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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 17, 2026 has been entered.
Response to Amendment
Claims 1, 7, 9, and 11-13 have been amended. Claims 8 and 10 have not been modified. Claims 2-6 and 14-18 have been cancelled. Claims 19-28 have been added. Claims 1, 7-13, and 19-28 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed March 17, 2026 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §112 Rejections, pg. 7 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the prior 112 rejection. However, Applicant amendments have caused a new 112 rejection; see below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 8 of Remarks:
Regarding a. 1., Applicant argues that the claims do not recite an abstract idea of certain methods of organizing human activity as the claims are directed towards receiving a series of additional baseline test data, updating the baseline of expected neurological functioning test data, receiving post-impairment test data from the neurological functioning test, and determining the likelihood that the post impairment test data is indicative of neurological impairment using a trained machine learning model are not diagnosing a patient. The Examiner respectfully disagrees.
Examiner submits that the determination of the likelihood that the post impairment test data is indicative of neurological impairment is a diagnosis of a patient’s condition, specifically whether or not the patient has impairment. This is supported by [0002] of Applicant specification, which recites: “A neurological impairment is a state in which the neurological functioning of an individual is debilitated by a neurological disorder, substance use, an injury such as a traumatic brain injury, or another cause of impairment. Such neurological impairments are conventionally diagnosed in consultation with a healthcare professional.”
The other steps of maintaining a baseline of expected neurological functioning, performing a neurological functioning test, and handling of pre- and post-impairment neurological data of a patient are steps that provide instruction for neurologists to perform on their patients to inform the diagnosis (determination of the likelihood). Thus, Examiner maintains that the claims recite an abstract idea of certain methods of organizing human activity. Examiner notes that characterization under an abstract idea itself is not a rejection, as alleged by Applicant.
Regarding a. 2., Applicant argues that the claims do not recite an abstract idea of mental processes as the system is dependent on a computer system. Examiner respectfully disagrees. Please see MPEP 2106.04(a)(2)III C, which recites: “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” Under the broadest reasonable interpretation of the claims, the claimed steps are performable mentally, or with pen and paper, by a neurologist for their patients.
Examiner directs Applicant to claim 2 of Example 47, which was found to be abstract for mental processes despite implementation of steps within a computing environment using machine learning.
Regarding b., Applicant argues that the claims integrate the recited judicial exception into a practical application by “enhanc[ing] computer functionality by enabling the trained machine learning model and generating outputs of the indication of the likelihood that the post-impairment test data is indicative of neurological impairment therefrom”. Applicant further argues that such an arrangement provides “conservation of compute resources by utilizing the particular baselines test data, as opposed to manually considering all test data a given vestibular test”, which provides a “reduction in an amount of data parsed by the trained machine learning model”.
Examiner submits that such an improvement is directed to an improvement to the abstract idea of neurological function testing and does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). Applicant arguments are directed towards increasing efficiency of the diagnosis process and not directed to any specific, technical improvements in how computers or machine learning functions.
Even further, efficiency alone is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2).
Regarding c., Applicant argues that the claimed improvement in determining the likelihood that the post-impairment test data is indicative of neurological impairment in the individual in a time and computationally efficient manner provides an arrangement of additional elements that is significantly more than the judicial exception. Examiner respectfully disagrees.
The consideration under Step 2B is if the additional elements (additional elements of claim 1: network interface, mobile device, computer network, processor, memory storage unit, trained machine learning model), alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea of determining the likelihood that the post-impairment test data is indicative of neurological impairment using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant argues 35 U.S.C. §103 Rejections, pg. 15 of Remarks:
Regarding IV, Applicant argues that Alberts in view of Kim does not teach the amended limitations, as Alberts teaches historical data instead of baselines. Examiner respectfully disagrees.
[0053] of Alberts recites: “the approach disclosed herein can in turn ascertain more useful information in distinguishing MS or other conditions from excepted norms, and further distinguish severity within a condition and over time for each patient, such as based on a historical analysis of test data over period of time (e.g., one or more years)”
[0060] recites: “Scoring may also take into account patient longitudinal date, i.e. data taken during similar tests on the same patient during different sessions over a period of time.”
[0083] recites: “a score can be evaluated relative to pre-test data (from a control group, longitudinal patient data and/or acquired during an un-timed pre-test).”
[0095] recites: “a first part of test can establish baseline static acuity data for the patient.”
[0127] recites: “Collection and aggregation of this data over time is important in assessing disease progression and response to treatment in MS patients, as reflected in the widespread use of the traditional forms of these neurological assessments in clinical trials”
It would be obvious to one of ordinary skill in the art that collecting longitudinal data of the patient (including data suggesting normal functioning; norms) to understand disease progression in the same patient over time using the same tests contemplates establishing baseline functioning. Even if Alberts did not teach or suggest a baseline as alleged by Applicant, Kim does ([0147], “The response module 1104 may separately receive and/or store baseline response data (e.g., in response to one or more baseline questions or prompts from the query module 1102) and test case response data (e.g., in response to one or more test case questions or prompts from the query module based on a potential medical event, or the like).”).
