CTNF 18/719,106 CTNF 97110 DETAILED ACTION Claims 1-2, 5-9, 11-17, 20-22, 27, 33-34, 36, and 39 are hereby the present claims under consideration. Examiner’s Note: All references to Applicant’s Specification are made using the paragraph numbers assigned in the US Publication of the present application US 20250281102 A1. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: An input configured to receive sensor data indicative of movement of a subject of claim 1 Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. An input configured to receive sensor data indicative of movement of a subject of claim 1 is not described as a particular structure by the specification. In particular, paragraph 0028 recites a variety of devices such as keyboards and mouses that may be inputs but recites that these devices receive user input rather than sensor data. Paragraph 0028 further recites that the inputs may include data sources such as the sensors themselves but these structures are seemingly for the generation or obtaining of data rather than the receipt of data. Thus, no particular structure is described for an input which receives data. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112(b) 07-30-02 AIA 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. 07-34-01 Claims 1-2, 5-9, 11-17, 20-22, 27, 33-34, 36, and 39 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. 07-34-23 Claim 1 the limitation “an input” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function as described in the claim interpretation section above. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. Claim 1 recites “an input configured to receive sensor data indicative of movement of a subject … a processor … wherein the processor is configured to receive the sensor data indicative of movement of the subject” it is unclear if the input is the same as, related to, a sub-component of, or different from the processor. Both elements are recited as performing the same function with no structural limitations to distinguish them. For the purposes of this examination, the input will be interpreted as part of the processor. Claim 1 recites “a subject” in line 3 but later recites “the user” in line 12. It is unclear if the “user” of the system is the same individual as the “subject” of the system. This is further exacerbated by claims 2, 5, 13, and 16-17 which each refer to “the user” rather than the subject but appear to be in reference the subject of the system whom is being acted upon and measurements taken therefrom. For the purposes of this examination, the limitations will be interpreted as the subject and user being the same individual. This rejection is similarly applied to claim 21. Claims 2, 5-9, 11-17, and 20 are rejected by virtue of their dependance on claim 1. Claims 22, 27, 33-34, 36, and 39 are rejected by virtue of their dependance on claim 21. Claim 7 recites “wherein the processor is further configured to reduce dimensions of the sensor data by generating the movement dataset before extracting the movement features” but it is unclear how the generation of the movement datasets before extracting features serves to reduce the dimensionality of the data. For the purposes of this examination, the limitation will be interpreted as requiring the dimensionality of the dataset to be reduced prior to generating the features. This rejection is similarly applied to claim 27. Claim 7 recites the limitation "the movement dataset" in line 2. There is insufficient antecedent basis for this limitation in the claim. This rejection is similarly applied to claim 27. Claim Rejections - 35 USC § 112(a) 07-30-01 AIA The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. 07-31-01 Claims 1, 20-21, and 39 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 the limitation “an input” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function as described in the claim interpretation section above. Therefore, the claim lacks sufficient written description and is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. Claim 1 recites “analyze the movement feature from the first subset of the plurality of submovement datasets to determine a potential clinical disorder of the user” but specification fails to support the claim which defines the invention in functional language specifying a desired result when the specification does not sufficiently identify how the invention achieves the claimed function. For there to be sufficient disclosure for a computer-implemented claim limitation, it is not enough that one skilled in the art could write a program to achieve the claimed function. Rather, the specification must disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill can reasonably conclude that the inventor invented the claimed subject matter. See Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, Fed. Reg. Vol. 76, No. 27, February 9, 2011, p. 7162-7175 (“the Supplementary Examination Guidelines”). In regards to claim 1, the specification does not provide the particular algorithm or steps taken to “determine a potential clinical disorder of the user” based on analyzing the movement features. In