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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 19 May 2026 has been entered.
Response to Amendment
The amendments filed 19 May 2026 have been entered. Claims 1 – 7, 9 – 12, and 14 - 15 are pending. Applicant’s amendments have not overcome each and every rejection under 35 U.S.C. 101 or 35 U.S.C. 103 applied to the claims.
Claim Rejections - 35 USC § 112
Claims 1 – 7, 9 – 12, and 14 - 15 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.
Claim 1 (lines 23 – 29) recites the term “response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object…including…hand position.” There is no particular description of how “all actions of the user” can be measured using the hand tracking sensor, the eye tracking system, and the head moment system. Particularly, as an example, given that one of the actions of the users is head position, it is not described how the hand tracking sensor would measure head position. Further, “all actions of the user to designate the location…of the software-generated object” could include a verbal action of declaring that the object is “there”. There is not adequate description of how truly all actions of the user are being measured. Therefore, adequate disclosure is needed. Claims 2 – 7, 9 – 12, and 14 - 15 are similarly rejected due to their dependence on Claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 7, 9 – 12, and 14 - 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (lines 23 - 26) recites the limitation “response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object.” It is unclear if each the hand tracking sensor, eye tracking system, and head movement sensor are intended to track “all actions of the user”. For example, is the head movement sensor intended to track the hand movement of the user to designate the location of the software-generated object? For the purposes of examination, the term “response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object” is deemed to claim “response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object”. Claims 2 – 7, 9 – 12, and 14 - 15 are similarly rejected due to their dependence on Claim 1.
Claim 4 (line 4) recites the term “using a statistical function or an artificial intelligence function”. It is unclear if this statistical function or artificial intelligence function are intended to be the same or different than the previously-recited statistical function or artificial intelligence function. For the purposes of examination, the term “using a statistical function or an artificial intelligence function” is deemed to claim “using a symptom index statistical function or a symptom index artificial intelligence function”.
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, 9 – 12, and 14 - 15 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.
Regarding Claim 1, the claim recites "an act or step, or series of acts or steps" to detect neurological impairment of a user using examination of sensorimotor control, and is therefore a process, which is a statutory category of invention (Step 1). The claims are then analyzed to determine whether they are directed to any judicial exception (Step 2A, Prong 1).
Each of Claims 1 – 7, 9 – 12, and 14 - 15 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 7, 9 – 12, and 14 - 15 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 – 7, 9 – 12, and 14 - 15 recites an abstract idea.
Specifically, Claims 1 – 7, 9 – 12, and 14 - 15 recite (underlined are observations, judgements, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 1:
A method to detect neurological impairment of a user using examination of sensorimotor control, the method comprising:
positioning a head-mounted display on the user's head and a hand tracking sensor in a hand of the user, the head-mounted display placing the user in a virtual or augmented reality environment and including an eye tracking system and a head movement sensor, the hand tracking sensor for tracking position and movement of the hand of the user relative to other objects displayed in the virtual or augmented reality environment;
presenting, on the head-mounted display by a processor executing software stored on a hardware storage device, the user with a software-generated object in the virtual or augmented reality environment;
providing, on the head-mounted display by the processor executing the software, instructions directing the user to execute one or more sensorimotor activities relating to the software- generated object within the virtual or augmented reality environment;
recording, on the hardware storage device by the processor executing the software
object data describing location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment at fixed time intervals between 45 and 500 measurements per second during the sensorimotor activities; and
response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment at the fixed time intervals during the sensorimotor activities, including trigger activation, head position, gaze direction, head orientation, visible area of the pupil, postural sway, or hand position;
generating, by the processor executing the software, a user sensorimotor control index calculated from the object data and the response data using a statistical function or an artificial intelligence function trained to determine inclusion of the user in a designated population; and
determining, by the processor executing the software, neurological impairment of the user by comparing the sensorimotor control index with an expected value.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claim 1, these limitations include:
observing and judgment to evaluate a user sensorimotor control index calculated from the object data and the response data using a statistical function or an artificial intelligence function trained to determine inclusion of the user in a designated population
observing and judgment to determine neurological impairment of the user by observation and judgment to compare the sensorimotor index with an expected value.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Similarly, Dependent Claims 2 – 7, 9 – 12, and 14 - 15 include the following abstract limitations, in addition the aforementioned limitations in Independent Claims 1 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
generating a symptom index calculated from the symptom data using a statistical function or an artificial intelligence function
observing and judgment of a symptom index calculated from the symptom data using a statistical function or an artificial intelligence function
assigning a test grade based upon the sensorimotor index;
observation and judgment of a test grade based upon the sensorimotor index;
determining if the user has a neurological impairment based upon the test grade.
observation and judgment of if the user has a neurological impairment based upon the test grade.
comparing the user sensorimotor index to at least one other sensorimotor index;
observation and judgment to compare the user sensorimotor index to at least one other sensorimotor index
determining if the user has a neurological impairment based upon the comparison with the at least one other sensorimotor index.
Observation and judgment to diagnose if the user has a neurological impairment based upon the comparison with the at least one other sensorimotor index.
at least one other sensorimotor index ids generated using statistical computation or machine learning artificial intelligence computation.