Applicant further argues that Kim is not cited for, and does not teach or suggest, the amended claim limitation of updating a baseline of expected neurological functioning test data for the individual. Examiner notes that Kim does teach updating a baselines of expected functioning. However, it is not cited for, as Alberts is relied upon instead. Thus, Applicant argument is moot.
Regarding Berme, Examiner notes that Applicant amendments have removed the need for citing Berme.
Regarding V, Examiner agrees and the 103 rejection for claim 6 is withdrawn as it has been cancelled.
Regarding VI, Applicant argues that the 103 rejections for claims 7-11 and 13-17 as independent claims 7 and 13 recite analogous limitations to claim 1. Examiner agrees that they recite analogous limitations to claim 1 and maintains the rejection with regards to the response to arguments above. However, the rejections for claims 14-17 are withdrawn as they have been cancelled.
Regarding VII, Applicant argues that the 103 rejections for claims 12 and 18 should be withdrawn as they have been cancelled. However, only claim 18 has been cancelled. As such, the 103 rejection is maintained for claim 12.
Claim Objections
Claims 9 and 24-28 are objected to because of the following informalities:
Claim 9 recites: “the series of additional baseline test data baseline test data”. Examiner suggests deleting the second recitation of “baseline test data”.
Claim 24 recites: “a score representing a probability distribution of a space comprises a range of results for the neurological functioning test”. This sentence should be amended to recite: “a score representing a probability distribution of a space comprising a range of results for the neurological functioning test”
Claim 25 is objected to by virtue of its dependency on claim 24.
Claims 26 and 27 recite: “The method of claim 1, a corresponding first value”. Claim 28 recites: “The method of claim 27,” Examiner suggests including a “wherein” and changing “method” to “system”, as claim 1 is a system claim.
Claim 28 is further objected to by virtue of its dependency on claim 27.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 23 recites “wherein a one-to-many relationship exists between a respective neurological functioning vector and the neurological functioning test”, which suggests wherein many vectors are produced from one test or one vector is produced from multiple tests. However, [0052] of Applicant specification recites: “One-to- many relationships exist between vectors and testing sessions which have been conducted.”, which instead suggests producing many vectors in one testing session.
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, 7-13, and 19-28 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine (1, 7-13, and 19-28). Accordingly, claims 1, 7-13, and 19-28 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system claim 1 as the claims that represents the claimed invention for analysis, as claim 1 is similar to method claim 7 and product claim 13.
Claim 1:
A system for generating indications of neurological impairment, the system comprising:
a network interface configured to communicate with one or more mobile devices via a computer network; and
a processor in communication with the network interface and a memory storage unit storing at least one program for execution by the processor, the at least one program comprising instructions for:
establishing a baseline of expected neurological functioning test data for an individual based on at least first baseline test data gathered from a vestibular test performed on the individual at a first time point;
receiving, over time, a series of additional baseline test data from additional vestibular tests performed on the individual at later time points;
updating, responsive to receiving each respective additional baseline test data in the series of additional baseline test data, the baseline of expected neurological functioning for the individual based on the respective additional baseline test data;
receiving post-impairment test data from a vestibular functioning test performed on the individual after an impairment,
determining a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the updated baseline of expected neurological functioning by evaluating the post impairment test data using a trained machine learning model; and
outputting an indication of the likelihood that the post-impairment test data is indicative of neurological impairment.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards a system for generating indications of neurological impairment, which is diagnosing a patient. Diagnosing a patient condition falls under the abstract concept of managing personal behaviors of people. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that MPEP 2106.04(a)(2) II states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
These above limitations, under their broadest reasonable interpretation, also cover performance of the limitation as mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass receiving gathered data, probabilistically determining a likelihood that the data is indicative of neurological impairment, and outputting an indication of the likelihood.
Accordingly, the claim recites an abstract idea.
Claims 7 and 13 are abstract for the same reasons as claim 1.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the claims:
a network interface (1); one or more mobile devices (1,12,27); a computer network (1); a memory storage unit (1); program (1,13,27); a processor (1); a trained machine learning model (1,7,9,13); a mobile device (12,27); non-transitory computer-readable medium (13)
Any and all computing devices (processor) with corresponding instructions (program, memory storage unit, non-transitory computer-readable medium) that would be able to perform the method are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0033] of Applicant specification recites: “The mobile device 120 may include a smart phone or tablet running an operating system such as, for example, Android@, iOS@, Windows@ mobile, or similar. The mobile device 120 may further include various sensors such as an image capture device capable of optically measuring an individual's pulse, and a gyroscope, accelerometer, or other motion- sensing device for measuring the motion of the mobile device 120. It is contemplated that for the performance of certain neurological functioning tests, the mobile device 120 may include a desktop computer or other similar device.” No specific, technical improvements are being made to the technology of computing devices as they are only applied to perform the abstract idea of determining a likelihood that the post impairment test data is indicative of neurological impairment.
