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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on October 10, 2024, July 17, 2025 and November 26, 2025 comply with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Replacement Drawings
The drawings were received on November 10, 2024. These drawings are acceptable.
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 19-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to a computer program. The scope of a computer program is broad enough to include either a computer program by itself, and/or a signal per se, both of which are non-statutory.
In order to overcome the rejection, the examiner suggest amending the preamble as follows: “A non-transitory computer readable medium recording thereon a computer program, which when executed by a computer, performs a method comprising”.
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 and 17-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
The following analysis is based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) published on January 7, 2019 (84 Fed. Reg. 50). See Also MEPE 2106.04(a)(2)(II).
With regard to claim 1:
Step 1:
Claim 1 meets step 1 requirement as it is directed towards a machine which is statutory subject matter. In this case, “a system” satisfies a “machine” category.
Step 2A, prong 1 test:
Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, claim 1 as a whole recites a method facilitating steps of organizing human activity e.g., mental process as explained in details below.
Claim 1 in general is about how the system provides for “inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video, and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program”.
The limitations of “inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video, and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, prong 2 test:
Does the claim recite additional elements that integrate the judicial exception into a practical application? No as explained below.
The claim recites the physical elements – “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” for receiving and processing various tasks. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video, and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations (a mathematical relationship, formula, or calculation). The “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” used in the steps are recited at a high level of generality, (i.e., as generic “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” for performing a generic computer function of processing data (the “inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video, and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program”), such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
Does the claim recite additional elements that amount to significantly more than the judicial exception? No as explained below.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception does not amount to significantly more because the additional elements, i.e. “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device”, amount to no more than mere instructions to apply the exception using a generic computer component, i.e. “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device”. In particular, the claims recite “inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video, and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program” steps amounts to no more than mere instructions to apply the exception using a generic computer component, i.e. a convolutional neural network. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
With respect to the “camera” for “capturing input frame images of n videos”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “camera … for capturing input frame images of n videos”, this is not a practical application as such activity is routinely practiced in the field by doctors/clinicians on a daily basis. By utilizing the camera to facilitate mere data gathering does not add anything that these practitioners do routinely in the field.
With regard to claims 17 and 18:
Step 1:
Claims 17 and 18 meet step 1 requirement as they are directed towards a process which is statutory subject matter. In this case, “method” satisfies a “process” category.
Step 2A, prong 1 test:
Do the claims recite an abstract idea, law of nature, or natural phenomenon? Yes, claims 17 and 18 as a whole recites a method facilitating steps of organizing human activity e.g., mental process as explained in details below.
Claims 17 and 18 in general are about how the system provides for “acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model, calculating a head movement speed and an eye movement speed, and generating information related to a balance function status using information related to the head movement speed and the eye movement speed” and ”acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject”.
The limitations of “acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model, calculating a head movement speed and an eye movement speed, and generating information related to a balance function status using information related to the head movement speed and the eye movement speed” and ”acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, prong 2 test:
Do the claims recite additional elements that integrate the judicial exception into a practical application? No as explained below.
The claim recites the physical elements – “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” for receiving and processing various tasks. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model, calculating a head movement speed and an eye movement speed, and generating information related to a balance function status using information related to the head movement speed and the eye movement speed” and ”acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations (a mathematical relationship, formula, or calculation). The “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” used in the steps are recited at a high level of generality, (i.e., as generic “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device” and “cameras” for performing a generic computer function of processing data (the “acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model, calculating a head movement speed and an eye movement speed, and generating information related to a balance function status using information related to the head movement speed and the eye movement speed” and ”acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject”), such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
Do the claims recite additional elements that amount to significantly more than the judicial exception? No as explained below.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception does not amount to significantly more because the additional elements, i.e. “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device”, amount to no more than mere instructions to apply the exception using a generic computer component, i.e. “processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device”. In particular, the claims recite “acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model, calculating a head movement speed and an eye movement speed, and generating information related to a balance function status using information related to the head movement speed and the eye movement speed” and ”acquiring information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model and generating head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject” steps amounts to no more than mere instructions to apply the exception using a generic computer component, i.e. a convolutional neural network. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
With respect to the “camera” for “capturing input frame images of n videos”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “camera … for capturing input frame images of n videos”, this is not a practical application as such activity is routinely practiced in the field by doctors/clinicians on a daily basis. By utilizing the camera to facilitate mere data gathering does not add anything that these practitioners do routinely in the field.
With regard to claims 19 and 20:
Step 1:
Claims 19 and 20 meet step 1 requirement as they are directed towards a manufacture which is statutory subject matter. In this case, “program” satisfies a “manufacture” category.
Step 2A, prong 1 test:
Do the claims recite an abstract idea, law of nature, or natural phenomenon? Yes, claims 19 and 20 as a whole recites a method facilitating steps of organizing human activity e.g., mental process as explained in details below.
Claims 17 and 18 in general are about how the “computer program written to perform the method of generating information on a balance function status according to claims 17 and 18” on a computer and recorded on a computer-readable recording medium.
The limitations of “the computer program written to perform the method of generating information on a balance function status” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, prong 2 test:
Do the claims recite additional elements that integrate the judicial exception into a practical application? No as explained below.
The claims recite the physical elements – “computer” for receiving and processing various tasks. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “method of generating information on a balance function status” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations (a mathematical relationship, formula, or calculation). The “computer” used in the steps is recited at a high level of generality, (i.e., as generic “computer” for performing a generic computer function of processing data (the “method of generating information on a balance function status”), such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
Do the claims recite additional elements that amount to significantly more than the judicial exception? No as explained below.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception does not amount to significantly more because the additional elements, i.e. “computer”, amounts to no more than mere instructions to apply the exception using a generic computer component, i.e. “computer”. In particular, the claims recite “method of generating information on a balance function status” steps amount to no more than mere instructions to apply the exception using a generic computer component, i.e. a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. §102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-7 and 17-20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Krueger et al. (U.S. Patent Application Publication No. US 2023/0210442 A1) (hereafter referred to as “Krueger (‘442)”).
