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 .
Notice to Applicant
Receipt of Applicant’s Amendment filed July 29, 2025 is acknowledged.
Response to Amendment
Claims 1, 3, 5-7, 12-13, and 18 have been amended. Claims 2, 4, 8-10, and 14-16 have not been modified. Claims 1-18 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed July 29, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 8 of Remarks:
Regarding Step 2A, Prong One, Applicant argues that the claims are not abstract as the claims have been amended to recite wherein “the baseline test data including data gathered from a vestibular test using sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope”. The Examiner respectfully disagrees.
Regarding mental processes, the amended claim limitation recites wherein data is gathered and stored within a memory storage unit, specifying that data must be gathered from a specific test via an accelerometer or a gyroscope. The claim is analogous to the MPEP 2106.04(a)(2) IIIA example of a claim that recites “collecting information, analyzing it, and displaying certain results of the collection and analysis” (Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)). Within the scope of the claim, the accelerometer and gyroscope are simply generic sensors that provide data that can be collected by mental processes for further analysis. Additionally, there is no indication that a person cannot read the output of an accelerometer, a gyroscope, or device with a vestibular test output and include the gathered data in storage. Thus, the Examiner maintains that the claims are abstract for mental processes.
Regarding Step 2A, Prong Two, Applicant argues that the use of specific sensors (the accelerometer and gyroscope) improve data fidelity and standardization and the use of a trained machine learning model provides an improvement to animation. Examiner notes that the accelerometer and gyroscope are simply generic input devices that perform the insignificant extra-solution activity of gathering data. No specific, technical improvements are being made to the sensor devices themselves to provide an improvement in data fidelity and standardization over other existing sensors. Rather, the use of said sensors is analogous to “apply it” to perform the insignificant extra-solution activity of gathering data.
Furthermore, the use of a trained machine learning model is similar in that the model is simply applied to perform the abstract idea of certain methods of human activity of determining a likelihood that a patient has neurological impairment, which is an activity that is routinely performed by neurologists for their patients. No specific, technical improvements are being made to the field of machine learning that improves the machine learning model over other solutions in the field.
Regarding Step 2B, Applicant argues that the use of sensors in combination with ML-based temporal baseline modeling does not reflect generic computing. Examiner respectfully disagrees, as this combination of additional elements is known. Please see:
Kim (US 20180322961): [0145], “In one embodiment, the response module 1104 is configured to receive response data (e.g., voice data of a verbal response, text data of a typed response, sensor data, image and/or video data from a camera or other image sensor, touch input from a touchscreen and/or touchpad, movement information from an accelerometer and/or gyroscope, or the like), in response to one or more questions and/or other queries from the query module 1102.” [0070], “medical condition classifier 240 may use other features, which may be referred to as non-speech features”
Costa (US 20140330159): [0022], “The body motion can be captured with methods including, but not limited to, cameras, accelerometers, gyroscopes, magnetometers, and force sensitive resistors.”[0045], “In accordance with embodiments of the present invention, an integrative neuromotor index can be calculated as a function of one or more of the neuromotor performance signals or indices. These parameters can be directly combined through addition, by comparing a vector, or through implementation of a model. In one embodiment, this model can be developed using principal component analysis, support vector machines, neural networks, or other machine learning algorithm.”
Applicant argues 35 U.S.C. §112b Rejections, pg. 9 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the 112b rejection. However, Applicant amendments have caused a new 112 rejection. Please see below.
Applicant argues 35 U.S.C. §103 Rejections, pg. 10 of Remarks:
Applicant argues that Alberts in view of Kim does not teach the amended limitations. Applicant arguments are moot as new art is applied to address the amended limitations.
Applicant further argues that combining Alberts with Kim lacks an articulated rationale and reasonable expectation of success as the unrelated diagnostic modalities (speech vs motion/gaze) solve different problems and would not be naturally combined. Examiner respectfully disagrees for the following reasons:
(1) Kim also teaches including motion as input to the data processing: [0143], “For example, 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, motor incoordination, disorientation, confusion, loss of memory, a blank or vacant look, a visible facial injury or other injury, observed results of a physical examination (e.g., range of motion, tenderness, sensation, strength, a balance examination, a coordination examination, or the like), and/or another observation.”
[0145], “In one embodiment, the response module 1104 is configured to receive response data (e.g., voice data of a verbal response, text data of a typed response, sensor data, image and/or video data from a camera or other image sensor, touch input from a touchscreen and/or touchpad, movement information from an accelerometer and/or gyroscope, or the like), in response to one or more questions and/or other queries from the query module 1102.”