particular, the specification paragraph 0030 recites that the clinical disorder may be a wide variety of different disorders including neurodegenerative disorders, movement disorders, or any suitable neurological disease or non-neurological disorder that restricts or changes the quality of movement. Thus the specification indicates that the determined disorder may be a wide variety of different disorders. However the specification does not describe how the movement features are analyzed and then correlated to a particular disorder. In particular, the specification paragraphs 0060-0078 describe the various measurements taken, the features derived therefrom, and how these features are distinguishable between a disease group and a control group. However the specification does not describe how any single or combination of features may be used to determine any of the recited clinical disorders of paragraph 0030. Rather the analysis appears to be directed towards the detection and severity determination of ataxia. While ataxia may be a symptom of a variety of different disorders, the specification does not detail how the ataxia measurements are used to distinguish one disorder from another such as determining a patient has Alzheimer’s disease rather than any other disease or disorder that similarly affects movement. The detection of one symptom of a plurality of disorders (ataxia) is not considered sufficient support for the determination of any clinical disorder which may have this symptom. Thus the specification appears to support the detection and severity determination of ataxia rather than the detection of “a potential clinical disorder” as recited in the claims. This scope of support appears to be further supported by paragraphs 0051-0052 which are drawn towards severity detection of a condition but again do not indicate how the system distinguishes one neurological and/or physical disorder from another. This rejection is similarly applied to claims 20-21 and 39. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-2, 5-9, 11-17, 20-22, 27, 33-34, 36, and 39 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. Claims 1-2, 5-9, 11-17, 20-22, 27, 33-34, 36, and 39 are directed to a method of processing movement signals using a computational algorithm, which is an abstract idea. Claims 1-2, 5-9, 11-17, 20-22, 27, 33-34, 36, and 39 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019) and the 2024 Update on Subject Matter Eligibility (Federal Register, Vol 89, No. 137, page 58128, July 17, 2024). The analysis of claim 1 is as follows: Step 1: Claim 1 is drawn to a machine. Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations: [A1] receive the sensor data indicative of movement of the subject [B1] generate a plurality of submovement datasets using the sensor data [C1] extract a movement feature from a first subset of the plurality of submovement datasets [D1] analyze the movement feature from the first subset of the plurality of submovement datasets to determine a potential clinical disorder of the user [E1] generate a report that indicates the potential clinical disorder of the user These elements [A1]-[E1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper. Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception: [A2] an input configured to receive sensor data indicative of movement of a subject [B2] a memory [C2] a processor coupled to the memory These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[C2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “receive sensor data indicative of movement of a subject” does not qualify as significantly more because this limitation merely describes that data is received and does not incorporate the sensor as part of the claimed invention. Further, the elements [A2]-[C2] do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l , 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l , 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic , 890 F.3d 1016 (Fed. Circ. 2018)). In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Claims 2, 5-9, 11-17, and 20 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions: Claim 5: one or more wearable sensor devices on at least one or a wrist or an ankle of the user; Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claim 5 are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). The limitations of claim 5 so not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the wearable sensors are nothing more than an accelerometer incorporated into any wearable device. Such sensors are routine and conventional as evidenced by Applicant’s lack of a particular description of the wearable devices in the specification paragraphs 0023 and 0031. In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Claim 21 recites the same abstract idea as claim 1 and is rejected on the same basis set forth in regards to claim 1 above. Claims 22, 27, 33-34, 36, and 39 depend from claim 21 and recite only limitations that further limit the abstract idea and thus do not amount to significantly more than the abstract idea and do not integrate the abstract idea into a practical application. 