DETAILED ACTION
This office action is based on the claims filed on 12/03/2024.
Claims 1-13 are currently pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/14/2025 is in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
Election/Restrictions
The claims examined according to the selected claims on the submitted Applicant Argument/Remarks made in an Amendment on 08/03/2026 with election was made without traverse to prosecute the invention of “HEALTHCARE SYSTEM FOR AND METHODS OF MANAGING BRAIN INJURY OR CONCUSSION”, Group I Claims (1-13) were elected and Group II Claims (14-20) were withdrawn.
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.
Claim 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-13 are drawn to a method of which is within the four statutory categories (i.e., a machine and a process). Claims 1-13 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represent an abstract idea of a series of steps that recite a process for diagnosing a treatment of a medical condition (brain injury). Collecting an individual data to analyze and determine a traumatic brain injury and suggest a treatment are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“(a) receiving a plurality of attributes of the subject, wherein the plurality of attributes is related to the traumatic brain injury;
(b) applying a machine learning model to the plurality of attributes to predict (i) a clinical outcome comprising the traumatic brain injury, and (ii) the plurality of treatment options for treatment of the traumatic brain injury”.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process but for the recitation of generic computer components. For example, the limitations encompass a user the ability to collect and analyze an individual/patient data for diagnosing a traumatic brain injury or concussion to provide an outcome results and suggest a treatment among treatment options, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper, but other than the mere nominal recitation of "machine learning model", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “machine learning model” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions, e.g., applying a machine learning model, is recited in the claims at a high level of generality and is described in the specification in an arbitrary form without disclosing a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function, such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-13, include all the limitations of claim(s) 1 and therefore likewise incorporate the above-described abstract idea. While the depending on claims add additional limitations, such as
As for claims 2-5, 9, and 13, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims recite additional elements “graphical user interface (GUI), electronic device, wearable device, machine learning” that implement the identified abstract idea. These hardware components are recited in the claim(s) at a high level that it amounts to no more than mere instructions to perform the steps of the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d). For example, the claim recites a machine learning model which is recited at a high level of generality and is described in the specification in an arbitrary form without disclosing how a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. Thus, the judicial exceptions recited in claims is/are not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 6-8, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper along with mathematical concepts but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 10-12, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 4 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Suffin et al. (US 2012/0253219 A1 – “Suffin”)
Regarding Claim 1 (Original), Suffin teaches a computer-implemented method for predicting a plurality of treatment options for treatment of a traumatic brain injury for a subject Suffin discloses measuring metrics/ neurophysiologic information of a subject's brain for diagnosing and treating physiologic brain imbalances associated with traumatic brain injury (Suffin: [0010], [0080]), the computer-implemented method comprising:
(a) receiving a plurality of attributes of the subject, wherein the plurality of attributes is related to the traumatic brain injury; Suffin discloses obtain a set of analytic brain signals from a patient collected from neurophysiologic instruments for assessing and treating physiologic brain imbalances (Suffin: [0010], [0060], [0080])
(b) applying a machine learning model to the plurality of attributes to predict (i) a clinical outcome comprising the traumatic brain injury, and (ii) the plurality of treatment options for treatment of the traumatic brain injury Suffin discloses applying an algorithm(s) to provide a report of the individual patient's neurophysiologic profile analysis and determine therapy options and recommendation for treating physiologic brain imbalances (Suffin: [Fig. 2, 3.1, 5-7], [0073], [0093], [0104], [0139]).
Regarding Claim 4 (Original), Suffin teaches the computer-implemented method of claim 1, wherein the receiving the plurality of attributes is via a wearable device Suffin discloses using EEG device for information acquisition and analysis where the EEG comprises electrodes attached to the patient scalp (Suffin: [0060], [0071], [0106]).
Claim Rejections - 35 USC § 103
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Suffin et al. (US 2012/0253219 A1 – “Suffin”) in view of Chang et al. (US 2021/0057053 A1– “Chang”)
Regarding Claim 2 (Original), Singh teaches the computer-implemented method of claim 1, wherein the receiving of the plurality of attributes is via a graphical user interface (GUI) of an electronic device
However, Suffin does not expressly disclose receiving input via GUI.