(2) The problem being solved by both Alberts and Kim are one and the same as the instant application.
Alberts recites: [0003], “This disclosure relates generally to a performance test for evaluation of neurological function, and more specifically to a system and method that can implement the performance test to evaluate a patient's neurological and/or cognitive function.” [0041], “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.).”
Kim recites: [0007], “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.” [0043], “A voice module 104 may interact with a user, asking questions verbally, recording the user's vocal responses, determining whether a response is accurate, or the like. For certain protocols, a voice module 104 may ask one or more questions multiple times (e.g., two times, three times, or the like) before moving on to a subsequent question, or the like. Based on the voice audio data, a voice module 104 may assess and/or diagnose one or more diseases or other medical conditions (e.g., concussion, depression, stress, stroke, cognitive well-being, mood, honesty, Alzheimer's disease, Parkinson's disease, cancer, or the like).”
(3) The instant claims do not preclude inclusion of speech data as the independent claim recites: “the baseline test data including data gathered from a vestibular test”. Under the broadest reasonable interpretation, the data may also include speech data, as speech has been demonstrated to be relevant to neurological functioning as evidenced by the RED FLAGS section of Figure C-3 of Graham.
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(4) It would also be reasonable for a person of ordinary skill in the art to combine the Alberts and Kim as both use the same cognitive functioning tests.
[0082] of Alberts recites: “Other cognitive functions tested by the cognitive speed processing test module 110 can include memory recall, attention and mental fatigue.”
[0049] of Kim recites: “To monitor memory loss, in certain embodiments, a voice module 104 may audibly list words and/or numbers to a user and ask the user to repeat them back, may display a series of pictures to the user and ask the user to repeat back a description of the series of pictures, or the like, and monitor changes in an accuracy of the user's responses over time, indicating memory loss and a decreased quality of life.”
Applicant further argues that Graham is insufficient to teach the subject matter taught by Kim. Examiner notes that Graham has not been applied to teach real-time neurological testing, implementation of cognitive test types, and specific user interface or system design details.
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.
Claims 1-6 are 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.
Claims 1 and 5 introduce new matter that is not supported by Applicant specification. Specifically, “vestibular test using sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope” in claim 1 and “vestibular test using head stability during a gaze task ” in claim 5.
Claims 2-6 are rejected by virtue of their dependency on claim, and claim 6 is further rejected by virtue of its dependency on claim 5.
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 1-18 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 (1-18). Accordingly, claims 1-18 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, method claim 7, and product claim 13 as the claims that represents the claimed invention for analysis.
Claim 1:
A system for generating indications of neurological impairment, the system comprising:
a network interface configured to communicate with mobile devices via a computer network, and to receive test data gathered from neurological functioning tests performed by the mobile devices;
a memory storage unit for storing baseline test data gathered from the neurological functioning tests, the neurological functioning tests having been performed on individuals at predetermined time intervals via the mobile devices, the baseline test data including data gathered from a vestibular test using sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope; and
a processor in communication with the network interface and the memory storage unit, the processor configured to:
update a baseline of expected neurological functioning for an individual based on the baseline test data;
after receiving post-impairment test data gathered from a neurological functioning test performed on the individual after an impairment via a mobile device associated with the individual, probabilistically determine a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the baseline of expected neurological functioning using a trained machine learning model; and
output 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. Please see Example 47, claim 2, which recites an abstract idea of mental processes despite claiming usage of a machine learning model.
Accordingly, the claim recites an abstract idea.
Claim 7:
A method for generating an indication of neurological impairment, the method comprising:
gathering baseline test data from neurological functioning tests performed on an individual at predetermined time intervals, the baseline test data including data gathered from a vestibular test from an accelerometer or a gyroscope;
updating a baseline of expected neurological functioning test data for the individual based on the baseline test data;
after an impairment, gathering post-impairment test data from a neurological functioning test performed on the individual;
probabilistically determining a likelihood that the post-impairment test data is indicative of neurological impairment in the individual based on the baseline of expected neurological functioning 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.
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. These encompass gathering data, updating a baseline based on the 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.