observation and judgment of at least one other sensorimotor index generated using statistical computation evaluation
make a determination about the type of impairment for each sensorimotor activity or a combination of one or more of the sensorimotor activities when completed together
observation and judgment to make a determination about the type of impairment for each sensorimotor activity or a combination of one or more of the sensorimotor activities when completed together;
As claimed, the aforementioned limitations are mental processes or mathematical algorithms under the 2019 PEG that would be performed by a biomedical or engineering professional using their education, background, and experience. Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in Independent Claim 1 (and its dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in Claims 1 – 7, 9 – 12, and 14 - 15), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
head-mounted display
hand tracking sensor
eye tracking system
head movement sensor
processor
software
hardware storage device
screen for each eye
machine learning artificial intelligence
accelerometer sensor
gyroscope sensor
magnetometer
Additional elements recited include a head-mounted display to place the user in a virtual or augmented environment, one or more sensor to record, one or more processor, and one or more hardware storage device. Each of these components is recited at a high level of generality. These generic hardware component limitations for the “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer” are no more than mere instructions to apply the exception using generic computer components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from independent claim 1 include pre-solution activity limitations, such as:
positioning a head-mounted display on the user's head and a hand tracking sensor in a hand of the user, the head-mounted display placing the user in a virtual or augmented reality environment and including an eye tracking system and a head movement sensor, the hand tracking sensor for tracking position and movement of the hand of the user relative to other objects displayed in the virtual or augmented reality environment;
providing, on the head-mounted display by the processor executing the software, instructions directing the user to execute one or more sensorimotor activities relating to the software- generated object within the virtual or augmented reality environment;
presenting, on the head-mounted display by a processor executing software stored on a hardware storage device, the user with a software-generated object in the virtual or augmented reality environment;
recording, on the hardware storage device by the processor executing the software object data describing location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment at fixed time intervals between 45 and 500 measurements per second during the sensorimotor activities; and response data from the hand tracking sensor, the eye tracking system, and the head movement sensor describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment at the fixed time intervals during the sensorimotor activities, including trigger activation, head position, gaze direction, head orientation, visible area of the pupil, postural sway, or hand position;
In addition to the aforementioned extra-solution activity limitations in Independent Claim 1, additional extra-solution activity limitations recited in the Dependent Claims include:
wherein the sensorimotor activities are selected from the group consisting of: smooth pursuit; convergence eye movement; saccadic eye movement; peripheral visual acuity; object discrimination; gaze stability; head-eye coordination; cervical neuromotor control; and combinations thereof.
wherein the instructions are provided to the user as audio or visual instructions.
wherein the data includes one or more of object data, response data, or symptom data.
wherein the placing of the user in a virtual or augmented reality environment includes displaying a three-dimensional environment to the user through the head- mounted display.
wherein the head-mounted display includes a screen for each eye.
from the group consisting of a previous sensorimotor index generated from the user's prior data, a designated population without a known neurological impairment, and a designated population with a known neurological impairment.
wherein each of the hand tracking sensor and the head movement sensor comprise an accelerometer sensor, a gyroscope sensor, a magnetometer, or combination thereof.
wherein the user is neurologically impaired due to trauma, vascular aging, or other physiological processes.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer” as recited in independent Claim 1 and its dependent claims are generically recited computer and hardware elements which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract ideas identified above in independent Claim 1 (and its dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process) using rules (e.g., computer instructions) executed by a computer processor as claimed. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in in independent Claim 1 (and its dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claim 1 (and its dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 – 7, 9 – 12, and 14 - 15 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer” as recited in independent Claim 1 and its dependent claims.
The additional elements of the “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer” in claims 1 – 7, 9 – 12, and 14 - 15, as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per applicant’s specification, the “head mounted display” is described generically in [0054] “also includes a head-mounted display (HMD) 108, which is also referred to interchangeably herein as a head-mounted extended reality device, configured to display a virtual immersive environment to the user when worn by the user. The term HMD should be understood to be synonymous with similar terms referring to similar display devices such as "headset, "VR device," "VR display," "AR device," "AR display," and the like. In some embodiments, the HMD covers substantially all of the user's visual field and may permit simultaneous visual input and interaction with the physical environment and the virtual environment”. The “head mounted display” 108 is shown as a generic black-box component in Figure 2.
Per applicant’s specification, the “hand tracking sensor” is described generically in [0055] …accelerometers (e.g. to detect postural sway or reaction movement of the head and/or hand)” and [0060] “Optionally, one or more hand controllers with sensors measuring hand movements can be included with the HMD 108”.
Per applicant’s specification, the “eye tracking system” is described as an off-the-shelf component in [0056] “…an eye tracking system 112; commercially available system by HTC with the trade name Vive Pro Eye that contains a Tobii eye tracker”. The “eye tracking device” is shown in Figure 1 as black-box “Eye Tracking System” 112.
Per applicant’s specification, the “head movement sensor” is described generically in [0055] …accelerometers (e.g. to detect postural sway or reaction movement of the head and/or hand)” and [0075] “head tracker”; and “gyroscope head sensor” is described generically in [0055] “gyroscopes (e.g. to measure the position of the head and/or hand).
Per applicant’s specification, the “processor” is described generically in [0054] “includes one or more processors 104”; “Suitable processors 104 include, but are not limited to, microprocessors, video processors, application specific integrated circuits (ASICs), and systems on a chip (SOACs).” The “processor” is shown in Figure 1 as black-box “Processors” 104 and 134.
Per applicant’s specification, the “software” is described generically in [0059] with “Operating the user system 102 (e.g., by running appropriate software)” describing creating a generic “virtual immersive environment”; [0076] “voice recognition software”; [0077] “the computer software program is executed by a computer processor on the HMD, at a remote computer device, or both.”;
Per applicant’s specification, the “hardware storage device” is described generically in [0054] “…a memory 106 (e.g. in the form of one or more hardware storage devices); “Suitable types of memory 106 include RAM, ROM, DRAM, SRAM, and MRAM, which may be stored on a hardware storage device such as disk media, electronic, or other like bulk, long-term storage or high-capacity storage medium.” There is nothing particular to the structure of the “hardware storage device” that deems it more than well-understood, routine, or conventional. The “hardware storage device” is shown generically as both “memory” 136 and 106 black-box rectangles in Figure 1.
Per applicant’s specification, the “screen” is described generically in [0060] as part of a “virtual reality (VR) headset, with one display screen per eye”. The images displayed on the screen during sensorimotor activities are described in detail, but the hardware associated with the screen itself is associated with a generic BR headset device.
Per applicant’s specification, the “machine learning artificial intelligence” is described generically to be performed by [0097] “neural networks, gradient boosting, or support vector machines”.