The machine learning model is taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0069-0070] recites: “In process 800, post-impairment test data is fed into machine learning model 810. The machine learning model 810 has been trained with previously gathered test data and associated diagnoses stored in training data store 820. The training data store 820 may include, for example, previously gathered neurological functioning test data along with diagnoses from healthcare professionals as to whether test data was reflective of a neurological impairment. The machine learning model 810 may weight the individual's personal baseline test data to some degree, and may weigh population baseline test data to some degree. The machine learning model 810 may also incorporate supplementary data such as the individual's age, gender, substance use, medical background, sports played, occupation, etc., to make its prediction. The machine learning model 810 may employ undirected graph models such as Markov networks.” No specific, technical improvements are being made to the technology of machine learning as a previously trained Markov network is simply applied to perform the abstract idea of determining a likelihood that the post impairment test data is indicative of neurological impairment.
The communication network is also taught at a high level of generality. [0019] recites: “The mobile device 120 and server 140 are in communication over one or more computer networks, indicated as network 102. The network 102 can include the internet, a Wi-Fi network, a local-area network, a wide-area network (WAN), a wireless cellular data network, a virtual private network (VPN), a combination of such, and similar.” No specific, technical improvements are being made to the technology of communication networks as they are only applied to perform the insignificant extra-solution activity of transmitting data.
Mobile devices are also taught at a high level of generality. [0017] recites: “The mobile device 120 executes a testing application 128 for performing neurological testing on the individual.” [0055] further recites: “For example, where a score obtained by an individual performing a neurological test falls within a certain range of values, a corresponding indication, which may include a warning or a color code, may be outputted.” No specific, technical improvements are being made to the technology of mobile devices, as they are only applied to perform an abstract idea of performing cognitive testing on a patient and an insignificant extra-solution activity of outputting or displaying data (a warning).
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 8: This claim recites wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is based at least in part on baseline test data gathered from other individuals; which only serves to limit the type of data that is considered for performance of the abstract idea of determining likelihood of neurological impairment.
Claim 9: This claim recites wherein the trained machine learning model is trained to classify post-impairment test data as indicative of neurological impairment based on training data selected from the series of additional baseline test data baseline test data; which teaches the machine learning model at a high level of generality, such that it is applied to perform the abstract idea of classifying post-impairment test data as indicative of neurological impairment.
Claim 10: This claim recites wherein the impairment comprises a traumatic brain injury, and wherein the neurological impairment comprises a concussion; which only serves to further limit the type of impairment.
Claim 11: This claim recites wherein the updated baseline of expected neurological functioning for the individual is determined by baseline test data including data gathered from at least one of a Post Concussion Symptom Scale (PCSS), visual eye movement testing, vestibular test using head stability during a gaze task, or cognitive testing; which only serves to limit type of data that is gathered for performance of the abstract idea of updating a baseline of expected neurological functioning.
Claim 12: This claim recites wherein the cognitive testing includes at least: memory testing, trail making testing, reaction time testing, and attention testing, each configured to be administered via a mobile device and analyzed by the processor; which only serves to limit the type of testing that is performed.
Claim 19: This claim recites wherein the updating the baseline test of baseline of expected neurological functioning test data is performed without human intervention; which only serves to automate a manually performable process, see MPEP 2106.05(a)I.
Claim 20: This claim recites wherein the first baseline test data comprises an absolute cumulative movement of the center of mass, a mean movement of the center of mass, a variance of movement of the center of mass, a standard deviation of movement of the center of mass, or a combination thereof; which teaches an abstract idea of mathematical concepts, as performing statistical analysis.
Claim 21: This claim recites wherein the updated baseline of expected neurological functioning comprises: a first neurological functioning vector associated with the brainstem of the first subject, and a second neurological functioning vector associated with the parietal cortex of the first subject; which only serves to narrow associations between tested neurological function and specific parts of the brain.
Claim 22: This claim recites wherein the updating the baseline of expected neurological functioning test data comprises modifying the first baseline test data based on (i) one or more average values associated with the first baseline test data and/or a respective additional baseline test data in the series of additional baseline test data and/or (ii) one or more outlier values associated with the first baseline test data and/or the respective additional baseline test data in the series of additional baseline test data; which teaches an abstract idea of mathematical concepts, as performing statistical analysis.
Claim 23: This claim recites wherein a one-to-many relationship exists between a respective neurological functioning vector and the neurological functioning test; which merely describes wherein many vectors may be associated with one test, which is abstract for testing neurological functioning.
Claim 24: This claim recites wherein the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment comprises a score representing a probability distribution of a space comprises a range of results for the neurological functioning test; which teaches an abstract idea of mathematical concepts, as performing statistical analysis using output scores.
Claim 25: This claim recites wherein the range of results is associated with every result achievable for the neurological functioning test; which only serves to limit the numerical range of scores.
Claim 26: This claim recites a corresponding first value of the post-impairment test data exceeds a corresponding first value of the first baseline test data; which teaches an abstract idea of diagnosing a patient with neurological impairment.
Claim 27: This claim recites the least one program further comprises instructions for displaying, in accordance with a determination the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment satisfies a threshold set of values, generating one or more instructions causing display of a warning at a first mobile device; which teaches an abstract idea of mathematical concepts, as comparing values with a threshold value, and certain methods of organizing human activity by providing a warning to a patient.
Claim 28: This claim recites wherein the threshold set of values comprises a minimum baseline value and a maximum baseline value defined, at least in part, by the updated baseline of expected neurological functioning; which only serves to narrow the acceptable threshold of numerical values.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 8-12 and 19-28, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 8-12 and 19-28, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 8-12 and 19-28, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. 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.