Claim 13:
A non-transitory computer-readable medium for storing programming instructions which cause a computer to perform a method for generating indications of neurological impairment, the method comprising:
executing an application to perform a neurological functioning test on an individual;
gathering test data from the neurological functioning test;
transmitting the test data to a server;
receiving, from the server, an indication of a likelihood that the test data is indicative of neurological impairment in the individual, the likelihood having been probabilistically determined by a trained machine learning model based on a baseline of expected neurological functioning developed by gathering baseline test data from neurological functioning tests performed on the individual at predetermined time intervals the baseline test data including data gathered from a vestibular test from an accelerometer or a gyroscope; and
outputting the indication of the likelihood that the 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.
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 data and outputting an indication of the likelihood.
Accordingly, the claim recites an abstract idea.
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 system, a network interface, mobile devices, a computer network, a memory storage unit, an accelerometer, a gyroscope, a processor, a trained machine learning model (1)
The Examiner notes that the independent claims recite insignificant extra-solution activities such as receiving, transmitting, storing, and selecting for output data. See also MPEP 2106.05(g).
The Examiner further notes that the executing an application step of Claim 13 is also taught at a high level of generality. The Examiner cites [0045] of Applicant specification which recites: “At block 402, an application, such as testing application 128, is executed to perform a neurological functioning test on an individual. The testing application 128 may be executed on mobile device 120.” No specific, technical improvements are being made to the technology of software applications.
Any and all computing devices 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. The Examiner cites [0033] of Applicant specification: “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.
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. The Examiner cites [0069-0070] of Applicant specification: “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 the input data.
The communication network is also 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. The Examiner cites [0019] of Applicant specification: “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.
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:
Claims 2, 8, and 14: These claims recite 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 recites an insignificant extra-solution activity of selecting a data type for manipulation. See also MPEP 2106.05(g).
Claim 3, 9, and 15: These claims recite wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is further based at least in part on trained machine learning model configured to classify post-impairment test data as indicative of neurological impairment based on training data selected from baseline test data; which teaches using a machine learning model at a high level of generality.
Claim 4 and 10: These claims recite 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 5, 11, and 17: These claims recite wherein the 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 recites an insignificant extra-solution activity of selecting a data type for manipulation. See also MPEP 2106.05(g).
Claim 6, 12, and 18: These claims recite wherein the cognitive testing includes at least: memory testing, trail making testing, reaction time testing, and attention testing, each configured to be administered via the mobile device and analyzed by the processor; which recites an insignificant extra-solution activity of selecting a data type for manipulation. See also MPEP 2106.05(g).
Claim 16: This claim recites wherein the test data is gathered after an impairment, and wherein the impairment comprises a traumatic brain injury, and wherein the neurological impairment comprises a concussion; which teaches an abstract idea of mental processes, such as timing the data gathering process. This claim also serves to further limit the type of impairment.
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 for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements 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)).
The use of sensors in combination with ML-based temporal baseline modeling is known. Please see:
Kim (US 20180322961): [0145], “In one embodiment, the response module 1104 is configured to receive response data (e.g., voice data of a verbal response, text data of a typed response, sensor data, image and/or video data from a camera or other image sensor, touch input from a touchscreen and/or touchpad, movement information from an accelerometer and/or gyroscope, or the like), in response to one or more questions and/or other queries from the query module 1102.” [0070], “medical condition classifier 240 may use other features, which may be referred to as non-speech features”
Costa (US 20140330159): [0022], “The body motion can be captured with methods including, but not limited to, cameras, accelerometers, gyroscopes, magnetometers, and force sensitive resistors.”[0045], “In accordance with embodiments of the present invention, an integrative neuromotor index can be calculated as a function of one or more of the neuromotor performance signals or indices. These parameters can be directly combined through addition, by comparing a vector, or through implementation of a model. In one embodiment, this model can be developed using principal component analysis, support vector machines, neural networks, or other machine learning algorithm.”
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 2-6, 8-12, and 14-18, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2-6, 8-12, and 14-18, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-6, 8-12, and 14-18, 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 1-18 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 1-5 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 Berme (US 9066667).
Regarding claim 1, Alberts teaches a system for generating indications of neurological impairment, the system comprising:
a network interface configured to communicate with mobile devices via a computer network, and to receive test data gathered from neurological functioning tests performed by the mobile devices ([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.”);
a memory storage unit for storing baseline test data gathered from the neurological functioning tests, the neurological functioning tests having been performed on individuals at predetermined time intervals via the mobile devices ([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).” [0142], “the data from these tests can be aggregated at the computing device and transmitted to a provider database via a network” [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)”). The Examiner interprets tracking a condition of a patient using test data over time to encompass a predetermined time interval.
