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 .
Claim Interpretation
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.
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:
A) “a data collector configured to collect sensing data obtained by sensing an instantaneous current value” and “a data generator configured to analyze the sensing data to generate a user's gait data;” in claim 1;
B) “the data generator is configured to: extract a first step and a second step of the user by continuous outer envelopes extracted from the sensing data, the first step and the second step being repeated sequentially; and distinguish a step of the user's left foot from a step of the user's right foot by defining a series of the first steps as index walking” in claim 2;
C) “the data generator is configured to analyze the collected sensing data to generate the gait data,” in claim 3;
D) “the data generator is configured to: extract the user's step by using a continuous outer envelope extracted from the sensing data; and generate the gait data by distinguishing” in claim 4;
E) “the data generator is configured to distinguish” in claim 5; and
F) “the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data;” in claim 6, 7 and 8;
The terms, “data collector and “data generator” are generic placeholder that do not by themselves, recite sufficiently structure. The recited claim limitation primarily defines these elements by the functions they perform rather than by structural characteristics. Specifically, A) The data collector is modified by functional language, “obtained by sensing an instantaneous current value that is supplied to a treadmill to operate a motor of the treadmill;”, B) The data generator is modified by functional languages, “extract a first step and a second step of the user by continuous outer envelopes” and “distinguish a step of the user's left foot from a step of the user's right foot”, C) The data generator is modified by functional language, “analyze the collected sensing data to generate the gait data,”, D) The data generator is modified by functional languages, “extract the user's step by using a continuous outer envelope” and “generate the gait data by distinguishing”, E) The data generator is modified by functional language, “distinguish”, and F) the data generator is modified by functional language, “extract individual steps from continuous outer envelopes”. The claim language does not recite sufficiently definite structural limitations for performing the claimed functions.
Accordingly, these limitations meet the 3-prong analysis, and therefore invokes 112(f).
The specification is then reviewed to determine whether corresponding structure linked to the claimed functions is disclosed.
Figure 2 discloses a system architecture data collector 110, data generator 120, and controller 130 as components of gait analysis device 100. The specification further states that the gait analysis device 100 may be “implemented in the form of hardware modules, in the form of software modules, or in a combination of hardware and software modules.” (Paragraph, 0086) and furthermore, the gait analysis device 100 may be “implemented and operated even with a relatively low- specification microprocessor unit (MPU).” (Paragraph, 0138)
Regarding the “data collector, the specification discloses “information (sensing data) on the instantaneous current value supplied to the treadmill is transmitted to the data collector 110 of the gait analysis device 100.” (Paragraph, 0090).
Regarding the “data generator”, the specification discloses corresponding algorithmic structure for the claimed computer-implemented functions, including
threshold setting for gait detection (Paragraph, 0032-0034),
detecting current in case of exceeding threshold (paragraph, 0047),
extracting a user’s gait data from continuous outer envelope of a user’s step (Paragraph, 0048),
partitioning individual gait posture phases (Paragraph, 0048), and
generating gait data (Paragraph, 0022-0029) (Paragraph, 0032-0048).
Therefore, the functions of the data collector and the data generator provide sufficient structures. Including algorithmic disclose liked to the claimed computer-implemented functions.
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.
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 § 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.
Claim 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea as followings (abstract ideas, mental process)
Step 1:
The claimed invention of claim 1-15 is directed to statutory subject matter as the claims recites a system and a method for a gait analysis device.
Step 2A, Prong One:
Regarding claim 1-15, the recited steps are directed to mental process, i.e., concepts that can be performed in a human mind (see MPEP 2106.04(a)(2) subsection (III)). The court have determined that concepts performed in a human mind falls within the judicial exceptions, often referred to as “abstract ideas”.
Claim 1: “analyze the sensing data”, “set a threshold”, and “generate the gait data by analyzing the sensing data”,
Claim 2: “extract a first step and a second step of the user” and “distinguish a step”,
Claim 3: “analyze the collected sensing data to generate the gait data”,
Claim 4: “extract the user's step by using a continuous outer envelope” and “distinguishing, in the envelope”,
Claim 5: “distinguish, in the envelope”,
Claim 6-8: “extract individual steps”, “overlap data of the extracted individual steps”, and “generate the gait data by matching each gait posture phase”,
Claim 9: “use at least one of the heatmap and the median graph” and “analyze a gait control state based on a distribution degree;” and “analyze a predicted disease”,
Claim 11-12: “calculate the degree of pain and discomfort” and “reflecting the collected sensing data in the analysis”,
Claim 13-15: “provide gait-related content”, “display device of a user walking on the treadmill;(opinion)”, and “control the providing of the gait-related content”
Specially,
Claim 1, the limitations of “analyze the sensing data to generate a user's gait data;” (opinion and evaluation), “set a threshold that is a maximum value of the instantaneous current value” (opinion), and “generate the gait data by analyzing the sensing data from a time point” (judge, extra-solution data output) are processes that, under the broadest reasonable interpretation, can be performed in the human mind.