07-04-03 AIA 07-04-01 Section 33(a) of the America Invents Act reads as follows: Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism. Claim 5 rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability , 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101). Claim 5 recites “one or more wearable devices on at least one of a wrist of an ankle of the user” which appears the indicate that the scope of the system encompasses the wearable device on the user and thus includes at least a portion of the user within the scope of the claim. Examiner’s Note: It would seem that an amendment to recite “one or more wearable devices configured to be worn on at least one of a wrist of an ankle of the user” would be sufficient to overcome this rejection . Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1, 21, and 39 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart . Regarding claim 1 , Eberhart discloses a medical assessment system for clinical disorder assessment (Abstract), comprising: an input configured to receive sensor data indicative of movement of a subject (Paragraphs 0037-0038: the movement monitoring device and the preprocessor which received the movement data); a memory (Paragraph 0151: the memory); and a processor coupled to the memory (Paragraph 0151: the processor coupled to the memory); wherein the processor is configured to: receive the sensor data indicative of movement of the subject (Paragraphs 0037-0038: the movement monitoring device and the preprocessor which received the movement data); generate a plurality of submovement datasets using the sensor data (Paragraphs 0054-0056: the preprocessor divides the received signals into epochs, or submovement datasets); extract a movement feature from a first subset of the plurality of submovement datasets (Paragraphs 0054-0056: the preprocessor extracts a plurality of features from the datasets); analyze the movement feature from the first subset of the plurality of submovement datasets to determine a potential clinical disorder of the user (Paragraphs 0057-0060: the preprocessor feeds the features to a computational intelligence system which determines a neurological disorder classification of the user); and generate a report that indicates the potential clinical disorder of the user (Paragraph 0060: the neurological disorder classification is output to a monitor and a report showing which movements are indicative of the disorder). Regarding claim 21 , Eberhart discloses a method for clinical disorder assessment, comprising: receiving sensor data indicative of movement of the subject (Paragraphs 0037-0038: the movement monitoring device and the preprocessor which received the movement data); generating a plurality of submovement datasets using the sensor data (Paragraphs 0054-0056: the preprocessor divides the received signals into epochs, or submovement datasets)); extracting a movement feature from a first subset of the plurality of submovement datasets (Paragraphs 0054-0056: the preprocessor extracts a plurality of features from the datasets); analyzing the movement feature from the first subset of the plurality of submovement datasets to determine a potential clinical disorder of the user (Paragraphs 0057-0060: the preprocessor feeds the features to a computational intelligence system which determines a neurological disorder classification of the user); and generating a report that indicates the potential clinical disorder of the user (Paragraph 0060: the neurological disorder classification is output to a monitor and a report showing which movements are indicative of the disorder). Regarding claim 39 , Eberhart discloses the method of claim 21. Eberhart further discloses the method wherein the potential clinical disorder includes a neurological disorder or a neurodegenerative disease (Paragraphs 0059-0061: the disorder is a neurological disorder) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-22-aia AIA Claim s 2, 5-6, 14-15, 20, 22, and 34 rejected under 35 U.S.C. 103 as being unpatentable over Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart as applied to claim s 1 and 21 above and further in view of Wagner US Patent Application Publication Number US 20160262685 A1 hereinafter Wagner Regarding claims 2 and 22, Eberhart discloses the medical assessment system and method of claims 1 and 21 respectively. Eberhart further discloses the use of motion data obtained through an actigraph sensor (Paragraphs 0042-0045). But fails to explicitly recite the system or method wherein the sensor data includes at least one of: video or a series of pictures of the user, position data, velocity data or acceleration data Wagner teaches a system including an image capture device, at least one accelerometer, and a central processing unit (CPU) with storage coupled thereto for storing instructions that when executed by the CPU cause the CPU to receive a first set of motion data from the image capture device related to at least one joint of a subject while the subject is performing a task and receive a second set of motion data from the accelerometer related to the at least one joint of the subject while the subject is performing the task. The CPU also calculates kinematic and/or kinetic information about the at least one joint of a subject from a combination of the first and second sets of motion data, and outputs the kinematic and/or kinetic information for purposes of assessing a movement disorder (Abstract). Thus, Wagner falls within the same field of endeavor as Applicant’s invention. Wagner teaches that patient motion data may be captured using an image capture device and/or accelerometer devices (Paragraph 0099). