ng was applied in the previous office action.
With respect to claims 1 and 11, Ji teaches a concussion detection system (abstract “system for evaluating head injury”), comprising: a kinematic detection device configured to be carried by a head of a user (par.0027 “multiple accelerometers 110 positioned to be against a head… when the helmet is worn”; see also Fig. 1); 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.0028 “Workstation 122… receives the accelerometer readings… executes machine readable instructions 126 located in its memory to characterize 206 direction and angle, and peak magnitude, of linear and rotational (torque) accelerations associated with that impact”; par.0037 “multiprocessor system”),
the controller configured to: receive kinematic data from the kinematic detection device, the kinematic data associated with a head impact of the user (par.0026-28 “head-impact-analysis system… receives the accelerometer readings”), 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.0037 “computing engine server 144… where the DHIM model 146 resides… the model is executed 213 to simulate hit-induced movement of brain tissue and resulting strain on fiber tracts within the brain… comparing calculated strain on tracts to thresholds, including concussion-type damage, and to determine neurological signs that may be associated with damage to those tracts”), when generating the strain identifier associated with the head impact, generate a tissue strain value
However, Ji does not teach generating a tissue strain rate value for a brain region of the head.
Ng teaches a brain injury monitoring system (title) taking into consideration tissue strain rate (par.0044 “strain rate may be closely correlated with higher concussion risk”).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art (“PHOSITA”) when the invention was filed to modify Ji to utilize tissue strain rate values in the manner recited in order to aid in assessment of concussion risk, as evidence by Ng (par.0044). Furthermore, PHOSITA would have had predictable success combining Ji and Ng as both teachings relate to the same narrow field of endeavor, i.e. concussion detection utilizing predictive models.
With respect to claims 2 and 12, Ji teaches wherein the kinematic detection device comprises: at least one accelerometer configured to generate a linear acceleration signal (par.0027-32); at least one gyroscope configured to generate a rotational velocity signal (par.0027-32); and a transceiver disposed in electrical communication with the at least one accelerometer and the at least one gyroscope (receiver 120 in Fig. 1; par.0028), the transceiver configured to transmit the linear acceleration signal and the rotational velocity signal as kinematic data to the concussion detection device (par.0028+; Fig. 1).
With respect to claims 3 and 13, Ji teaches wherein the kinematic detection device comprises a mouthguard (par.0026).
With respect to claims 6 and 16, Ji 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.0026-72).
With respect to claims 7 and 17, Ji 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.0026-72).
With respect to claims 8 and 18, Ji 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 the 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.0026-72).
With respect to claims 9 and 19, Ji 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.0026-72).
With respect to claims 10 and 20, Ji 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.0026-72).
With respect to claim 22, Ji 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 display each brain image of the consecutive set of brain images in a consecutive manner to animate a position of the predicted displacement field within the user's brain over an impact duration (par.0026-72).
With respect to claim 23, Ji teaches wherein when receiving kinematic data from the kinematic detection device, the kinematic data associated with the head impact of the user, the controller is further configured to receive cranial size data associated with the head of the user; the controller is configured to apply the cranial size data as a scaling factor to x-y-z directions of a strain prediction model to generate a sized strain prediction engine, the cranial size data comprising one of head dimension data and brain dimension 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 cranial size data to the sized strain prediction engine to generate the strain identifier having predicted tissue strain values and tissue strain rate values tailored to the user's cranial size (par.0026-72).
With respect to claim 24, Ji teaches wherein the brain image comprises a voxelized 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 (par.0026-72).
Response to Arguments
Applicant's arguments filed with respect to the 35 USC 101 rejections raised in the previous office action have been fully considered, but they are not persuasive. The amended limitations, as they pertain to output a brain image, amounts to mere extra-solution activity (MPEP 2106.05g, displaying/outputting a result does not integrate claims into a practical application). For at least these reasons, the 35 USC 101 rejections are maintained.
Applicant’s arguments with respect to the prior art rejections raised in the previous office action have been considered, but are moot in view of the updated combination of references1. Please see prior art section for more detail, updated citations (new Ji reference), and updated obviousness rationale.
Conclusion
No claim is allowed.
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on July 9, 2026 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PUYA AGAHI whose telephone number is (571)270-1906. The examiner can normally be reached M-F 8 AM - 5 PM.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
1 Ji reference was cited in applicant’s IDS filed on July 9, 2026.
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