Claim 2, under the broadest reasonable interpretation, the limitations of “extract a first step and a second step of the user by continuous outer envelopes extracted from the sensing data, the first step and the second step being repeated sequentially;” and “distinguish a step of the user's left foot from a step of the user's right foot defining a series of the first steps as index walking” also encompass mental process such as observation, evaluation, and judgement.
Claim 3, the limitation of “analyze the collected sensing data to generate the gait data, which is divided into gait posture phases (stance phase) defined in a specific vision-based reference model.” encompasses a mental process (e.g., algorithm or mathematical reasoning).
Claim 4, the limitation of “extract the user's step by using a continuous outer envelope extracted from the sensing data;” and “distinguishing, in the envelope constituting the one step” involves processes that can be performed mentally (e.g., observation and judgement).
Claim 5, the limitation of “distinguish, in the envelope constituting the one step” similarly encompasses mental process (observation and judgement).
Claim 6-8, the limitations of “extract individual steps from continuous outer envelopes extracted from the sensing data;”, “overlap data of the extracted individual steps to generate a heatmap in”, and “generate the gait data by matching each gait posture phase defined in the specific vision-based reference model” are mental process.
Claim 9, the limitations of “use at least one of the heatmap and the median graph” and “analyze a gait control state based on a distribution degree;” also encompass mental process.
Claim 10-12, the limitation of “calculate the degree of pain and discomfort related to the user's predicted disease” is a process that encompass performance in the human mind that a person can calculate with a pen and paper, under the broad reasonable interpretation. Also, the limitations of “reflecting the collected sensing data in the analysis” and “in case that the predicted disease has been analyzed using the gait data.” are a mental process.
Claim 13-15, the limitation of “provide gait-related content” encompasses a human mind including judge, observation, and mental process and the limitation of “display device of a user walking on the treadmill” and the limitation of “control the providing of the gait-related content” encompass mental processes such as presentation, opinion, and judgement.
Merely including instructions to implement an abstract idea on a computer does not integrate the judicial exception into a practical application. Accordingly, for Claim 1-15, the judicial exception is not integrated into a practical application.
Step 2A, Prong Two:
The claims do not include additional elements sufficient to amount to significantly more than the judicial exception.
The limitations of “a data collector (claim 1)”, “a data generator (claim 1-6)”, and “a controller (claim 1,9, 10-15)” represent generic computer components performing the abstract idea at a high level of generality and amount to no more than instructions to apply the exception using a generic computer. Accordingly, the components, “a data collector”, “a data generator”, and “a controller” do not meaningfully limit the abstract idea and do not integrate the judicial exception into a practical application.
The claims, including dependent claims, are analyzed as a whole to determine whether additional limitations amount to significantly more than the abstract idea.
Claim 1, the additional limitation of “the gait data is displayed, recorded, or transmitted,” is merely insignificant extra-solution activity that a person can present, record and transmit with a pen and paper.
Claim 3, the limitation of “a specific vision-based reference model.” Is an abstract idea (e.g., an algorithm or mathematical idea).
Claim 4, the limitation of “in the envelope constituting the one step, an LR section in which a current value increases, an MS section in which the current value decreases after the LR section, and a PW section in which the current value decreases and reaches a minimum value after the MS section.” Is a process that can be performed mentally or with a pen and paper.
Claim 5, the limitation of “in the envelope constituting the one step, a TS section in which the current value decreases after the MS section,” is also a process mentally or a person can process with a pen and paper as well.
Claim 9, the limitation of “the shape of a curve, a distinction location for each gait posture phase, a slope between the distinction locations, and relative sizes of the distinction locations.” Is a process that can be performed with a pen and paper.
Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception. Regarding Claim 1-16, the judicial exception is not integrated into a practical application.
Step 2B:
The claim 1-15 do not include additional elements that are sufficient to amount significantly more than the judicial exception.