And a processor in communication with the network interface and the memory storage unit ([0099], “The computing device 310 is programmed with instructions executable (e.g., by one or more hardware processor) to perform one or more test modules to evaluate a patient's condition that affects cognitive and/or motor performance”), the processor configured to:
update a baseline of expected neurological functioning for an individual based on the 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 wherein the vestibular test uses sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope; after receiving post-impairment test data gathered from a neurological functioning test performed on the individual after an impairment via a mobile device associated with the individual, probabilistically determine a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the baseline of expected neurological functioning using a trained machine learning model; and output an indication of the likelihood that the post-impairment test data is indicative of neurological impairment.
However, Kim does teach after receiving post-impairment test data gathered from a neurological functioning test performed on the individual after an impairment via a mobile device associated with the individual, probabilistically determine a likelihood that the post- impairment test data is indicative of neurological impairment in the individual based on the baseline of expected neurological functioning a trained machine learning model ([0007], “A method, in certain embodiments, includes re-querying a user, in response to a potential concussion event, with one or more questions using a user interface of a computing device. In some embodiments, a method includes recording, on a computing device, one or more test case verbal responses of a user to one or more re-queried questions. 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.” [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.” [0059], “For example, a person may speak to a mobile device 110 and mobile device 110 may record the speech and transmit the recorded speech data to medical condition diagnosis service 140 over network 130.” [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 output 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]).
Although Alberts teaches a data gathered from a vestibular test from an accelerometer or a gyroscope ([0088], “FIG. 14 depicts an example of a balance test module 180 that can be configured to evaluate a patient's balance based on a static center-of-gravity movement. The balance test module 180 can determine a volume of an ellipsoid in three-dimensional space corresponding to the center-of-gravity movement of the patient, demonstrated as function 182... Data from sensors (e.g., one or more accelerometers, magnetometers and a gyroscope) can be collected during each test and a corresponding score can be computed based on such results.”), Alberts in view of Kim does not teach wherein the vestibular test uses sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope.
However, Berme does teach wherein the vestibular tests uses sensor measurements of head movement stability during a gaze stabilization task collected via an accelerometer or a gyroscope (Col. 3, lines 31-40, “generate a quantitative assessment of gaze stabilization based upon the computed maximum head velocity or head speed at which the subject is capable of correctly identifying one or more configurations of the one or more visual objects. In a further embodiment of this aspect of the present invention, the motion sensing device comprises at least one of an accelerometer, a gyroscope, and a magnetometer. In yet a further embodiment, the motion sensing device is configured to be attached to the head of the subject by means of an adjustable band.”). It would be obvious to one of ordinary skill in the art that high head velocity or speed would indicate low head movement stability.
Alberts in view of Kim further in view of Berme 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 Berme for the advantage of providing a “method for testing the dynamic visual acuity and gaze stabilization of a subject that is capable of being quickly implemented, and produces accurate results” (Berme; Col. 2, lines 30-32).
Regarding claim 2, Alberts in view of Kim further in view of Berme teaches the system of claim 1. 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 3, Alberts in view of Kim further in view of Berme teaches the system of claim 1. Alberts does not explicitly teach wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is further based at least in part on trained machine learning model configured to classify post-impairment test data as indicative of neurological impairment based on training data selected from baseline test data.
However, Kim teaches wherein probabilistically determining the likelihood that the post-impairment test data is indicative of neurological impairment is further based at least in part on trained machine learning model configured to classify post-impairment test data as indicative of neurological impairment based on training data selected from baseline test data ([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. 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.”).
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 “Medical condition classifier… to output one or more diagnosis scores that indicate whether the person has the medical condition” (Kim; [0068]).
Regarding claim 4, Alberts in view of Kim further in view of Berme teaches the system of claim 1. 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 5, Alberts in view of Kim further in view of Berme teaches the system of claim 1. Alberts further teaches wherein the 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 (PCS S), visual eye movement testing, vestibular test using head stability during a gaze task, or cognitive testing ([0081], “The cognitive processing speed test module 110 can also be programmed to provide additional measures beyond simple measure of accuracy.”).
Claim 6 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 Berme (US 9066667) and Graham (Graham, Robert, Sports-Related Concussions In Youth: Improving The Science, Changing The Culture, 4 February 2014, National Academies Press).
Regarding claim 6, Alberts in view of Kim further in view of Berme teaches the system of claims 1 and 5. 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 configured to be administered via