Therefore, whether taken individually or as an ordered combination, claims 1, 7-13, and 19-28 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1, 7-11, 13, 19, and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961).
Regarding claim 1, Alberts teaches a system for generating indications of neurological impairment, the system comprising:
a network interface configured to communicate with one or more mobile devices via a computer network ([0047], “the communication interface 34 can include a network interface that is configured to provide for communication with corresponding network 36, such as can include a local area network or a wide access network (WAN) (e.g., the internet or a private WAN) or a combination thereof.” [0131], “The care provider can access a database to retrieve test results for a plurality of different patients that conducted the test at different remote locations, via a tablet computer where a test was implemented or a remote computer (e.g., smart phone, desktop PC or the like). As a further example, the test results can be communicated to one or more providers. This can be done by simply reviewing the results on the computing device or the results can be sent to the provider(s) via a network connection, as disclosed herein.”); and
a processor in communication with the network interface and a memory storage unit storing at least one program for execution by the processor ([0050], “The processing unit 16 (e.g., a processor core) can be configured in the system for accessing the memory 14 and executing the machine-readable instructions.”), the at least one program comprising instructions for:
establishing a baseline of expected neurological functioning test data for an individual based on at least first baseline test data gathered from a vestibular test performed on the individual at a first time point ([0053], “the approach disclosed herein can in turn ascertain more useful information in distinguishing MS or other conditions from excepted norms, and further distinguish severity within a condition and over time for each patient, such as based on a historical analysis of test data over period of time (e.g., one or more years)” [0060], “Scoring may also take into account patient longitudinal date, i.e. data taken during similar tests on the same patient during different sessions over a period of time.” [0083], “a score can be evaluated relative to pre-test data (from a control group, longitudinal patient data and/or acquired during an un-timed pre-test).” [0095], “a first part of test can establish baseline static acuity data for the patient.” [0127], “Collection and aggregation of this data over time is important in assessing disease progression and response to treatment in MS patients, as reflected in the widespread use of the traditional forms of these neurological assessments in clinical trials” [0020], “FIG. 13 depicts another example of a movement assessment test module that includes a balance test module”). It would be obvious to one of ordinary skill in the art that collecting longitudinal data of the patient (including data suggesting normal functioning) to understand disease progression in the same patient over time using the same tests contemplates establishing baseline functioning. Furthermore, balance testing is vestibular testing, as supported by [0038] of Applicant specification (“vestibular testing module 132 may measure an individual's ability to maintain stability of one's center of mass. As an example, a tilt test may be used... The tilt test measures some aspects of function in the parietal and brainstem balance centers.”).
receiving, over time, a series of additional baseline test data from additional vestibular tests performed on the individual at later time points ([0055], “The collection module 48, 58 can also aggregate data (AFTD) from any additional function test module 47, 57 into the test data (TD).” [0060], “Scoring may also take into account patient longitudinal date, i.e. data taken during similar tests on the same patient during different sessions over a period of time.” [0083], “a score can be evaluated relative to pre-test data (from a control group, longitudinal patient data and/or acquired during an un-timed pre-test).” [0095], “a first part of test can establish baseline static acuity data for the patient.” [0127], “Collection and aggregation of this data over time is important in assessing disease progression and response to treatment in MS patients, as reflected in the widespread use of the traditional forms of these neurological assessments in clinical trials”);
updating, responsive to receiving each respective additional baseline test data in the series of additional baseline test data, the baseline of expected neurological functioning for the individual based on the respective additional baseline test data ([0059], “The scoring module 60 can, for example, characterize the cognitive and motor abilities of the given patient based on percentiles of neurological normal function for the manual dexterity test data, the cognitive function test data and the motion test data. It will be appreciated that the scoring function and/or scoring module 60 can use another means to determine the cognitive and motor abilities of the patient with respect to neurological normative values that gives an understanding of the patient's disease state and/or progression.”).
Although it would be obvious to one of ordinary skill before the time of filing to receive post-impairment data ([0053], “distinguish severity within a condition and over time for each patient, such as based on a historical analysis of test data over period of time (e.g., one or more years)””) as tracking of a condition is performed over time and probabilistically determine a likelihood that data is indicative of neurological impairment ([0048], “Results data acquired for one or modules for different patient cohorts can be aggregated together based on the testing metadata and assessed (e.g., by statistical processing) for a variety of purposes (e.g., clinical research and diagnosis).”), Alberts does not explicitly teach receiving post-impairment test data from a vestibular functioning test performed on the individual after an impairment, determining a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the updated baseline of expected neurological functioning by evaluating the post impairment test data using a trained machine learning model; and outputting an indication of the likelihood that the post-impairment test data is indicative of neurological impairment.