As discussed above, the “data collector”, “data generator”, and “controller” merely provide instructions to apply the judicial exception using a generic computer performing routine and conventional activities, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Therefore, these elements do not provide an inventive concept sufficient to transform the claimed abstract idea into patent eligible subject matter.
Claim Rejections - 35 USC § 112
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.
Claim 3,6,9,10 and 13 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 3 recites the limitation "the collected sensing data” in the line 1-2. There is insufficient antecedent basis for this limitation in the claim. Therefore, the scope of the claim is unclear under 35 U.S.C 112(b). For the purposes of examination, “the collected sensing data” is interpreted to be “the sensing data”
The term “specific” in claim 3 is a relative term which renders the claim indefinite. The term “specific” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the scope of “a specific vision-based reference model” is indefinite
Claim Rejections - 35 USC § 103
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.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over “Chu” (US 20110312473 A1) in view of “Ohki” (Title: Treadmill Motor Current Value Based Walk Phase Estimation.; 2009:7131-4).
Regarding claim 1, Chu teaches A gait analysis device (Abstract: “A new apparatus, system and method for fall prevention training is provided that delivers, studies and analyzes the biomechanics of a disturbance event, such as a slip or trip incident”, and Fig. 1. The apparatus 10) comprising:
a data collector (sensor 205) configured to collect sensing data obtained by sensing an instantaneous current value that is supplied to a treadmill to operate a motor of the treadmill (Paragraph, 0156: “the motor current 250 is detected by a sensor 205 in FIG. 15B. This sensor 205 can be any current monitoring sensor such a hall effect sensor.”, and “The motor current signal 250 is preferably amplified by a gain, and offset relative the zero current output level of the sensor 205 and, in this embodiment, converted through analog to digital conversion 254 to a digital signal for processing.”, and Paragraph, 0154: “heel strikes can be detected by monitoring motor current.” And “To maintain the belt at a constant velocity, the motor drive 208 increases the motor current to maintain the constant velocity in the presence of the increased drag”);
a data generator (central control unit 32) configured to analyze the sensing data to generate a user's gait data (Paragraph, 0154: “heel strikes can be detected by monitoring motor current.”, Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred”, Paragraph, 0156: “The heel strike feature is detected from this smoothed output in the event detection algorithm 258.”, Paragraph, 0159: “The detection algorithm 258 may use one or more of these variables.”, and Paragraph, 0100: “The apparatus 10 itself preferably includes its own central control unit 32 with the appropriate control algorithm and custom motor control software which provides bilateral, independent bi-directional real-time biofeedback motor control function. The control algorithm is written as a state machine, and responds according to a lookup-table of inputs to determine the next step) and
a controller (central control unit 32) configured to send the gait data so that the gait data is displayed, recorded, or transmitted (Paragraph, 0035: “The current invention improves on the existing systems by delivering systematic, progressive perturbations while, if desired, simultaneously recording relevant training data)”, and paragraph, 0100: “the treadmill apparatus 10 of the present invention includes two main components, the perturbation platform (PPU) 24 with force measurement capability, safety harness 38 and handrails 36 as well as a central control unit (CCU) 32 with control algorithms, safety interlocks, data storage and transfer protocols, and user interface.”).
Chu teaches “of the instantaneous current value” as mentioned above. However, Chu is silent on wherein the data generator (the data structure 238) is configured to: set a threshold that is a maximum value for a predetermined time in an idle state.
Ohki teaches wherein the data generator (the data structure 238) is configured to: set a threshold that is a maximum value of the instantaneous current value for a predetermined time in an idle state (P7132, line 12-22, the second paragraph on the right column: “First, the algorithm approximates ITloss by belt velocity v, and formulates to ITloss(v). ITloss(v) depends on the belt condition such as temperature and humidity. Therefore, ITloss(v) is measured before the usage of the treadmill each time”, “Second, the algorithm constructs a motor current threshold IThreshold(v) by adding offset to ITloss (v) in order to reduce the affect of noise. Finally, the algorithm observes the motor current I and estimates the walk phase by determining whether I exceeds IThreshold(v) or not Therefore, as in (2). Figure 4 shows a block diagram of the bilateral separated treadmill within the estimation algorithm.”, and P7133, line 13-16, the first column on the right column: “Therefore, it was determined that the gait characteristics of hemiplegic patients could affect to the accuracy of walk phase estimations”)
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It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Ohki because Ohki teaches a known threshold-setting technique in which a threshold value is determined from treadmill motor current measurements obtained during a non-walking/idle condition prior to gait detection (Chu, P7132, line 16-21, the second paragraph on the right column). Since Chu similarly analyzes treadmill motor current to detect gait-related events. One of ordinary skill in the art would have applied Ohki’s known threshold-based motor-current analysis technique to Chu’s similar treadmill motor-current gait detection system to improve signal discrimination and gait-event detection reliability by reducing background motor-current fluctuations and false detections, yielding predictable results.