Per applicant’s specification, the “sensors” are described generically in [0055] “Suitable sensors include, but are not limited to, sensors for measuring the position and/or movement of the head, hand, and/or other body parts” and are listen to be “accelerometers”, “gyroscopes”, and “magnetometers”. For named “sensors” in applicant’s specification, the “magnetometer” is described generically in [0055] “…and magnetometers” (e.g. to measure physical orientation of the user”; “accelerometer sensor” is described generically in [0055] …accelerometers (e.g. to detect postural sway or reaction movement of the head and/or hand)” and [0075] “head tracker”; and “gyroscope sensor” is described generically in [0055] “gyroscopes (e.g. to measure the position of the head and/or hand). There is nothing particular to the described structure of the “magnetometer”, “accelerometer sensor”, or “gyroscope sensor”, that deem them more than well-understood, routine, or conventional. In combination, the claimed terms associated with “sensors” consist of well-understood, routine, or conventional components used in a well-understood manner to perform a routine function of measuring body positioning with a VR headset device.
Accordingly, in light of Applicant’s specification, the claimed terms “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer” are reasonably construed as a generic computing device or hardware. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “head-mounted display”, “hand tracking sensor”, “eye tracking system”, “head movement sensor”, “processor”, “software”, “hardware storage device”, “screen for each eye”, “machine learning artificial intelligence”, “accelerometer sensor”, “gyroscope sensor”, and “magnetometer”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1 – 7, 9 – 12, and 14 - 15 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method of Claims 1 – 7, 9 – 12, and 14 - 15 is directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1 – 7, 9 – 12, and 14 - 15 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in independent Claim 1 (and its dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1 – 7, 9 – 12, and 14 - 15 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1 – 7, 9 – 12, and 14 - 15 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 – 7, 9 – 12, and 14 - 15 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 3, 5 - 7, 9 – 12, and 14 – 15 are rejected under 35 U.S.C. 103 as being unpatentable over Berme et. al., (US 10,342,473 B1) in view of Zidan et. al., (US 2020/0397288 A1).
Regarding Claim 1, Berme discloses A method to detect neurological impairment of a user using examination of sensorimotor control ([Abstract]), the method comprising:
positioning a head-mounted display on the user's head (Fig. 1, “eye position tracking device 124”) and a hand tracking sensor in a hand of the user (Fig. 1, “limb position detection device 128”), the head-mounted display placing the user in a virtual or augmented reality environment (Fig. 8 , 9, and 14; [Col 35, Lines 55 – 60] “the subject visual display device 106 may take other forms as well, such as a head-mounted display…”) and including an eye tracking system and a head movement sensor (Fig. 1, Fig. 2, “Head Position Detection Device/Eye Movement Tracking Device 122, 124”; ), the hand tracking sensor for tracking position and movement of the hand of the user relative to other objects displayed in the virtual or augmented reality environment (Fig. 8 , 9, and 14; Fig. 1, “limb position detection device 128”; [Col 33, Lines 49 – 53] “determine a position of one or more limbs of the subject 108 from the one or more third signals output by the at least one limb position detection device 128…”; [Col 33]; [Col 33, Lines 35 – 60]; [Col 35, Lines 55 - 60“the subject visual display device 106 may take other forms as well, such as a head-mounted display…”; Figs. 18A – 18D)(Examiner notes that the images on a screen when supplied on a head mounted display would broadly be a virtual environment.);
presenting, on the head-mounted display by a processor executing software stored on a hardware storage device ([Col 11, Lines 5 – 12] “the data acquisition/data processing device 104 of the system 100 of FIG. 1 comprises a microprocessor 104…”; Fig. 2), the user with a software-generated object in the virtual or augmented reality environment (Fig. 8 , 9, and 14; [Col 35, Lines 55 – 60] “the subject visual display device 106 may take other forms as well, such as a head-mounted display…”);
recording, on the hardware storage device by the processor executing the software ([Col 33, Lines 49 – 51] “…The data acquisition/data processing device 104 also is specially programmed…”; Fig. 2)
object data describing location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment at fixed time intervals between 45 and 500 measurements per second during the sensorimotor activities ([Col 37, Lines 40 – 57] “…follow a moving target on the screen of the subject visual display device 106 while the eye movement and eye position tracking device 124 is used to track the angular position of the subject's eyes…”; [Col 13, Lines 17 – 62] including “…eye movement and eye position tracking device 124 may capture at least sixty (60) frames per second…increase the minimum resolution of the camera to 250 frames per second (i.e., 250 Hz) by decreasing the image size being downloaded by approximately one-quarter…”); and
response data from the hand tracking sensor ([Col 28, Lines 51 – 59] “…signals from the IMUs…”), the eye tracking system ([Col 13, Lines 33 – 35] ‘…The one or more video cameras of the eye movement and eye position tracking device 124…capture at least sixty (60) frames per second…”), and the head movement sensor ([Col 28, Lines 51 – 59] “…signals from the IMUs…”), describing all actions of the user to designate the location, movement, position, or visual characteristics of the software-generated object within the virtual or augmented reality environment ([Col 37, Lines 40 – 57] “…follow a moving target on the screen of the subject visual display device 106…”’; [Col 27, Lines 36 – 65] “…determine a gaze direction of a subject during a balance test…the head position of the subject 108 is measured using the head position detection device 122 while at least one of the one or more limbs of the subject 108 and the head of the subject 108 are displaced by the subject 108…”; Fig. 13 and 14; [Col 19, Lines 44 – 54] “…data acquisition/data processing device 104…generate output data 148 that includes the x, y, and z coordinates of the center point of the pupil…”; ) at the fixed time intervals during the sensorimotor activities ([Col 13, Lines 33 – 35] ‘…The one or more video cameras of the eye movement and eye position tracking device 124…capture at least sixty (60) frames per second…”; [Col 28, Lines 51 – 59] “…all of the signals from the IMUs are treated as continuous functions of time…may be readily discretized to account for IMU sensor devices that take discrete time samples from a bandwidth-limited continuous signal”), including trigger activation, head position ([Col 27, Lines 36 – 65] “…head position of the subject 108 is measured…”), gaze direction ([Col 27, Lines 36 – 65] “…gaze direction…”), head orientation ([Col 28, Lines 20 – 24] “…the orientation and position of one or more body portions ( e.g., arms and head) of a subject
108 could be determined’)…”), visible area of the pupil, postural sway ([Col 20, Lines 31 – 42] “…the postural sway data for the subject 108…”), or hand position (Fig. 8 , 9, and 14; Fig. 1, “limb position detection device 128”; [Col 33, Lines 49 – 53] “determine a position of one or more limbs of the subject 108 from the one or more third signals output by the at least one limb position detection device 128…”);
generating, by the processor executing the software (Fig 2., “Data Acquisition/Daya Processing Device 104”), a user sensorimotor control index calculated from the object data and the response data using a statistical function ([Col 20, Lines 31 – 42] “determine a first numerical score for the subject 108 based upon the eye movement and eye position data, and a second numerical score for the subject 108 based upon the postural sway data. Then, the data acquisition/data processing device 104 may be specially programmed to combine the first numerical score with the second numerical score to obtain an overall combined sway and eye movement score for the subject.”; ([Col 21, Lines 30 – 36] “…the numbers for a normal subject are : 90 % accuracy , 400 deg / sec velocity , and 200 millisecond latency . All of these values may be summarized in a single number (i.e., a hybrid value) to quantify the eye movements…”; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”)(Examiner notes that determining the mean of data is broadly a statistical computation, and this contributes to the overall computation of the score”) or an artificial intelligence function trained to determine inclusion of the user in a designated population; and
determining, by the processor executing the software (Fig 2., “Data Acquisition/Daya Processing Device 104”), neurological impairment of the user by comparing the sensorimotor control index with an expected value ([Col 21, Lines 4 – 19] “…The final score results(s) may be compared with the score for a normal subject...”; [Col 21, Lines 4 – 19] “…When one or more of the individual scores or their product ( as illustrated above ) is not normal , this may be indicative of a possible concussion…”; [Col 41, Lines 1 – 25]).