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system and method of Eberhart to utilize a motion capture camera and/or an accelerometer integrated into the actigraph of Eberhart because such a modification would be a simple substitution of one known element (the actigraph sensor of Eberhart) for another known element (the motion capture camera and/or accelerometer of Wagner) with no surprising technical effect (the patient’s motion data is gathered). Regarding claim 5, Eberhart in view of Wagner teaches the medical assessment system of claim 2. Modified Eberhart further teaches the system, wherein the acceleration data, the position data, or the velocity data is received from one or more wearable sensor devices on at least one of a wrist or an ankle of the user (Paragraph 0042: the actigraph is mounted on the user’s wrist). Regarding claim 6, Eberhart in view of Wagner teaches the medical assessment system of claim 2. Modified Eberhart fails to further disclose the system, wherein the acceleration data is derived from the video data. Wagner teaches that the camera data may also be processed to provide acceleration data to match with or verify the accelerometer data. The accelerometer data may similarly be used to verify or map to the data collected through image analysis (Paragraphs 0023, 0103-0104, 0111, and 0136). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of modified Eberhart to calculate acceleration data from the image data and from the accelerometer as taught by Wagner because calculating data from both sources may result in more accurate data gathering since the deficiencies of each method can be compensated for with the other. Regarding claims 14-15 and 34, Eberhart discloses the medical assessment system and method of claims 1 and 21 respectively. Eberhart further discloses the system or method wherein the movement feature may be a maximum or minimum, power spectral density, number of zero crossings, or other metrics (Paragraphs 0054-0055), but fails to explicitly disclose the movement feature is a representing value of at least one of: distances, peak velocities, peak accelerations, or durations of the first subset; and wherein the representing value is a mean value or a standard deviation value. Wagner teaches that patient motion data may be captured using an image capture device and/or accelerometer devices (Paragraph 0099). Wagner teaches that the accelerometer and motion capture data may be used to generate a variety of features including maximum speed, duration, and distance travelled. Wagner further teaches that the means and/or standard deviations of these metrics may be utilized (Paragraphs 0101-0102). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system and method of Eberhart to utilize a motion capture camera and/or an accelerometer integrated into the actigraph of Eberhart and the determine features such as those used by Wagner because such a modification would be a simple substitution of one known element (the actigraph sensor of Eberhart) for another known element (the motion capture camera and/or accelerometer of Wagner) with no surprising technical effect (the patient’s motion data is gathered), and the use of such features provides additional metrics which modified Eberhart may use to discriminate between neurological disorders. Regarding claim 20, Eberhart discloses the medical assessment system of claim 1. Eberhart fails to further disclose the system wherein the potential clinical disorder is ataxia-telangiectasia, spinocerebellar ataxia, multiple system atrophy, or amyotrophic lateral sclerosis. Wagner teaches a system for assessing movement disorders (Abstract). Wagner teaches that the system may be used to assess a variety of movement disorders including ataxia, multiple systems atrophy, and amyotrophic lateral sclerosis (Paragraph 0093). Wagner teaches that the assessment may be for the initial diagnosis of a movement disorder based on comparing the subject’s data to reference data. The difference between the subject’s data and the reference data also provides an indication of the severity of the condition (Paragraph 0095). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of Eberhart to determine a variety of different movement disorders as taught by Wagner because Eberhart already measures the required movement data and configuring the system to detect a wider range of diseases makes the system more widely applicable and useful to a greater patient population . 