Claim 2, 4-5, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Ohki and in further view of “Cho” (WO 2020209595 A2)
Regarding claim 2, Chu in view of Ohki teaches the gait analysis device of claim 1(See rejection of claim 1 above),
Claim 2, Chu does not teach wherein the data generator is configured to: extract a first step and a second step of the user by continuous outer envelopes extracted from the sensing data, the first step and the second step being repeated sequentially; and
Cho teaches wherein the data generator is configured to: extract a first step and a second step of the user by continuous outer envelopes extracted from the sensing data, the first step and the second step being repeated sequentially (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform.”, Paragraph, 0066: ”FIG. 6A illustrates an envelope of a current value measured when the analysis apparatus 100 is walking”, and Paragraph, 0058: “the gait measured by the analysis apparatus 100 indicates 15 steps between 10 seconds, and since the left foot and the right foot are repeatedly used for walking”); and
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because Cho teaches extracting gait step information from continuous outer envelopes of treadmill motor current signals (Cho, 0012, 0066), while Chu already determines gait events using treadmill motor current sensing (Chu, 0155,0156). Therefore, It would have been recognized that Cho’s known envelope-based signal processing technique would improve Chu’s step extraction and pattern recognition in the same way, with predictable results.
Chu does not teach distinguish a step of the user's left foot from a step of the user's right foot by defining a series of the first steps as index walking in case that maximum values of envelopes of the series of the first steps are greater than values obtained by adding a reference value to maximum values of envelopes of a series of the second steps.
Cho teaches distinguish a step of the user's left foot from a step of the user's right foot by defining a series of the first steps as index walking (Paragraph, 0058:” Referring to FIG. 4C, the gait measured by the analysis apparatus 100 indicates 15 steps between 10 seconds, and since the left foot and the right foot are repeatedly used for walking, the current value of one step is observed to be smaller than the current value of the other step.”) in case that maximum values of envelopes of the series (Paragraph, 0016:” Peak is the maximum value of current in the envelope”). However, Cho is still silent “on obtained by adding a reference value to maximum values of envelopes of a series of the second steps.”.
Ohki explicitly teaches on obtained by adding a reference value to maximum values of envelopes of a series of the second steps (P7132, line 16-21, the second paragraph on the right column: “the algorithm constructs a motor current threshold IThreshold(v) by adding offset to ITloss(v) in order to reduce the affect of noise. Finally, the algorithm observes the motor current I and estimates the walk phase by determining whether I exceeds IThreshold(v) or not”).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho with Ohki because Cho teaches distinguishes alternating gait steps by comparing envelope characteristics of repeated steps, while Ohki teaches adding an offset/reference value to a motor-current threshold to reduce noise and improve signal discrimination (Ohki, P7132, line 16-21, the second paragraph on the right column).
It would have been obvious to one of ordinary skill in the art to modify Chu with Ohki to incorporate the threshold-offset technique because applying a threshold-offset value derived from baseline motor-current data would distinguish gait-related motor-current changes from baseline motor-current variation Such modification would have predictably reduced the effect of signal fluctuation during gait analysis and improved determination of gait-pattern and abnormal determinations (Ohki, P7132, line 16-21, the second paragraph on the right column)
It would have been obvious to one of ordinary skill in the art to modify Chu with Cho’s envelope-based gait analysis technique because the envelope-based step-comparison process techniques distinguishes individual gait steps from motor-current-derived data and enables determination of gait patterns from the extracted gait events. Applying Cho’s envelope-based step-comparison technique to Chu’s motor-current-based gait analysis would have predictably improved identification of gait events and gait patterns (Cho, Paragraph, 0016,0058).
Regarding claim 4, Chu in view of Ohki teaches the gait analysis device of claim 1(See rejection of claim 1 above),
Chu teaches wherein the data generator is configured to: extract the user's step by using a continuous outer envelope extracted from the sensing data (Fig. 16, 18C, and 19A and Paragraph, 0155: “In one embodiment, heel strikes can be detected by monitoring motor current. A graph of motor current 240 versus time can be seen in FIG. 16” and Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred thereby warranting delivery of a perturbation”).