With regard to claim 1, Krueger (‘442) describes at least one processor and a memory that stores instructions executable by the processor and stores at least one artificial neural network model executed on a computing device (see Figures 2 and 10, elements 412, 414 and 418, and refer for example to paragraph [0214], [0255] and [0284] which discuss the processors and the memory storing instructions, and refer for example to paragraphs [0100], [0135] and [0562] which discuss the neural network models), wherein the at least one processor inputs frame images of n videos of a subject captured by n (natural number) cameras to at least one artificial neural network model to acquire at least one of information related to head coordinates, coordinates of a pupil center, and eye phase changes of the subject according to an order of frame images of an m-th (natural number from 1 to n) video (see Figure 2 and refer for example to paragraph [0114], which discusses the cameras capturing video, refer for example to paragraph [0126], which discusses acquiring information related to head coordinates, refer for example to paragraphs [0179] and [0180], which discuss acquiring information related to coordinates of a pupil center, and refer for example to paragraph [0142], [0179] and [0180], which discuss acquiring eye phase changes of the subject), and uses the information to generate information related to head movement and eye movement for generating information on a balance function status or performing a balance function rehabilitation program (see Figure 1, elements 698 and 820, and refer for example to paragraphs [0188] through [0193]).
As to claim 5, Krueger (‘442) describes wherein the second artificial neural network model is an artificial neural network model trained to generate the information related to the coordinates of a pupil center by allowing the at least one processor to use, as training data, eye area images extracted to include an eye from frame images of at least one video in which a human face is captured or multi-frame images in which frame images of multiple videos in which a human face is captured are concatenated (refer for example to paragraphs [0077], [0099] and [0115]).
In regard to claim 6, Krueger (‘442) describes wherein the at least one processor generates pupil area images in which an area occupied by the pupil and the remaining area have different pixel values in the eye area images, and trains the second artificial neural network model using the pupil area images or an array of pixel values of the pupil area images as the training data (refer to paragraphs [0077], [0099] and [0115]).
With regard to claim 7, Krueger (‘442) describes wherein the at least one processor trains the second artificial neural network model to generate eye feature points and coordinate information of the feature points from the eye area images, and to generate horizontal coordinate values and vertical coordinate values of the pupil center using coordinates of a plurality of preset feature points (refer for example to paragraphs [0113], [0124] [0158], [0179] and [0265]).
As to claim 17, Krueger (‘442) describes acquiring, by the at least one processor (see Figures 2 and 10, elements 412, 414 and 418, and refer for example to paragraph [0214], [0255] and [0284] which discuss the processors and the memory storing instructions), information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frame images of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to at least one artificial neural network model, and generating, by the at least one processor, head movement information and eye movement information using the information acquired from the at least one artificial neural network model (see Figure 2 and refer for example to paragraph [0114], which discusses the cameras capturing video, refer for example to paragraphs [0179] and [0180], which discuss acquiring information related to head movement information and eye movement), calculating a head movement speed and an eye movement speed (see Figure 2 and refer for example to paragraphs [0179] and [0180], which discuss head movement speed and an eye movement speed), and generating information related to a balance function status using information related to the head movement speed and the eye movement speed (see Figure 1, elements 698 and 820, and refer to paragraphs [0188] through [0193]).
In regard to claim 18, Krueger (‘442) describes acquiring, by the at least one processor (see Figures 2 and 10, elements 412, 414 and 418, and refer for example to paragraph [0214], [0255] and [0284] which discuss the processors and the memory storing instructions), information related to head coordinates, coordinates of a pupil center, and eye phase changes of a subject according to an order of frames of an m-th (natural number from 1 to n) video by allowing the at least one processor to input frame images of n videos of the subject captured by n (natural number) cameras to the at least one artificial neural network model (see Figure 2 and refer for example to paragraph [0114], which discusses the cameras capturing video, refer to paragraph [0126], which discusses acquiring information related to head coordinates, refer to paragraphs [0179] and [0180], which discuss acquiring information related to coordinates of a pupil center, and refer to paragraph [0142], [0179] and [0180], which discuss acquiring eye phase changes of the subject); and generating, by the at least one processor, head movement information and eye movement information using the information acquired from the at least one artificial neural network model and performing a balance function rehabilitation program by head and eye movements of the subject (see Figure 1, elements 698 and 820, and refer for example to paragraphs [0188] through [0193]).
With regard to claim 19, Krueger (‘442) describes a computer program written to perform the method of generating information on a balance function status according to claim 17 on a computer and recorded on a computer-readable recording medium (see Figures 2 and 10, elements 412, 414 and 418, and refer to paragraph [0214], [0255] and [0284] which discuss the processors and the memory storing instructions).
As to claim 20, Krueger describes a computer program written to perform the balance function rehabilitation method according to claim 18 on a computer and recorded on a computer-readable recording medium (see Figures 2 and 10, elements 412, 414 and 418, and refer for example to paragraph [0214], [0255] and [0284] which discuss the processors and the memory storing instructions).
Allowable Subject Matter
Claims 2-4 and 8-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kiderman, Krueger (‘672), (‘141) and (‘212), and Sasaki all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Center information webpage on the USPTO website. For more information about the Patent Center, see https://www.uspto.gov/patents/apply/patent-center. Should you have questions about access to the Patent Center, contact the Patent Electronic Business Center (EBC) at 571-272-4100 or via email at: ebc@uspto.gov .
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
/JOSE L COUSO/Primary Examiner, Art Unit 2667
May 14, 2026
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