07-22-aia AIA Claim s 7-9 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart as applied to claim s 1 and 21 above and further in view of Chen “ Dimensionality reduction of data sequences for human activity recognition ” published by ScienceDirect on October 19 th 2016, pages 294-302 hereinafter Chen . Regarding claims 7 and 27, Eberhart discloses the medical assessment system and method of claims 1 and 21 respectively. Eberhart fails to further disclose the system or method wherein the processor is further configured to reduce dimensions of the sensor data by generating the movement dataset before extracting the movement features. Chen teaches a system for dimensionality reduction of human movement signals (Abstract). Thus, Chen is reasonably pertinent to the problem at hand. Chen teaches that principle component analysis is sued in many disciplines and serves to find the best angle to observe the most variation of the data points. Chen teaches that the first principle component is the eigenvector with the greatest eigenvalue and thus showing the greatest variation. Chen teaches that dimensionality may be reduced to a desired degree by principle component analysis, the reduction is not limited to a single dimension (Page 296: 3.1 principle component analysis). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the dimensionality reduction as taught by Chen into the system and method of Eberhart prior to feature extractions because such dimensionality reduction reduces the processing requirements of the system and thus may allow the system to be smaller, consume less power, and or produce results faster than would otherwise be required. Regarding claim 8, Eberhart in view of Chen teaches the medical assessment system of claim 7. Modified Eberhart fails to further disclose the system wherein; to reduce the dimensions of the sensor data, the processor is configured to project the sensor data on a two-dimensional plane or a manifold plane. Chen teaches that principle component analysis is sued in many disciplines and serves to find the best angle to observe the most variation of the data points. Chen teaches that the first principle component is the eigenvector with the greatest eigenvalue and thus showing the greatest variation. Chen teaches that dimensionality may be reduced to a desired degree by principle component analysis, the reduction is not limited to a single dimension (Page 296: 3.1 principle component analysis). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the dimensionality reduction to a two dimensional plane as taught by Chen into the system of Eberhart prior to feature extractions because such dimensionality reduction reduces the processing requirements of the system and thus may allow the system to be smaller, consume less power, and or produce results faster than would otherwise be required. Regarding claim 9, Eberhart in view of Chen teaches the medical assessment system of claim 7. Modified Eberhart fails to further disclose the system wherein the movement dataset comprises at least one of: a first principal component dataset in a primary direction, the primary direction having maximum movement variation of the sensor data; or a second principal component dataset in a secondary direction, the secondary direction being orthogonal to the primary direction. Chen teaches that principle component analysis is sued in many disciplines and serves to find the best angle to observe the most variation of the data points. Chen teaches that the first principle component is the eigenvector with the greatest eigenvalue and thus showing the greatest variation. Chen teaches that dimensionality may be reduced to a desired degree by principle component analysis, the reduction is not limited to a single dimension (Page 296: 3.1 principle component analysis). It is noted that the second principle component being orthogonal to the first is inherently taught by the method of principle component analysis. It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the dimensionality reduction into a 1-D vector or a 2-D plane as taught by Chen into the system of modified Eberhart because the dimensionality reduction reduces the processing requirements of the system and thus may allow the system to be smaller, consume less power, and or produce results faster than would otherwise be required . 07-22-aia AIA Claim s 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart further in view of Chen “ Dimensionality reduction of data sequences for human activity recognition ” published by ScienceDirect on October 19 th 2016, pages 294-302 hereinafter Chen as applied to claim 7 above and further in view of Oubre et. al. “ Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task ” published by IEEE on January 15 th 2020. Pages 601-611 hereinafter Oubre . Regarding claim 11, Eberhart in view of Chen teaches the medical assessment system of claim 7. Modified Eberhart fails to further disclose the system wherein the processor is configured to generate the plurality of submovement datasets by: identifying zero crossing in in the movement dataset; and dividing the movement dataset at each zero crossing to form the plurality of submovement datasets from the movement dataset. Oubre teaches a system