Chu does not teach explicitly “generate the gait data by distinguishing, in the envelope constituting the one step;”
Cho teaches generate the gait data by distinguishing, in the envelope constituting the one step (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform.”),
Chu teaches an LR section in which a current value increases, an MS section in which the current value decreases after the LR section, and a PW section in which the current value decreases and reaches a minimum value after the MS section (Paragraph, 0154: “, heel strikes can be detected by monitoring motor current. A graph of motor current 240 versus time can be seen in FIG. 16”, Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred thereby warranting delivery of a perturbation.”, and Fig. 16, and 19A illustrate increasing slope starting at the heel-strike (IC) 241, approaching the peak (maybe around Mid-stance), and decreasing slope to toe-off (pre-swing, PW) 242, similar pattern to the invention figure except for MS, TS, and LR. Fig. 18C illustrates current pattern over time based on stance phase).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because both references analyze gait characteristics of a treadmill user based on motor-current signals. Cho teaches distinguishing features within an envelope corresponding to an individual step and using the envelope shape to determine a motion pattern of the user (Cho, paragraph 0050 and 0059). Incorporating Cho’s envelope-based step characterization into Chu’s gait-analysis system would have predictably improved the determination of gait patterns and abnormal gait conditions from sensed treadmill data.
Regarding claim 5, Chu in view of Ohki and Cho teaches the gait analysis device of claim 4 (See rejection of claim 4 above),
Chu teaches wherein the data generator is configured to distinguish, in the envelope constituting the one step, a TS section in which the current value decreases after the MS section, (Fig. 16, 18C, and 19A and Paragraph, 0155: “In one embodiment, heel strikes can be detected by monitoring motor current. A graph of motor current 240 versus time can be seen in FIG. 16” and Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred thereby warranting delivery of a perturbation”). However, Chu does not explicitly teach “in the envelope constituting the one step”.
Cho teaches in the envelope constituting the one step (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform”),
Chu teaches a TS section in which the current value decreases after the MS section, wherein the TS section is before the PW section, and a decreasing slope of the TS section is less than a decreasing slope of the MS section and a decreasing slope of the PW section. (Paragraph, 0154: “, heel strikes can be detected by monitoring motor current. A graph of motor current 240 versus time can be seen in FIG. 16”, Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred thereby warranting delivery of a perturbation.”, and Fig. 16, and 19A illustrate increasing slope starting at the heel-strike (IC) 241, approaching the peak (maybe around Mid-stance), and decreasing slope to toe-off (pre-swing, PW) 242, similar pattern to the invention figure except for MS, TS, and LR. Fig. 18C illustrates current pattern over time based on stance phase)
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because both references analyze gait characteristics of a treadmill user based on motor-current signals. Cho teaches distinguishing features within an envelope corresponding to an individual step and using the envelope shape to determine a motion pattern of the user (Cho, paragraph 0050 and 0059). Incorporating Cho’s envelope-based step characterization into Chu’s gait-analysis system would have provided additional step-level information for determining gait patterns and abnormal gait conditions from sensed treadmill data.
Regarding claim 14, Chu in view of Ohki and Cho teaches the gait analysis device of claim 4 (See rejection of claim 4 above),
Chu does not teach wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill and control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data.
Cho teaches wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill (paragraph, 20: “The analysis device may further include a display of a curved surface disposed in front of the treadmill to collect the exercise desire of the target to be measured, and,” and “by analyzing the walking pattern of the target to be measured,”) and
Cho teaches control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data. (Paragraph, 11: “a processor that determines a movement pattern of the target to be measured based on the instantaneous current value detected through the current sensor.” And Paragraph, 49: “The processor 170 May determine the motion pattern of the object to be measured based on the current value sensed through the current sensor 120.”).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because both references analyze gait characteristics of a treadmill user based on motor-current signals and Cho provides an analysis of a user’s gait and health condition through a display device (Cho, Paragraphs, 42,43, and 78). It would have been obvious to one of ordinary skill in the art to incorporate Cho’s display into Chu’s gait-analysis system so that the display could provide the user with gait-analysis results and gait-condition information generated from the analyzed gait data. Such a modification would have predictably enabled display of gait-related content based on analyzed gait patterns and gait-pattern changes.
Regarding claim 15, Chu in view of Ohk and Choi teaches the gait analysis device of claim 5 (See rejection of claim 5 above),
Chu does not teach wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill and control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data.