Berme does not specifically disclose providing, on the head-mounted display by the processor executing the software, instructions directing the user to execute one or more sensorimotor activities relating to the software-generated object within the virtual or augmented reality environment. Berme does broadly disclose that the user is instructed on how and when to perform the tests ([Col 5, Lines 46 – 50] “…(v) instructing the subject to perform the first task…”), but Berme does not specifically disclose that this is by instructions on the head-mounted display.
Zidan teaches a headset system using 3d visual effects and instructions provided on the headset screen(s) for performing tests associated with vestibulo-ocular function, including smooth pursuit, saccades, and other tests to diagnose conditions such as concussion ([0095] – [0104]; [0106]; [Abstract]; Fig. 2). Specifically for Claim 1, Zidan teaches providing, on the head-mounted display by the processor executing the software ([0009]) instructions directing the user to execute one or more sensorimotor activities ([0207] “eye tests”, Table 1; As an example, for the pursuit test, [0257] “…the 3D medical headset 284 is activated to begin generating visual or audible instructions to the eyes of the subject 112”; [0258] “Follow the dot with your eyes.”) relating to the software-generated object ([0258] “the dot” as an example; [260] “..the particular type of graphic that is presented depends on the type of eye test being conducted”) within the virtual or augmented reality environment (Fig 34, [0046] “Fig 34 is a rear view of the 3d medical headset of Fig 32, illustrating the first frame of an example of a pursuit graphic”; Fig 33, [0045] “Fig 34 is a rear view of the 3d medical headset of Fig 32, illustrating an example of a 3D visual effect generated by the 3D medical headset)
Zidan provides a motivation to combine at [0258] with “In a third step, the 3D medical headset 284 displays a sentence that states "Follow the dot with your eyes." Alternatively, the 3D medical headset 284 can audibly generate this instruction by outputting an audible output played to the subject 112 via the ear assemblies 170.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that conveying the instructions to the user via the headset would allow for either visual instruction at the location where they are already looking for the test, or with auditory instructions as an alternative, giving multiple options to increase the probability that the subject is given adequate, understandable instruction.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Berme’s broad disclosure of instructing the user to perform the task while the subject wears a headset for sensorimotor testing with the instructions provided directly displayed by the headset taught by Zidan, creating a single sensorimotor testing system that provides the instructions directly on the headset display, thereby increasing the probability that the user see follows the instruction.
Regarding Claim 2, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 2, Berme discloses wherein the sensorimotor activities ([Column 22, Lines 36 – 47] “…different vestibular or ocular motor tests…”) are selected from the group consisting of:
smooth pursuit ([Column 22, Lines 36 – 47] “…test involving smooth pursuits…”);
convergence eye movement ([Column 22, Lines 36 – 47] “…near point convergence (NPC) test…”);
saccadic eye movement ([Column 22, Lines 36 – 47] “…test involving saccades…”);
peripheral visual acuity;
object discrimination;
gaze stability ([Column 22, Lines 36 – 47] “…test involving saccades…”; [Col 24, Lines 27 – 28] “…vestibular-ocular reflex (VOR) test evaluates the subject’s ability to stabilize vision…”);
head-eye coordination ([Column 22, Lines 36 – 47] “…test involving saccades…”; [Col 24, Lines 27 – 28] “…vestibular-ocular reflex (VOR) test evaluates the subject’s ability to stabilize vision as the head moves…”);
cervical neuromotor control; and combinations thereof.
Regarding Claim 3, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 3, Berme discloses wherein the instructions are provided to the user as audio or visual instructions ([Col 22, Lines 54 – 56] “…The subject is instructed by the clinician…”).
Regarding Claim 5, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 5, Berme discloses wherein the placing of the user in a virtual or augmented reality environment includes displaying a three-dimensional environment to the user ([Col 35, Lines 55 – 60] “…subject visual display device 106 may take other forms as well such as a head-mounted display, a heads-up display, or a 3-dimensional display…”).
Berme discloses a head-mounted display ([Col 35, Lines 55 – 60] “…subject visual display device 106 may take other forms as well such as a head-mounted display…”) and displaying a three-dimensional environment to the user ([Col 35, Lines 55 – 60] “…subject visual display device 106 may...a 3-dimensional display…), but Berme does not specifically disclose that the head-mounted display is able to be the same structure as a 3-dimensional display for displaying a three-dimensional environment to the user.
Zidan teaches displaying a three-dimensional environment to the user through the head- mounted display ([0194] “the 3D medical headset 284 is configured and operable to generate a 3D visual effect 286 that provides a 3D viewing experience to users”).