and method for analyzing upper limb movement in stroke survivors (Abstract). Thus, Oubre is reasonably pertinent to the problem at hand. Oubre teaches the decomposition of movement data into 1D movement elements and teaches that these elements are segmented using the zero-crossings of velocity data to generate movement elements with zero initial and terminal velocity (page 603: section C. Inertial time-series decomposition; page 602: Introduction paragraph 3) It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the segmentation of the acceleration data using the zero-crossings as taught by Oubre into the system of modified Eberhart because Oubre teaches that this segmentation method produces a bell-shaped morphology for neurologically intact individuals and thus may contain morphological characteristics of movement elements and may contain relevant information regarding motor impairment severity. Thus this segmentation method may serve as a discriminative variable for motor impairment and improve detection and severity determination of motor impairments. Regarding claim 12, Eberhart in view of Chen teaches the medical assessment system of claim 7. Modified Eberhart fails to further disclose the system, wherein a first submovement dataset of the plurality of submovement datasets is a dataset between two abutting zero velocity crossings in the movement dataset. Oubre teaches the decomposition of movement data into 1D movement elements and teaches that these elements are segmented using the zero-crossings of velocity data to generate movement elements with zero initial and terminal velocity (page 603: section C. Inertial time-series decomposition; page 602: Introduction paragraph 3). Thus, each movement element is a dataset between two velocity zero-crossings. It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the segmentation of the acceleration data using the zero-crossings as taught by Oubre into the system of modified Eberhart because Oubre teaches that this segmentation method produces a bell-shaped morphology for neurologically intact individuals and thus may contain morphological characteristics of movement elements and may contain relevant information regarding motor impairment severity. Thus this segmentation method may serve as a discriminative variable for motor impairment and improve detection and severity determination of motor impairments . 07-22-aia AIA Claim s 13 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart further as applied to claim s 1 and 21 above and further in view of Oubre et. al. “ Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task ” published by IEEE on January 15 th 2020. Pages 601-611 hereinafter Oubre . Regarding claims 13 and 33, Eberhart discloses the medical assessment system and method of claims 1 and 21 respectively. Eberhart fails to further disclose the system or method wherein the processor is further configured to: group the plurality of submovement datasets into a plurality of subsets based on a duration and a direction of the movement of the user in the plurality of submovement datasets, wherein the first subset is among the plurality of subsets. Oubre teaches that the system may perform a density-based clustering, or grouping, algorithm using Euclidian distance as the distance metric. The clustering algorithm identifies clusters of similar data. The cutoffs used to generate the clusters may be optimized as a trade-off between inclusiveness of data and homogeneity (page 604: E. Unsupervised approach to identify homogeneous movement elements). Oubre does not explicitly recite that the clusters are formed based on a duration and a direction of the movement of the user in the submovement datasets. An obvious variation of Oubre would be to cluster the datasets based on a duration and direction of movement of the user. Such a variation is considered obvious because clustering is used to group datasets based on their degree of similarity in the desired metric. The particular metric(s) used for grouping the datasets is customizable and there are a finite number of identified, predictable solutions, with a reasonable expectation of success when selecting the grouping metric(s). Such metrics include, duration, amplitude, frequency, distance, velocity, type of movement the user is performing, similarity to a given template, and other such metrics associated with the gathered movement data. Thus the particular metrics used to group the datasets are considered to be obvious variants since there are a finite number of predictable metrics to use with a reasonable expectation of success and no surprising technical effect has been set forth based on Applicant’s particular grouping method. It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement obvious variation of the clustering algorithm of Oubre as described above into the system and method of Eberhart because the clustering operation serves the gather similar datasets based on the selected metric(s) and may serve as more discriminative datasets based on the desired condition being detected or may serve to add context to other datasets such as is described in