Cho teaches wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill (paragraph, 20: “The analysis device may further include a display of a curved surface disposed in front of the treadmill to collect the exercise desire of the target to be measured, and,” and “by analyzing the walking pattern of the target to be measured,”) and
Cho teaches control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data. (Paragraph, 11: “a processor that determines a movement pattern of the target to be measured based on the instantaneous current value detected through the current sensor.” And Paragraph, 49: “The processor 170 May determine the motion pattern of the object to be measured based on the current value sensed through the current sensor 120.”).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because both references analyze gait characteristics of a treadmill user based on motor-current signals and Cho provides an analysis of a user’s gait and health condition through a display device (Cho, Paragraphs, 42,43, and 78). It would have been obvious to one of ordinary skill in the art to incorporate Cho’s display into Chu’s gait-analysis system so that gait-related information derived from analyzed gait data could be communicated to the user through a display. Such a modification would have predictably enabled display of gait-related content based on analyzed gait patterns and gait-pattern changes.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable Chu in view of Ohki and in further view of “Wu” (US9996739)
Regarding claim 3, Chu in view of Ohki teaches the gait analysis device of claim 1 (See rejection of claim 1 above),
Chu teaches wherein the data generator is configured to analyze the collected sensing data (Paragraph, 0155: “FIGS. 18A-18F show various data derived from motor current motor position which can be used to identify when a heel strike 241 or toe-off 242 event has occurred”, and Paragraph, 0156: “the motor current 250 is detected by a sensor 205 in FIG. 15B”, and “The heel strike feature is detected from this smoothed output in the event detection algorithm 258”) to generate the gait data (Paragraph, 0155: “various data derived from motor current motor position”, and Paragraph, 0156: “The heel strike feature is detected from this smoothed output in the event detection algorithm 258.”), which is divided into gait posture phases (stance phase) (Paragraph, 0161: “The event detection algorithm 258 also identifies events that can occur as a percentage of step time (e.g. mid-stance).”, and “from between 0-100% of the gait cycle from heel strike to toe off, including but not limited to the braking phase and the propulsion phase of stance.”). However, Chu is silent on “defined in a specific vision-based reference model.”
Wu teaches “based in a specific vision-based reference model” (Col2, lines 19-20: “cameras are also used to analyze gait”, Col4, lines 66-67: “FIG. 1 is a graphical depiction of various exemplary segments of a gait cycle;”, Col5, lines 42-45: “during the single stance phase of a walking cycle, there is one foot/part of the leg that is not moving as shown in FIG. 1”).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Wu to incorporate Wu’s a specific vision-based reference model into Chu’s system in order to improve gait phase identification and gait characterization. Applying this known gait analysis technique to Chu’s gait analysis system would have provided a standardized reference for classifying gait posture phases by enabling gait data derived from motor-current signals to be correlated with defined gait posture phases of a gait cycle (Wu, Col2, lines 37-42).
Claim 6-8 are rejected under 35 U.S.C. 103 as being unpatentable Chu in view of Ohki, Cho, Wu, Hayash (“Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network”, Sensors, 2021, 21(11), 3643), and further “Cox” (US7717826)
Regarding claim 6, Chu in view of Ohki and Wu teaches the gait analysis device of claim 3 (See rejection of claim 3 above),
Chu does not explicitly teach wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data.
Cho teaches wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform.”),
Chu is silent on “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”;
Hayashi teaches “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”; (Page 4, section 3 and Fig. 3: “The procedure of our gait classification method is as follows.1. Calculation of STFT of the received signal (Figure 3 shows an example). 2. Extraction of feature envelopes from the STFT spectrogram (vu(t), vm(t), and vl(t) in Figure 3). 3. Inputting the feature envelopes to the LSTM network (Figure 4).” and Fig. 3 and 6: illustrates spectrogram image (analogous to heatmap), Page 8, section 4.3, the 2nd column, line 3-4: examples of obtained heat maps are shown in Figure 6); and
Chu does not teach “generate the gait data by matching each gait posture phase defined in the specific vision-based reference model to at least one of the heatmap and a median graph derived from the sensing data”.