Chang discloses receiving of the plurality of attributes indicating brain health via a graphical user interface (GUI) of an electronic device (Chang: [Fig. 10], [0027], [0031], [0124]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate the feature of using GUI to receive inputs, as taught by Chang which used for improving brain health and tailoring treatment for brain health (Chang: [0028], [0092]).
Regarding Claim 3 (Original), the combination of Suffin and Chang teaches the computer-implemented method of claim 2, wherein the subject or a medical professional enters the plurality of attributes using the GUI Chang discloses inputs to the brain health baseline record managing system by the brain health subject, first responders, or other parties (Chang: [0029]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2 and incorporated herein.
Claims 5-7 and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Suffin et al. (US 2012/0253219 A1 – “Suffin”) in view of Jodoin et al. (US 2023/0022257 A1– “Jodoin”)
Regarding Claim 5 (Currently Amended), Suffin teaches the computer-implemented method of claim 1, wherein the machine learning model embeds the plurality of attributes into a latent representation in a latent space
However, Suffin does not expressly disclose latent representation in a latent space.
Jodoin discloses a pretrained machine learning model analysis techniques transferring the neurological parameters to a latent representation in a latent space (Jodoin: [0108], [0118]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate implementing latent space and representation on algorithms, as taught by Jodoin which reduces the computation time needed to obtain revised or improved tractography results (Jodoin: [0108]).
Regarding Claim 6 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 5, wherein the latent representation corresponds to the plurality of attributes indicates indicating a presence or absence of a symptom in the subject, a severity or mildness of a symptom in the subject, or any combination thereof Suffin discloses performing neurometric analysis to physiologic brain imbalance to determine deviation in neurophysiologic functioning and if the outcomes showing a normal function or asymptomatic and/or symptomatic and scores representing deviation from normal outcomes (Surrin: [Fig. 2], [0044], [0056], [0067], [0078]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 5 and incorporated herein.
Regarding Claim 7 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 6, further comprising
classifying the traumatic brain injury as a concussion; Jodoin discloses identifying a traumatic brain injury such as classifying traumatic brain injury as a concussion Suffin discloses classifying physiologic brain imbalances using neurophysiologic information (Suffin: [0013], [0036]). Jodoin discloses traumatic brain injury as concussion (Jodoin: [0065], [0083], [0109])
classifying the concussion as a concussion phenotype, wherein the concussion phenotype comprises a persistent concussion, wherein the classifying is based on a position of the latent representation in the latent space Jodoin discloses classification of brain injury as concussion based on sudden neuro-inflammation and swelling observed and classification according to on position of the latent representation in the latent space corresponds to the information associated with the neurological anatomical region (Jodoin: [0113).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate implementing classification of brain injury as concussion and position latent space corresponds of the disease in the anatomical region, as taught by Jodoin which reduces the computation time needed to obtain revised or improved tractography results (Jodoin: [0108]).
Regarding Claim 9 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 7, further comprising predicting, by the machine learning model, a recovery timeline for the subject, based at least in part on the latent representation Suffin discloses predicating treatment and recovery based on stage of the condition and determine pathway for recovery (Suffin: [Fig. 8], [0127], [0149]). Jodoin discloses using the neurological fibers latent representation to analyze and assess medication intervention for a disease (Jodoin: [0065]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate assessing medication impact based on neurological latent representation, as taught by Jodoin which reduces the computation time needed to obtain revised or improved tractography results (Jodoin: [0108]).
Regarding Claim 10 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 9, further comprising selecting a treatment in the plurality of treatment options, wherein the treatment is personalized adapted to the latent representation of the traumatic brain injury of the subject and the treatment is delivered or administered to the subject, and wherein the treatment comprises a brain treatment protocol Suffin discloses therapy protocol and recommended treatment and initiating therapy based on the patient stage (Suffin: [Fig. 4-8], [0080] [0141], [0149]). Jodoin discloses using the neurological fibers latent representation to analyze and assess impact of applied medication intervention for a disease (Jodoin: [0065]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate treatment selection based on neurological fiber latent representation, as taught by Jodoin which reduces the computation time needed to obtain revised or improved tractography results (Jodoin: [0108]).