Oubre (page 604: E. An Unsupervised Approach to Identify Homogeneous Movement Elements – page 605: I Comparative Analysis; Fig. 2) 07-22-aia AIA Claim s 16-17 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhart US Patent Application Publication Number US 20030191406 A1 hereinafter Eberhart further as applied to claim s 1 and 21 above and further in view of Klapper US Patent Application Publication Number US 20050234309 A1 hereinafter Klapper . Regarding claims 16 and 36, Eberhart discloses the medical assessment system and method of claims 1 and 21 respectively. Eberhart further discloses the system and method, wherein to analyze the movement features from the first subset of the plurality of submovement datasets (Paragraphs 0057-0060: the preprocessor feeds the features to a computational intelligence system which determines a neurological disorder classification of the user), the processor is configured to: obtain a model trained using a reference (Paragraphs 0057-0060, 0080-0081, and 0085-0088: the neural network used to analyze the datasets is trained using reference data); provide the movement feature to the regression model (Paragraphs 0057-0060: the preprocessor feeds the features to a computational intelligence system); and generate an output of the regression model to determine the potential clinical disorder of the user (Paragraphs 0057-0060: the preprocessor feeds the features to a computational intelligence system which determines a neurological disorder classification of the user). Eberhart fails to further disclose the model being a regression model. Klapper teaches a system comprising wearable accelerometers coupled with computer implemented learning and statistical analysis techniques in order to classify the movement states of Parkinson's patients and to provide a timeline of how the patients fluctuate throughout the day (Abstract). Thus, Klapper falls within the same field of endeavor as Applicant’s invention. Klapper teaches the use of a linear regression model for classifying movement states from motion data (Paragraphs 0054-0055, 0105, 0119, and 0151-0157). It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the model of Eberhart to be a regression model such as the one taught by Klapper because Klapper teaches that a regression model is also suitable for classifying motion data and thus it would be a simple substitution of one known element (the model of Eberhart) for another known element (the regression model of Klapper) with no surprising technical effect (the model still serves to classify the motion data). Regarding claim 17, Eberhart in view of Klapper teaches the medical assessment system of claim 16. Modified Eberhart further discloses the system wherein the indication of the potential clinical disorder of the user is indicative of an estimated severity level of the potential clinical disorder determined based on the output of the regression model (Paragraphs 0060-0061: the model outputs the appropriate neurological disorder classification and may further include a level of severity). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW ERIC OGLES whose telephone number is (571)272-7313. The examiner can normally be reached M-F 8:00AM - 5:30PM. 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. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached on Monday-Friday from 9:00AM – 4:00PM at (571) 272 – 7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MATTHEW ERIC OGLES/Examiner, Art Unit 3791 /JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791 Application/Control Number: 18/719,106 Page 2 Art Unit: 3791 Application/Control Number: 18/719,106 Page 3 Art Unit: 3791 Application/Control Number: 18/719,106 Page 4 Art Unit: 3791 Application/Control Number: 18/719,106 Page 5 Art Unit: 3791 Application/Control Number: 18/719,106 Page 6 Art Unit: 3791 Application/Control Number: 18/719,106 Page 7 Art Unit: 3791 Application/Control Number: 18/719,106 Page 8 Art Unit: 3791 Application/Control Number: 18/719,106 Page 9 Art Unit: 3791 Application/Control Number: 18/719,106 Page 10 Art Unit: 3791 Application/Control Number: 18/719,106 Page 11 Art Unit: 3791 Application/Control Number: 18/719,106 Page 12 Art Unit: 3791 Application/Control Number: 18/719,106 Page 13 Art Unit: 3791 Application/Control Number: 18/719,106 Page 14 Art Unit: 3791 Application/Control Number: 18/719,106 Page 15 Art Unit: 3791 Application/Control Number: 18/719,106 Page 16 Art Unit: 3791 Application/Control Number: 18/719,106 Page 17 Art Unit: 3791 Application/Control Number: 18/719,106 Page 18 Art Unit: 3791 Application/Control Number: 18/719,106 Page 19 Art Unit: 3791 Application/Control Number: 18/719,106 Page 20 Art Unit: 3791 Application/Control Number: 18/719,106 Page 21 Art Unit: 3791 Application/Control Number: 18/719,106 Page 22 Art Unit: 3791 Application/Control Number: 18/719,106 Page 23 Art Unit: 3791 Application/Control Number: 18/719,106 Page 24 Art Unit: 3791 Application/Control Number: 18/719,106 Page 25 Art Unit: 3791 Application/Control Number: 18/719,106 Page 26 Art Unit: 3791
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