However, Kim does teach receiving post-impairment test data from a vestibular functioning test performed on the individual after an impairment ([0142], “in response to a hit, a fall, an accident, and/or another potential concussion event (e.g., at a sporting event or other activity), a user (e.g., an injured player or other person, a coach, a parent, a medical professional, an administrator, or the like) may request a medical assessment” [0140], “the query module 1102 may question an administrator about one or more signs the administrator may have observed in the user being assessed and/or diagnosed, such as a lack of balance, …, a balance examination, a coordination examination, or the like), and/or another observation.”). Examiner notes that testing for balance is a method of vestibular testing, as supported by [0038] of Applicant specification, as noted above.
determining a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the updated baseline of expected neurological functioning by evaluating the post impairment test data using a trained machine learning model ([0020], “A method, in one embodiment, includes assessing, on a computing device, a likelihood that a user has a concussion based on a voice analysis of one or more recorded baseline verbal responses and one or more recorded test case verbal responses.” [0044], “A voice module 104 may compare a user's answers to previous answers from when the user was healthy (e.g., to baseline answers).” [0167], “The interface module 1108 may display a baseline assessment and/or score next to a current (e.g., text case) assessment and/or score for comparison (e.g., side by side), may display a difference between a baseline assessment and/or score and a current (e.g., text case) assessment and/or score, or the like.” [0068], “Medical condition classifier 240 may use any appropriate techniques, such as a classifier implemented with a support vector machine or a neural network, such as a multi-layer perceptron.”); and
outputting an indication of the likelihood that the post-impairment test data is indicative of neurological impairment ([0068], “Medical condition classifier 240 may process the acoustic features and the language features with a mathematical model to output one or more diagnosis scores that indicate whether the person has the medical condition, such as a score indicating a probability or likelihood that the person has the medical condition and/or a score indicating a severity of the medical condition.”[0020], “a likelihood that a user has a concussion”).
Alberts in view of Kim are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts with Kim for the advantage of indicating “a likelihood that a user has a concussion” (Kim; [0007]).
Claim 7 is rejected for the same reasons as claim 1, as described above.
Regarding claim 8, Alberts in view of Kim teaches the method of claim 7. Alberts does not teach wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is based at least in part on baseline test data gathered from other individuals.
However, Kim more does teach wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is based at least in part on baseline test data gathered from other individuals ([0006], “A backend server device, in various embodiments, is configured to store at least baseline recorded verbal responses from a plurality of users, test case recorded verbal responses from a plurality of users, and/or assessments of a medical condition for at least the test case recorded verbal responses.” [0007], “method, in one embodiment, includes assessing, on a computing device, a likelihood that a user has a concussion based on a voice analysis of one or more recorded baseline verbal responses and one or more recorded test case verbal responses.”).
Alberts in view of Kim are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts with Kim for the advantage of incorporating “recorded baseline verbal responses” (Kim; [0007]).
Regarding claim 9, Alberts in view of Kim teaches the method of claim 7. Alberts does not explicitly teach wherein the trained machine learning model configured to classify post-impairment test data as indicative of neurological impairment based on training data selected from the series of additional baseline test data.
However, Kim teaches wherein the trained machine learning model configured to classify post-impairment test data as indicative of neurological impairment based on training data selected from the series of additional baseline test data ([0142], “The query module 1102, in one embodiment, questions and/or otherwise queries a user at a predefined health state, such as a known healthy state, a predefined stage of a medical condition, or the like, to collect one or more baseline voice recordings, training data, or other data.” [0167], “the interface module 1108 may provide a user with a baseline assessment and/or score based on baseline response data, a test case assessment and/or score based on test case response data, a follow-up assessment and/or score based on subsequent responses” [0082], “To train a model for diagnosing a medical condition, a corpus of training data may be collected… it may be known that the person had no concussion, or a mild, moderate, or severe concussion.” [0068], “Medical condition classifier 240 may process the acoustic features and the language features with a mathematical model to output one or more diagnosis scores that indicate whether the person has the medical condition, such as a score indicating a probability or likelihood that the person has the medical condition and/or a score indicating a severity of the medical condition.”). It would be obvious to tone of ordinary skill in the art that collecting training data when a patient has a known state (ie. healthy), which may include follow-up baseline test assessments, for training a machine learning model encompasses the above limitation.
Alberts in view of Kim are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts with Kim for the advantage of using a “training a mathematical model for diagnosing a medical condition” (Kim; [0088]).
Regarding claim 10, Alberts in view of Kim teaches the method of claim 7. Alberts further teaches wherein the impairment comprises a traumatic brain injury, and wherein the neurological impairment comprises a concussion ([0041], “This disclosure also provides systems and methods that can be utilized to implement a performance test to assess various aspects a patient's neurological and cognitive function. The patient can have a neurological condition that affects cognitive and motor performance, such as multiple sclerosis (MS) or other neurological disorders (e.g., Parkinson's, essential tremor, stroke, concussion, etc.). For example, the performance test can be used to determine the severity of the neurological condition in the patient.”). One of ordinary skill in the art understands that a concussion is a type of mild traumatic brain injury.
Regarding claim 11, Alberts in view of Kim teaches the method of claim 7. Alberts further teaches wherein the updated baseline of expected neurological functioning for the individual is determined by baseline test data including data gathered from at least one of a Post Concussion Symptom Scale (PCSS), visual eye movement testing, vestibular test using head stability during a gaze task, or cognitive testing ([0055], “The collection module 48, 58 can also aggregate data (AFTD) from any additional function test module 47, 57 into the test data (TD).” [0130], “enables a care provider (e.g. a physician) to monitor the patient's condition over time to determine the course of disease” [0081], “The cognitive processing speed test module 110 can also be programmed to provide additional measures beyond simple measure of accuracy.”).