Wu teaches “generate the gait data by matching each gait posture phase (Col6, lines 1-12: “A fast, robust and accurate gait cycle segmentation system and method is set forth, which can use a single non-calibrated camera and is not limited by viewing angles. With reference to FIG. 2, the method 10 includes one or more of the following steps: 1) acquisition of a sequence of images or video frames of interest (step 20); 2) feature detection in each frame (step 30); 3) cross-frame feature stability/duration calculation and filtering (step 40); 4) gait cycle/step segmentation (step 50); 5) (optional) gait assessment (step 60). Optional steps include background elimination (step 70) and human body part detection/tracking (step 80). All of these steps will be described in detail below.”, and Col2, lines 37-42: “Model-based methods apply human body/shape or motion models to recover features of gait mechanics and kinematics. The relationship between body parts will be used to segment each stride/step or for other purposes. Models include generative and discriminative models.”) defined in the specific vision-based reference (Col2, lines 19-20: “cameras are also used to analyze gait”, Col4, lines 66-67: “FIG. 1 is a graphical depiction of various exemplary segments of a gait cycle;”, Col5, lines 42-45: “during the single stance phase of a walking cycle, there is one foot/part of the leg that is not moving as shown in FIG. 1”.
Wu is silent on “to at least one of the heatmap and a median graph derived from the sensing data”.
Cox teaches “to at least one of the heatmap and a median graph derived from the sensing data” ((Col6, line 19-30): “The Software calculates an average stride profile, based on analyzing numerous stride waveforms and after accounting for the slight differences in the stride to-stride durations in each leg. This averaging process is intended to “average out the effects of the treadmill itself, since the user contacts a different section of the treadmill belt with each step. The average stride profiles are thus believed to be predominantly influenced by the gait of the user, and thus provide a suitable and sensitive signature for performing gait analysis.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cho to incorporate envelope-based step analysis into Chu’s system because envelope-based step analysis permits extraction of gait characteristics corresponding to individual gait steps and determination of gait patterns (Cho, Paragraph 0050, 0059).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hayashi to incorporate heatmap representations because heatmap representations facilitate classification of gait patterns from extracted gait features (Hayashi,” Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network”, Sensors 2021, 21, 3643, page 8, Section 4.3, the second paragraph, lines 14-17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Wu to incorporate technique which segments gate cycles into defined gait phases provides a fast and accurate framework for analyzing gait-cycle characteristics (Wu, Col2, lines 38-43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cox because averaged stride profiles provide a suitable and sensitive gait signature (Cox, Col6, lines 19-30).
Regarding claim 7, Chu in view of Ohki and Cho teaches the gait analysis device of claim 4 (See rejection of claim 4 above),
Chu does not explicitly teach wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data.
Cho teaches wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform.”),
Chu is silent on “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”;
Hayashi teaches “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”; (Page 4, section 3 and Fig. 3: “The procedure of our gait classification method is as follows.1. Calculation of STFT of the received signal (Figure 3 shows an example). 2. Extraction of feature envelopes from the STFT spectrogram (vu(t), vm(t), and vl(t) in Figure 3). 3. Inputting the feature envelopes to the LSTM network (Figure 4).” and Fig. 3 and 6: illustrates spectrogram image (analogous to heatmap), Page 8, section 4.3, the 2nd column, line 3-4: examples of obtained heat maps are shown in Figure 6); and
Chu does not teach “generate the gait data by matching each gait posture phase defined in the specific vision-based reference model to at least one of the heatmap and a median graph derived from the sensing data”.
Wu teaches “generate the gait data by matching each gait posture phase (Col6, lines 1-12: “A fast, robust and accurate gait cycle segmentation system and method is set forth, which can use a single non-calibrated camera and is not limited by viewing angles. With reference to FIG. 2, the method 10 includes one or more of the following steps: 1) acquisition of a sequence of images or video frames of interest (step 20); 2) feature detection in each frame (step 30); 3) cross-frame feature stability/duration calculation and filtering (step 40); 4) gait cycle/step segmentation (step 50); 5) (optional) gait assessment (step 60). Optional steps include background elimination (step 70) and human body part detection/tracking (step 80). All of these steps will be described in detail below.”, and Col2, lines 37-42: “Model-based methods apply human body/shape or motion models to recover features of gait mechanics and kinematics. The relationship between body parts will be used to segment each stride/step or for other purposes. Models include generative and discriminative models.”) defined in the specific vision-based reference (Col2, lines 19-20: “cameras are also used to analyze gait”, Col4, lines 66-67: “FIG. 1 is a graphical depiction of various exemplary segments of a gait cycle;”, Col5, lines 42-45: “during the single stance phase of a walking cycle, there is one foot/part of the leg that is not moving as shown in FIG. 1”.
Wu is silent on “to at least one of the heatmap and a median graph derived from the sensing data”.