Zidan provides a motivation to combine at [0194] with “the 3D visual effect 286 can provide a user with an immersive viewing experience, creating the impression the user is located in a different physical place even though the user knows the user is wearing a headset…” and [0209] – [0210] “…As a result of the 3D visual effect 286, the subject 112 could rotate the subject's head
160 in various directions, and the viewable graphics would change to show different graphics or views based on the head rotations. This behavior by the subject 112 can compromise or skew the results of the pursuit eye movements test and other eye tests.…the head movement de-coupler 303, the medical assembly 110 is configured to de-couple head movements from any changes in the pursuit graphic 294 or in other graphics intended to be experienced without head movement…” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that being able to actively control the 3d-generated visualizations through the headset in response to head movement would be useful for immersion and also ensuring accurate test results for pursuit eye movement tests or others in which the graphics are intended to be generated independent of the subject’s head movement.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the sensorimotor testing using a head-mounted display or 3d display disclosed by Berme with the 3-d visual effect graphics on a display in a headset taught by Zidan, creating a single sensorimotor testing system that could use 3-d visual effects displayed on a headset display to administer sensorimotor tests, in an immersive, customizable way to ensure more accurate test results.
Regarding Claim 6, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 6, Berme discloses the head-mounted display ([Col 35, Lines 55 – 60] “…subject visual display device 106…head-mounted display…”)
Berme does not specifically disclose wherein the head-mounted display includes a screen for each eye.
Zidan teaches wherein the head-mounted display includes a screen for each eye ([0032] “Fig 20 is a rear isometric view of an embodiment of a display unit of the medical headset of Fig 10 illustrating a plurality of display devices”; Fig 20, “left screen” 238, “right screen 236”; [0176] “…the right screen 236 is operable to generate an image A, for example, to the right eye 161 while the left screen 238 simultaneously generates an image B, for example, to the left eye 163.”)
Zidan provides a motivation to combine at [0176] with ““…the right screen 236 is operable to generate an image A, for example, to the right eye 161 while the left screen 238 simultaneously generates an image B, for example, to the left eye 163…Accodingly…the display unit 198 produces a 3d visual effect 286…” and [0169] “…The view splitter 212 can cause the brain to perceive a three-dimensional (3D) visual effect 286…” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that having a screen for each eye would be useful for isolating the information shown to each eye, either to simulate “cover-the-eye” type testing to test one eye at a time, or to create a 3D visual effect by showing different images to each eye for the brain to perceive in 3d.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the sensorimotor testing using a head-mounted display or 3d display disclosed by Berme with the one screen for each eye in a headset taught by Zidan, creating a single sensorimotor testing system that could use individual screens for the eyes to either do single eye “cover-the-eye” type testing or create 3-d visual effects displayed on a headset display to administer sensorimotor tests in an immersive, customizable way to ensure more accurate test results.
Regarding Claim 7, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 7, Berme discloses wherein the step of determining, by the processor executing the software (Fig 2., “Data Acquisition/Daya Processing Device 104”), neurological impairment of the user ([Col 20, Lines 31 – 42]; [Col 21, Lines 4 – 19]) comprises:
assigning a test grade based upon the sensorimotor control index ([Col 21, Lines 34 – 46] “…the eye score to increase with increased abnormality…abnormal balance sway score and an abnormal eye score…”)(Examiner notes that the qualitative denotation of a score as “abnormal” is a “test grade” consistent with Applicant’s Specification description at [0048] of “Test grade" means a qualitative value representing the degree of sensorimotor control of an individual on a sensorimotor control test.”); and
determining if the user has a neurological impairment based upon the test grade ([Col 21, Lines 34 – 47] “…product of an abnormal balance sway score and an abnormal eye score results in a larger combined sway and eye movement score…as such, subjects or patients who are concussed…higher combined sway and eye movement score than subjects or patients who are not concussed…”)
Regarding Claim 9, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 9, Berme discloses wherein the step of determining, by the processor executing the software ([Col 40, Lines 62 – 63] “…data acquisition / data processing device 104 may be specially programmed…”; Fig. 2) neurological impairment of the user ([Col 21, Lines 4 – 19]) comprises:
comparing the user sensorimotor control index to at least one other sensorimotor index ([Col 21, Lines 4 – 19] “…The final score results(s) may be compared with the score for a normal subject...”; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”); and
determining if the user has a neurological impairment based upon the comparison with the at least one other sensorimotor index ([Col 21, Lines 4 – 19] “…When one or more of the individual scores or their product ( as illustrated above ) is not normal , this may be indicative of a possible concussion…”; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”)
Regarding Claim 10, Berme discloses as disclosed above, The method of claim 9. For the remainder of Claim 10, Berme discloses wherein the at least one other sensorimotor index is selected from the group consisting of a previous sensorimotor index generated from the user's prior data ([Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average combined score…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”), a designated population without a known neurological impairment ([Col 21, Lines 4 – 19] “…The final score results(s) may be compared with the score for a normal subject...”), and a designated population with a known neurological impairment.
Regarding Claim 11, Berme discloses as disclosed above, The method of claim 9. For the remainder of Claim 11, Berme discloses wherein the at least one other sensorimotor index is generated using statistical computation ([Col 21, Lines 30 – 36] “…the numbers for a normal subject are : 90 % accuracy , 400 deg / sec velocity , and 200 millisecond latency . All of these values may be summarized in a single number (i.e., a hybrid value) to quantify the eye movements…”; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”)(Examiner notes that determining the mean of data is broadly a statistical computation, and this contributes to the overall computation of the score”) or machine learning artificial intelligence computation.
Regarding Claim 12, Berme discloses as disclosed above, The method of claim 11. For the remainder of Claim 12, Berme discloses makes a determination about the type of impairment for each sensorimotor activity or a combination of one or more of the sensorimotor activities when completed together ([Col 21, Lines 30 – 36]; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”).
Berme does not disclose wherein the machine learning artificial intelligence makes a determination about the type of impairment for each sensorimotor activity or a combination of one or more of the sensorimotor activities when completed together.