Regarding claim 13, this claim is rejected for the same reasons as claim 1, as described above. Alberts further teaches a non-transitory computer-readable medium for storing programming instructions which cause a computer to perform a method for generating indications of neurological impairment ([0050], “ The memory 14 can include one or more non-transitory memory device configured to store machine readable instructions and/or data.”).
Regarding claim 19, Alberts in view of Kim teaches the method of claim 7. Alberts further teaches wherein the updating the baseline test of baseline of expected neurological functioning test data is performed without human intervention ([0049], “By implementing such testing in the system as part of a self-administered testing platform, related scoring and analysis can be generated by the computer automatically because data is collected by such computer, obviating the need for human involvement, and allowing error-free score generation.”).
Regarding claim 23, Alberts in view of Kim teaches the method of claim 7. Alberts further teaches wherein a one-to-many relationship exists between a respective neurological functioning vector and the neurological functioning test ([0004], “The MSFC is a three-part, standardized, quantitative, assessment instrument for use in clinical studies, particularly clinical trials of MS. The MSFC can produce scores for each of the three individual measures—walking, hand/arm control, and cognitive function—as well as a composite score.”). Examiner interprets the MSFC to be one test that produces three vectors.
Regarding claim 24, Alberts in view of Kim teaches the method of claim 7. Alberts does not teach wherein the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment comprises a score representing a probability distribution of a space comprises a range of results for the neurological functioning test.
However, Kim does teach wherein the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment comprises a score representing a probability distribution of a space comprises a range of results for the neurological functioning test ([0090], “The value for the indicator of the medical condition diagnosis may have two values (e.g., 0 if the person does not have the medical condition and 1 if the person has the medical condition) or may have a larger number of values (e.g., a real number between 0 and 1 or multiple integers indicating a likelihood or severity of the medical condition).” [0144], “One or more questions and/or prompts of the query module 1102 may allow the detection module 1106 to determine a Standardized Concussion Assessment Tool (SCAT) score, a SCAT2 score, a SCAT3 score, a SCAT5 score, a Glasgow Coma Score (GCS), a Maddocks Score, a Concussion Recognition Tool (CRT) score, and/or another concussion score.” [0045], “study participants with results of a questionnaire or other test, may provide a score similar to and/or on the same scale as a questionnaire or other test, or the like”). Under the broadest reasonable interpretation, one of ordinary skill in the art would understand that each of the standard concussion tools taught by Kim along with any questionnaires with outputs on a scale will output a score that will represent a probability distribution of a space comprising a range of results for the neurological functioning test because such questionnaires and tools will give a range of scores that represent no injury to high likelihood of injury.
Alberts in view of Kim are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts with Kim for the advantage of “dynamically assess[ing] brain health, function and cerebral activity” (Kim; [0002]).
Regarding claim 25, Alberts in view of Kim teaches the method of claims 7 and 24. Alberts further teaches wherein the range of results is associated with every result achievable for the neurological functioning test ([0004], “The EDSS rates disease severity using a 20 point scale, ranging from 0 to 10 in 0.5 point increments, with increasing numbers reflecting increased disability.”). Examiner notes that a range of 0-10 is associated with every result achievable for the specific test.
Regarding claim 26, Alberts in view of Kim teaches the system of claim 1. Alberts does not teach wherein a corresponding first value of the post-impairment test data exceeds a corresponding first value of the first baseline test data.
However, Kim does teach wherein a corresponding first value of the post-impairment test data exceeds a corresponding first value of the first baseline test data ([0090], “The value for the indicator of the medical condition diagnosis may have two values (e.g., 0 if the person does not have the medical condition and 1 if the person has the medical condition) or may have a larger number of values (e.g., a real number between 0 and 1 or multiple integers indicating a likelihood or severity of the medical condition).” [0020], “A method, in one embodiment, includes assessing, on a computing device, a likelihood that a user has a concussion based on a voice analysis of one or more recorded baseline verbal responses and one or more recorded test case verbal responses.” [0044], “A voice module 104 may compare a user's answers to previous answers from when the user was healthy (e.g., to baseline answers).”).
Alberts in view of Kim are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts with Kim for the advantage of “comput[ing] a score that indicates a probability or a likelihood that the person has the medical condition and/or a severity of the condition” (Kim; [0057]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961) further in view of Graham (Graham, Robert, Sports-Related Concussions In Youth: Improving The Science, Changing The Culture, 4 February 2014, National Academies Press).