Regarding Claim 11 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 10, wherein the brain treatment protocol comprises a drug regimen, a rehabilitation exercise Suffin discloses treatment protocol to include monotherapy (medical treatment using drug) [drug regimen] and electrotherapeutic (medical and physical therapy) [rehabilitation] (Suffin: [3.1]).
Regarding Claim 12 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 11, further comprising selecting a treatment in the plurality of treatment options for the subject based at least in part on the activity data of the subject Suffin discloses treatment options based on activity/behavior such as libido, sleep, (Suffin: [3.1], [0080], [0106]).
Regarding Claim 13 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of any one of claims 1, further comprising training the machine learning model by:
(a) receiving a dataset comprising (i) a plurality of attributes of a plurality of reference subjects, and (ii) a plurality of clinical outcomes for the plurality of subjects; Suffin discloses obtain a set of analytic brain signals from a patient collected from neurophysiologic instruments for assessing and treating physiologic brain imbalances and providing multivariable neurophysiologic outcome measurements (Suffin: [0010], [0012], [0060], [0080])
(b) processing a reference dataset using the machine learning model to generate a plurality of outputs, wherein the plurality of outputs parameterizes the plurality of clinical outcome predictions, and (ii) updating a parameter of the machine learning model based on a loss function, wherein the loss function is based on the plurality of clinical outcomes and the plurality of outputs, wherein the plurality of outputs is indicative of a plurality of clinical outcome predictions Suffin discloses outcome predication correlated with symptomatic behavioral assessments (Suffin: [0140]). Jodoin discloses training a model using brain data of plurality of patients and update the parameters based on a loss function (Jodoin: [Fig. 9], [0096-0098]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Suffin incorporate the machine learning training using input information to predict the output and modify it using loss function, as taught by Jodoin which reduces the computation time needed to obtain revised or improved tractography results (Jodoin: [0108]).
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Suffin et al. (US 2012/0253219 A1 – “Suffin”) in view of Jodoin et al. (US 2023/0022257 A1– “Jodoin”) in view of Everett et al. (US 2020/0116739 Al “Everett”)
Regarding Claim 8 (Currently Amended), the combination of Suffin and Jodoin teaches the computer-implemented method of claim 7, further comprising generating a probability that the traumatic brain injury is the concussion or a plurality of probabilities for a plurality of concussion phenotypes of the traumatic brain injury, wherein the probability is generated based on the latent representation Suffin discloses probability that the patient suffer an abnormal or pathologic brain functions (Suffin: [0063], [0066]). Jodoin discloses probability of neurological disease/neuroinflammation based on neurological fiber latent representation (Jodoin: [0108], [0116], [0143], [0145]
However, the combination of Suffin and Jodoin do not expressly teach a probability that the traumatic brain injury is the concussion
Everett discloses probability of the traumatic brain injury as a concussion by comparing brain derived neurotrophic factor (BDNF) as biomarkers that correlate the level of BDNF to concussion significance (Everett: [0138], [0175])
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Suffin and Jodoin incorporate implementing probability of concussion, as taught by Everett which helps differentiating between patients with and those without traumatic brain injury (Everett: [0016]).
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
US 2024/0225611 “Firouzi” discloses use a physics guided machine learning model to determine measurements of various metrics (e.g., ICP, ABP, and/or ICE) of a subject's brain.
US 2021/0290178 “Kolls” discloses monitoring EEG data signal and other information from multiple clinical sources to estimate the current severity of a brain injury and to predict recovery potential of a given patient with the brain injury.
US 2016/0306942 “Rapaka” discloses receiving 3D image data representative of a subject's brain and identifying subject specific anatomical structures in the 3D image data for computing functional indicators of neurological disorder.
The references are relevant since it discloses analyzing received brain data to determine disease and treatment.
Conclusion
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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