Zidan teaches wherein the machine learning artificial intelligence (“AI module 396”) machine makes a determination about the type of impairment for each sensorimotor activity or a combination of one or more of the sensorimotor activities when completed together (All of [0265], from “The system logic 118 includes an artificial intelligence (AI) module 396, which includes one or more AI or machine learning algorithms…” to “…The medical assembly 110 outputs an examination output 127, which includes such diagnostic resource 388 that identifies one or more possible diagnoses 382 of one or more disorders.”).
Zidan provides a motivation to combine at [0269] with “The process of generating and processing of such time series data can be relatively complex, causing the medical assembly 110 to undergo relatively high demands for processing power and power consumption. In an embodiment, the AI module 396 includes machine learning algorithms, such as deep learning algorithms, to efficiently and accurately analyze such time series data…improvement to computer functionality…improved efficiency accuracy, speed, and performance”. A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using machine learning algorithms to analyze large quantities of time series data associated with the testing would be useful for increasing the efficiency and speed of the analytical task to classify impairment based on the test data.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the sensorimotor testing system with scoring results and determination of concussion based on mathematical analysis and test results disclosed by Berme with Zidan’s taught machine learning classification algorithm analysis to classify vestibulo-ocular testing data with possible diagnoses (including concussion), creating a single sensorimotor testing system with capabilities to efficiently and quickly classify test data to a diagnosis, such as concussion.
Regarding Claim 14, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 14, Berme discloses wherein each of the hand tracking sensor (Fig 1., “limb position detection device 128”; [Col 27, Lines 49 – 65] “..at least one limb position detection device 128 is positioned on one or more limbs of the subject 108. For example…FIG. 1, an inertial measurement unit (IMU) may be attached to one or both arms of the subject 108.”) and the head movement sensor (Fig. 1, “head position detection device 124”; [Col 3, Lines 22 – 24.] “…the head position detection device comprises at least one of the following : ( i ) one or more inertial measurement units…”) comprise an accelerometer sensor ([Col 28, Lines 20 – 24] “…orientation and position of one or more body portions (e.g., arms and head) of a subject 108…signals from the plurality of inertial measurement units…”; [Col 28, Lines 3 – 12] “each inertial measurement unit…inertial measurement units 212…triaxial (three-axis) accelerometer…”), a gyroscope sensor ([Col 28, Lines 3 – 12] “…triaxial…gyroscope…”), a magnetometer ([Col 28, Lines 3 – 12] “…triaxial…magnetometer…”), or combination thereof (Col 28, Lines 3 – 12] “…triaxial…accelerometer…gyroscope…magnetometer…”).
Regarding Claim 15, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 15, Berme discloses wherein the user is neurologically impaired due to trauma ([Col 40, Lines 61 – 66] “..the data acquisition / data processing device 104 may be specially programmed to determine whether or not a subject 108 has a particular medical condition (e.g. ., a traumatic brain injury (TBI)…”; [Col 40, Lines 22 – 28]), vascular aging, or other physiological processes ([Col 40, Lines 22 – 28] “…one or more of the following medical conditions may be assessed…neurological disorder or disease…”)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Berme et. al., (US 10,342,473 B1) in view of Zidan et. al., (US 2020/0397288 A1), further in view of Laukkanen, et. al., (“Brain Injury Vision Symptom Survey (BIVSS) Questionnaire”, Ref U on PTO-892).
Regarding Claim 4, Berme discloses as disclosed above, The method of claim 1. For the remainder of Claim 4, Berme discloses further comprising collecting symptom data from the user through self- report ([Col 23, Lines 18 – 22] “..during the performance of the smooth pursuits test, the following symptoms of the subject may be tracked and recorded by the clinician: (i) headache, (ii) dizziness, (iii) fogginess, and (iv) nausea”)(Examiner notes that the symptoms that are tracked are broadly “self-reported” by the patient’s body such that they are observable during testing to be documented.), and generating, by the processor executing the software (Fig 2., “Data Acquisition/Daya Processing Device 104”), a score using a statistical function ([Col 21, Lines 30 – 36] “…the numbers for a normal subject are : 90 % accuracy , 400 deg / sec velocity , and 200 millisecond latency . All of these values may be summarized in a single number (i.e., a hybrid value) to quantify the eye movements…”; [Col 41, Lines 1 – 25] including“…a mean first performance value…a mean second performance value…comparison of the initial average…to the subsequent average combine score of 150…determines that the first subject has “Possibly Sustained a Concussion…”)
Berme does not particularly disclose generating a symptom index calculated from the symptom data using a statistical function or an artificial intelligence function.
Laukkanen teaches a self-reporting questionnaire, the Brain Injury Vision Symptom Survey (BIVSS) Questionnaire, that can be scored in order to give insight into if the patient has TBI or not ([Abstract]) Specifically for Claim 4, Laukkanen teaches comprising collecting symptom data from the user through self-report ([Page 2, Left Column, Bottom] “BIVSS is a self-administered 28-item scaled survey…Participants responded to the frequency of symptoms…”; Table 1, including “headaches or dizziness after using eyes”) and generating a symptom index calculated from the symptom data using a statistical function ([Page 3, Bottom] “…Factor coefficients were created for each question using Anderson-Rubin transformations…factors scores with a mean of zero and standard deviation of one…”; [Page 2, Left Column, Top – 2nd Full Paragraph] “…Multiple logistic regression was used to evaluate the model on predicting TBI using the factors in the model. Sensitivity and specificity were reported based on the predicted probability of TBI assuming a 50% cutoff.”; Fig. 2 “Rasch scores…”) or an artificial intelligence function.
Laukkanen provides a motivation to combine at [Page 8, Left Column, 4th Full Paragraph] with “The BIVSS can contribute to future clinical care by helping vision care providers better understand the dimensions and patterns of visual symptoms after TBI. It may also help guide the
diagnostic examination and serve as a biomarker for rehabilitation.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using a quantifiable questionnaire to obtain qualitative patient symptom data would be useful in combination with sensorimotor test as a comparative measurand to identify conditions such as TBI, or quantify improvement or worsening or symptoms over time.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the observation and documentation of symptoms during sensorimotor testing relative to concussion disclosed by Berme with Laukkanen’s taught scorable BIVSS self-reporting questionnaire of symptoms that could be associated with TBI, creating a singles sensorimotor testing system with a quantifiable description of patient symptoms that can be used for diagnosis or quantify improvement or worsening or symptoms over time.