Regarding claim 12, Alberts in view of Kim teaches the method of claim 11. Alberts further teaches wherein the cognitive testing includes at least: memory testing ([0081], “Other cognitive functions tested by the cognitive speed processing test module 110 can include memory recall, attention and mental fatigue.”),
reaction time testing ([0083], “The data collection module 134 can collect data related to the cognitive processing speed test. The data collection module 134 can record each response with a time stamp 142, sampling for responsive inputs at a suitable sample rate (e.g., about 60 Hz or a higher or lower rate) 144. The responsive inputs can also be recorded with respect to test parameters 146 (e.g., key and symbol layout). The data processing module 136 can include a time calculator 148 to calculate the time between the individual input responses.”), and
attention testing ([0081], “Other cognitive functions tested by the cognitive speed processing test module 110 can include memory recall, attention and mental fatigue.”),
each conducted during baseline or post-impairment assessment via a mobile device ([0095], “a first part of test can establish baseline static acuity data for the patient. Following the static visual acuity test, the contrast control can vary the contrast in a dynamic manner for a plurality of tests.” [0071], “the mobile computing device executing the test module 80 can be a tablet computer (e.g., an iPad tablet computer available from Apple, Inc. or another computer having a touch screen interface).” [0054], “Each of the applications 40, 50 can be stored in the memory 14 of FIG. 1 and be executed by the processor 16 of FIG. 1, for example. The applications 40, 50 each include machine readable instructions for an MS performance test (MSPT) and corresponding data that can be programmed to test and evaluate MS status and/or condition of a patient.”).
Alberts in view of Kim does not teach wherein the cognitive testing includes trail making testing.
However, Graham does teach wherein the cognitive testing includes trail making testing (Pg. 114-115, “Tests such as Trail Making, Paced Auditory Serial Attention Test, Digit Symbol Substitution, and Digit Span have long been used in documenting cognitive deficits in TBI.”).
Alberts in view of Kim further in view of Graham are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim with Graham for the advantage of “documenting cognitive deficits in TBI” (Graham; Pg. 115).
Claims 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961) further in view of Simon (US 20160022206).
Regarding claim 20, Alberts in view of Kim teaches the method of claim 7. Although Alberts teaches center of gravity, which is related to center of mass ([0087], “gyrometer data to the patient's center of gravity based on placement of the computing apparatus at a predetermined position during execution of the test module 160.”), Alberts in view of Kim does not explicitly teach wherein the first baseline test data comprises an absolute cumulative movement of the center of mass, a mean movement of the center of mass, a variance of movement of the center of mass, a standard deviation of movement of the center of mass, or a combination thereof.
However, Simon does teach wherein the first baseline test data comprises an absolute cumulative movement of the center of mass, a mean movement of the center of mass, a variance of movement of the center of mass, a standard deviation of movement of the center of mass, or a combination thereof ([0049], “Perhaps even just using a single 3-axis accelerometer to measure static balance in several postures with eyes closed could document the degree of stability, including enable an assessment of variance around a center-of-gravity or center-of-mass (COG/COM).”).
Alberts in view of Kim further in view of Simon are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim with Simon for the advantage of “validat[ing] and quantify[ing] the patient's dizziness and vertigo complaints” (Simon; [0099]).
Regarding claim 21, Alberts in view of Kim teaches the method of claim 7. Alberts further teaches wherein the updated baseline of expected neurological functioning comprises:
a first neurological functioning vector associated with the brainstem or parietal cortex of the first subject ([0045], “The one or more accelerometers can be configured to measure acceleration of the apparatus along one or more axis, such as to provide an indication of acceleration (e.g., an acceleration vector) of the apparatus in three dimensions. The one or more accelerometers can measure the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion or shock. Additionally, the one or more accelerometers can possess a high resolution (4 mg/LSB) that can enables measurement of inclination changes less than 1.0°, for example. The one or more accelerometers may provide various sensing functions, such as activity and inactivity sensing to detect the presence or lack of motion, direction of motion, the smoothness of motion, and if the acceleration on any axis exceeds a user-defined level.”). Examiner notes that the result of tilt tests are data vectors associated with parietal cortex or brainstem functioning, as supported by Applicant specification ([0038], “The tilt test measures some aspects of function in the parietal and brainstem balance centers.”).
Alberts in view of Kim does not teach a second neurological functioning vector associated with the brainstem or parietal cortex of the first subject.
However, Simon does teach a second neurological functioning vector associated with the brainstem or parietal cortex of the first subject ([0114], “As a non-limiting example, it is well known that vertigo, the eye movement of the subject is abnormal relative to healthy controls. As a surrogate for vertigo and dizziness, the subject could record their eye movement while standing still in place. Then, they could be asked to move slowly and record their eye movement. Then as a final return position, they could be asked to record eye movement at rest again. If in fact they have dizziness and/or vertigo, the saccadic movements of the eye, as evidence by the Pierce or King-Devick Saccade tests, Developmental Eye Movement test or an improvement on the DEM, should appear abnormal relative to their pre-injury or adaptive norm group.”). Examiner notes that the result of a King-Devick test is a data vector associated with parietal cortex or brainstem functioning, as supported by Applicant specification ([0035], “Visual eye movement may be measured by the King-Devick (K-D) test. The K-D test involves measuring saccades and vergence, thus measuring some aspects of frontal, parietal and brainstem eye movement centers.”).
Alberts in view of Kim further in view of Simon are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim with Simon for the advantage of “dynamically assess[ing] brain health, function and cerebral activity” (Simon; [0002]).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961) further in view of Gross (US 20180121608).