Response to Arguments
Applicant's arguments filed 19 May 2026 have been fully considered but they are not persuasive to overcome the 35 U.S.C. 101 and 35 U.S.C. 103 rejections.
Regarding the 35 U.S.C. 101 analysis:
The applicant argues at [Page 7, “Claim Rejections – 35 U.S.C. 101” Section] – [Page 9, Top] that the claims have been amended to clarify that the claim limitation cannot practically be performed in the human mind and that they do not fall in the “certain methods of organizing human activity” grouping of abstract ideas. Looking to the amended claims, the limitation of “providing…instructions directing the user to execute one or more sensorimotor activities relating to the software-generated object within the virtual or augmented reality environment” appears to be part of the pre-solution data gathering steps of obtaining data for the subsequently-recited abstract ideas of calculating a user sensorimotor control index and determining neurological impairment of the user using the gathered data. While the data-gathering is not a method of organizing human activity as an abstract idea, it is extra-solution data gathering obtained in a well-understood, routine, and conventional way. Relative to the claim’s overall patent-eligibility under 35 U.S.C. 101, the argument is not persuasive.
The applicant argues at [Page 9, 1st Full Paragraph] that the collection of multiple distinct types of discrete object data and response data at fixed time intervals between 45 and 500 measurements per second cannot be practically performed in the human mind. As recited in amended Claim 1, the elements that occur at 45 – 500 measurements per second are extra-solution data gathering using sensors and a processor as a tool in a well-understood, routine, and conventional way to obtain video, kinematic, and object data that are then fed into the abstract ideas of calculating a user sensorimotor control index and determining neurological impairment of the user using the gathered data. There is nothing particularly-claimed about the measurement intervals that indicates that the hardware storage device, processor, or sensors are more than well-understood, routine, and conventional in this data gathering. This pre-solution data-gathering at 45 – 500 measurements per second is used for the subsequently-recited abstract ideas in the claim, those abstract ideas being the aspects that can be performed in the human mind. The argument is not persuasive.
The applicant argues at [Page 9, 1st Full Paragraph] that calculating using a statistical function or an artificial intelligence function cannot be practically performed in the human mind. A calculation of a statistical function, such as computing an average of numeric data, is a common action performed by human researchers with the aid of time, equations, and paper (or a calculator or processor used in their routine way to process data). It is common for researchers to calculate a statistical function to show statistical significance of differences in data, using their mind, time, equations, and paper. Merely because the claims recite “by the processor” does not indicate that there are recited improvements to processor. The claims do not recite an improvement of the computer itself. Looking to MPEP 2106.05(a), I, “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality” include “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016).” The argument is not persuasive.
The applicant argues at [Page 9, “Improvements to the Functioning of Any Technology of Technical Field” Section] that the examiner’s eye cannot perceive or quantify impairment. Applicant describes at [0007] that “The detection of neurological injury is performed by a physician or other healthcare provider skill in neurologic examination techniques…” and [0131] “…Determination of concussion was based on physician diagnosis.”, both of which indicate that an examiner’s eye can broadly “perceive impairment”. As recited, the impairment itself is not quantified. Rather, it is determined that there is a neurological impairment based on comparing the sensorimotor control index (number) with an expected value (a number). It is routine for a human medical professional to compare two values (or a value to a threshold) to determine if they are thus diagnosed with a condition (such as reviewing chart medical results). Further, there is nothing recited regarding what criteria the user sensorimotor control index is calculated by. As recited, the “user sensorimotor control index” could broadly be quantified as a 0 for does not appear to have control or 1 for appears to have control. There is no particular granularity or specificity to the quantification or particular improvement to a machine recited. The argument is not persuasive.
Regarding the Subject Matter Eligibility Declaration of Jennifer C. Reneker, PT, Ph.D. under 37 CFR 1.132:
The applicant argues at [Affidavit Page 2, Paragraph 7] – [Affidavit Page 3, 1st Full Paragraph] that the collection of multiple distinct types of discrete object data and response data at fixed time intervals between 45 and 500 measurements per second cannot be practically performed in the human mind. Applicant argues the collection of 18 – 26 distinct types of discreet ([discrete]) data at 90 measurements per second. There are not 18 – 26 distinct types of discrete data recited in the claims, nor are there particular data manipulation limitations claimed regarding these 18 – 26 distinct types of discrete data. As described in the 35 U.S.C. 101 rejection and the discussion above, the elements that occur at 45 – 500 measurements per second are extra-solution data gathering using sensors and a processor as a tool in a well-understood, routine, and conventional way to obtain video, kinematic, and object data that are then fed into the abstract ideas of calculating a user sensorimotor control index and determining neurological impairment of the user using the gathered data. There is nothing particularly-claimed about the measurement intervals that indicates that the hardware storage device, processor, or sensors are more than well-understood, routine, and conventional in this data gathering. This pre-solution data-gathering at 45 – 500 measurements per second is used for the subsequently-recited abstract ideas in the claim, those abstract ideas being the aspects that can be performed in the human mind. The argument is not persuasive.
The applicant argues at [Affidavit Page 3, 2nd – 3rd Full Paragraph (“1)”)] that there is inconsistency with delivery and lack of standardization in humans generating a target with their hand in a clinical setting for a sensorimotor test. Regarding “generating a target to follow with their hands”, as recited in claim 1, the target setting for an object to follow is achieved by the limitations the head-mounted display placing the user in a virtual or augmented reality environment, and presenting, on the head-mounted display by a processor executing software stored on a hardware storage device, the user with a software-generated object in the virtual or augmented reality environment, both of which are considered pre-solution activity setting up the parameters of the system. This is a common manner by which virtual-reality-based human studies are set-up. The argument is not persuasive.
The applicant argues at [Affidavit Page 3, 4th Full Paragraph (“2)”)] that it is not possible for another human to perceive or objectively measure for a statistical result, the impairment, in the same way every time. As recited, there is not a particular degree of specific “difference” that is being detected such that a benefit to reduce stacking source of errors is apparent. Claim 1 broadly recites generating a user sensorimotor index from the data, wherein the sensorimotor index is a score based on the eye, head, and hand movement of the user during the one or more sensorimotor activities. In example, a human medical profession performing a sensorimotor test moving a finger in front of a patient for concussion evaluation could assign Boolean sensorimotor index values if a reaction of the eye, head, and hand is observed: 0 for no reaction, 1 for reaction. If the eyes and head follow the finger, 1, if they do not, 0. If the user can touch the target hand, 1, if not, 0. They can then compare the results to what they might expect for the result, based on their education, background, experience, and the protocol of the test. The argument is not persuasive.
The applicant argues at [Affidavit Page 3, 5th Full Paragraph (“3)”)] that there is an inability of a human to perceive a plurality of processes occurring within another human at the same time because there is too much going on during a test such as eye movement including speed, trajectory, and smoothness while a subject is maintaining postural control. As claimed, there is nothing particular regarding the granularity of the perception. Further, the data collection is claimed in the alternative, with “including trigger activation, head position, gaze direction, head orientation, visible area of the pupil, postural sway, or hand position”. As such, so only one aspect needs to be observed, such as head position, and without particular movement resolution. This would be satisfied by a researcher observing that the subject’s head facing toward the researcher. For an example of a human observing multiple processes at once, it is within the realm of a medical professional to be able to observe that a patient’s eyes are slowly lolling side-to-side as they fall prone or lean against a wall for support during a test. As broadly recited in amended claim 1, there is no such indication that stacking particulars of the eye, head, and hand tracking are being evaluated to a degree more than to the ability of a human. Returning to the finger-follow test above, a human medical professional can observe that a person’s eyes are not following smoothly with the presence of visibly-apparent nystagmus, which is the common goal of the well-known follow-the-finger concussion test. Further, the medical professional would also be able to notice if the patient is simultaneously not moving their head and cannot raise their hands. Further, a clinical professional could review a video of a person (using a camera as a tool in its well-understood, routine, and conventional way, with the common frame rate of videos being 30 frames-per-second or 60 frames-per-second) and write down multiple aspects that they perceive in the video regarding the patient’s eye movement and posture during the test, given time and paper, and their education, background, and experience. The argument is not persuasive.
The applicant argues at [Affidavit Page 3 - 4, 5th Full Paragraph (“4)”)] that the invention improves items 1) – 3) by collecting sensor-based data at between 45 and 500 measurements per second so that multiple variables describing the patient’s performance on the sensorimotor control activities can be described quantitatively. This describes pre-solution data gathering activity of gathering the sensor-based data at a particular rate to be used for the abstract ideas of calculating the performance on the sensorimotor control activities. The data gathering is performed in a well-understood, routine, and conventional way, particularly since an example of sensor data gathering between 45 and 500 measurements per second is a routine camera video of the testing at 60 frames per second. As recited, the claim is broad enough to encompass the concept of a person with ordinary skill in the art, for example a researcher or university student, using a set of sensors as data gathering tools to obtain visual, file, and IMU orientation data in a usual way. The abstract idea of broadly calculating an index using a statistics equation can then be performed by said researcher using the extra-solution data-gathering as input to a statistics equation to obtain a user sensorimotor control index that can be then compared by the researcher to a threshold. The argument is not persuasive.
The applicant argues at [Affidavit Page 4, 1st Full Paragraph] that the virtual immersive environment and the tracking systems and sensors described and claimed in the application beneficially capture the entirety of the user’s visual field and thus their visual attention, and capture their response data, thereby permitting objective testing. As recited, the sensors and eye tracking system are performing extra-solution data gathering for the subsequently-recited abstract ideas of calculating a user sensorimotor control index and determining neurological impairment of the user using the gathered data. Merely because the claims recite “by the processor” does not indicate that there are recited improvements to processor. Looking to MPEP 2106.05(a), I, “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality” include “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016).”The argument is not persuasive.
The applicant argues at [Affidavit Page 4, 1st Full Paragraph] that a remote computer device may be used to monitor the virtual immersive environment and the sensorimotor test presented to the individual. There is nothing particularly-recited in the claims about a remote computer device. The argument is not persuasive.
The applicant argues at [Affidavit Page 4, 1st Full Paragraph] that the remote healthcare provider may make real time adjustments and provide feedback and/or assessments of the user’s test grade. There is nothing particularly-recited about a remote healthcare provider or tangible adjustments to the treatment of a patient in the claims. The argument is not persuasive.
The applicant argues at [Affidavit Page 4, 1st Full Paragraph] and [Page 9, “Improvements to the Functioning of Any Technology of Technical Field” Section] that the method does not require a baseline (pre-injury) test for a given patient. There is nothing particularly recited in the claims that indicates that there is no baseline required for a given patient. Rather, as recited, the “comparing the sensorimotor control index with an expected value” appears to broadly encompass a pre-injury test value for the patient, as well as Claim 9 “wherein the at least one other sensorimotor index is selected from…a previously sensorimotor index generated from the user’s prior data”. This does not appear to be an improvement to the technology itself as claimed. The argument is not persuasive.
The applicant argues at [Affidavit Page 4, 1st Full Paragraph – 2nd Full Paragraph] and [Page 9, “Improvements to the Functioning of Any Technology of Technical Field” Section] that the method is repeatable across patients and time and provides objective measures of impairment that can be used to drive treatment decisions, such that the claimed invention provides a technical solution that improves the technical field of detecting neurological impairment of a user. As recited, the quantification of measures of impairment are generated from the broad abstract ideas of calculating a user sensorimotor control index and determining neurological impairment of the user using the gathered data. From MPEP 2106.05(a): It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). The argument is not persuasive.
Regarding the 35 U.S.C. 103 Rejections:
Applicant’s arguments with respect to claims 1 – 7, 9 – 12, and 14 – 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/JUSTIN XU/Primary Examiner, Art Unit 3791
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