Regarding claim 22, Alberts in view of Kim teaches the method of claim 7. Alberts in view of Kim does not teach wherein the updating the baseline of expected neurological functioning test data comprises modifying the first baseline test data based on (i) one or more average values associated with the first baseline test data and/or a respective additional baseline test data in the series of additional baseline test data and/or (ii) one or more outlier values associated with the first baseline test data and/or the respective additional baseline test data in the series of additional baseline test data ([0237], “the pre-treatment microstate parameters values serve as a baseline or as a reference for the following extracted microstate parameters values”)
However, Gross does teach wherein the updating the baseline of expected neurological functioning test data comprises modifying the first baseline test data based on (i) one or more average values associated with the first baseline test data and/or a respective additional baseline test data in the series of additional baseline test data and/or (ii) one or more outlier values associated with the first baseline test data and/or the respective additional baseline test data in the series of additional baseline test data (Table 1, “Eye Tracking Variable Example parameters Search Rate Score May include the number of fixations divided by time for each repetition and averaged across assessments.” [0106], “display statistics associated with an evaluated task 804 of a visual assessment, e.g., how a user's performance may vary over time,” [0028], “Visual assessments may include any evaluations of eye movement data, including assessments of a user's visual performance and/or assessments of a user's neurological state… visual performance may refer to a user's eye movement being normal (…), above normal (...), or impaired (e.g., due to injury or a neurological disorder).” [0113], “the visual performance exam may include a Smooth Pursuit Eye Movements (SPEM) test, e.g., for indicating the presence of traumatic brain injury.”). Examiner interprets averaging scores across assessments to encompass (i).
Alberts in view of Kim further in view of Gross are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim with Gross for the advantage of tracking “a user's progression in gain or loss of neurological function” (Gross; [0028]).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961) further in view of Tsai (US 20160073874).
Regarding claim 27, Alberts in view of Kim teaches the system of claim 1. Alberts in view of Kim does not teach the least one program further comprises instructions for displaying, in accordance with a determination the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment satisfies a threshold set of values, generating one or more instructions causing display of a warning at a first mobile device.
However, Tsai does teach the least one program further comprises instructions for displaying, in accordance with a determination the probabilistic determination of the likelihood that the post-impairment test data is indicative of neurological impairment satisfies a threshold set of values, generating one or more instructions causing display of a warning at a first mobile device ([0032], “Indicator 103f may include one or more lights, light emitting diodes, a display, a user interface, speaker, etc. that enables blink reflex device 100 to output an indication, notification, and/or sound that can be viewed or heard by an operator of blink reflex device 100 that identifies whether the subject suffers from a neurological condition and/or a level of severity of such a neurological condition” [0130], “if the change in blink reflex of the subject is not less than a second threshold, blink reflex device 100 may determine that it is likely that the subject is suffering from a significant neurological condition.” [0048], “User device 110 may include any computation or communication device, such as a wireless mobile communication device, that is capable of communicating with network” [0059], “User interface 220 may also, or alternatively, render video, images, audio, graphical, or textual information associated with a blink reflex of the subject for display to enable the subject or medical practitioner to determine whether the subject potentially suffers from a neurological condition or the severity thereof.”). Examiner notes that the blink reflex device is also a mobile device.
Alberts in view of Kim further in view of Tsai are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim with Tsai for the advantage of “identif[ying] whether the subject suffers from a neurological condition and/or a level of severity of such a neurological condition” (Tsai; [0032]).
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Alberts (US 20160302710) in view of Kim (US 20180322961) further in view of Tsai (US 20160073874) and Karunanithi (US 20180254096).
Regarding claim 28, Alberts in view of Kim further in view of Karunanithi teaches the system of claims 1 and 27. Alberts in view of Kim does not teach wherein the threshold set of values comprises a minimum baseline value and a maximum baseline value defined, at least in part, by the updated baseline of expected neurological functioning.
However, Karunanithi does teach wherein the threshold set of values comprises a minimum baseline value and a maximum baseline value defined, at least in part, by the updated baseline of expected neurological functioning ([0129], “The current activity level and, in particular the subject domain scores, are then compared to respective thresholds at step 620. The thresholds are typically in the form of a threshold range, corresponding to an expected range of domain scores, as determined using the reference domain scores. The ranges could be based solely on the absolute domain scores, with a fixed range either side of the score.” [0206], “if a subject is undergoing rehabilitation and their activity capabilities gradually improve, then it might be desirable to update the reference activity levels to reflect these changes.” [0004], “ADL scales have also been widened to assessment that accommodate more sophisticated functional requirements full range of activities necessary for independent living such as ability to cope with financial transactions, neurological disorders and cognitive impairment prevalent with aging.” [0248], “Rescaled ADLs measured through this time period are shown in FIG. 11B. During the last week, due to neurological decline event, the subject's home activities were confined to the bedroom where the subject was found to be inactive.”). Under the broadest reasonable interpretation, the ADL score can be used as a measure for expected neurological functioning, as it is tuned to serve as a proxy for understanding neurological health and how it affects other aspects of a person’s life.
Alberts in view of Kim further in view of Tsai and Karunanithi are considered analogous to the claimed invention because they are in the field of neurological evaluation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Alberts in view of Kim further in view of Tsai with Karunanithi for the advantage of understanding “typical variations that would be expected for the subject” and “determining a condition suffered by the subject” (Karunanithi; [0032]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID CHOI whose telephone number is (571)272-3931. The examiner can normally be reached M-Th: 8:30-5:30 ET.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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