Cox teaches “to at least one of the heatmap and a median graph derived from the sensing data” ((Col6, line 19-30): “The Software calculates an average stride profile, based on analyzing numerous stride waveforms and after accounting for the slight differences in the stride to-stride durations in each leg. This averaging process is intended to “average out the effects of the treadmill itself, since the user contacts a different section of the treadmill belt with each step. The average stride profiles are thus believed to be predominantly influenced by the gait of the user, and thus provide a suitable and sensitive signature for performing gait analysis.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cho to incorporate envelope-based step analysis into Chu’s system because envelope-based step analysis permits extraction of gait characteristics corresponding to individual gait steps and determination of gait patterns (Cho, Paragraph 0050, 0059).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hayashi to incorporate heatmap representations because heatmap representations facilitate classification of gait patterns from extracted gait features (Hayashi,” Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network”, Sensors 2021, 21, 3643, page 8, Section 4.3, the second paragraph, lines 14-17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Wu to incorporate technique which segments gate cycles into defined gait phases provides a fast and accurate framework for analyzing gait-cycle characteristics (Wu, Col2, lines 38-43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cox because averaged stride profiles provide a suitable and sensitive gait signature (Cox, Col6, lines 19-30).
Regarding claim 8, Chu in view of Ohki and Cho teaches the gait analysis device of claim 5 (See rejection of claim 5 above),
Chu does not explicitly teach wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data.
Cho teaches wherein the data generator is configured to: extract individual steps from continuous outer envelopes extracted from the sensing data (Paragraph, 0012: “the processor may determine a motion pattern of the object to be measured based on an envelope of a current signal corresponding to one step of the object to be measured and a shape of an envelope internal waveform.”),
Chu is silent on “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”;
Hayashi teaches “overlap data of the extracted individual steps to generate a heatmap in which data for each step is distributed at each distinction time point within a unit time”; (Page 4, section 3 and Fig. 3: “The procedure of our gait classification method is as follows.1. Calculation of STFT of the received signal (Figure 3 shows an example). 2. Extraction of feature envelopes from the STFT spectrogram (vu(t), vm(t), and vl(t) in Figure 3). 3. Inputting the feature envelopes to the LSTM network (Figure 4).” and Fig. 3 and 6: illustrates spectrogram image (analogous to heatmap), Page 8, section 4.3, the 2nd column, line 3-4: examples of obtained heat maps are shown in Figure 6); and
Chu does not teach “generate the gait data by matching each gait posture phase defined in the specific vision-based reference model to at least one of the heatmap and a median graph derived from the sensing data”.
Wu teaches “generate the gait data by matching each gait posture phase (Col6, lines 1-12: “A fast, robust and accurate gait cycle segmentation system and method is set forth, which can use a single non-calibrated camera and is not limited by viewing angles. With reference to FIG. 2, the method 10 includes one or more of the following steps: 1) acquisition of a sequence of images or video frames of interest (step 20); 2) feature detection in each frame (step 30); 3) cross-frame feature stability/duration calculation and filtering (step 40); 4) gait cycle/step segmentation (step 50); 5) (optional) gait assessment (step 60). Optional steps include background elimination (step 70) and human body part detection/tracking (step 80). All of these steps will be described in detail below.”, and Col2, lines 37-42: “Model-based methods apply human body/shape or motion models to recover features of gait mechanics and kinematics. The relationship between body parts will be used to segment each stride/step or for other purposes. Models include generative and discriminative models.”) defined in the specific vision-based reference (Col2, lines 19-20: “cameras are also used to analyze gait”, Col4, lines 66-67: “FIG. 1 is a graphical depiction of various exemplary segments of a gait cycle;”, Col5, lines 42-45: “during the single stance phase of a walking cycle, there is one foot/part of the leg that is not moving as shown in FIG. 1”.
Wu is silent on “to at least one of the heatmap and a median graph derived from the sensing data”.
Cox teaches “to at least one of the heatmap and a median graph derived from the sensing data” ((Col6, line 19-30): “The Software calculates an average stride profile, based on analyzing numerous stride waveforms and after accounting for the slight differences in the stride to-stride durations in each leg. This averaging process is intended to “average out the effects of the treadmill itself, since the user contacts a different section of the treadmill belt with each step. The average stride profiles are thus believed to be predominantly influenced by the gait of the user, and thus provide a suitable and sensitive signature for performing gait analysis.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cho to incorporate envelope-based step analysis into Chu’s system because envelope-based step analysis permits extraction of gait characteristics corresponding to individual gait steps and determination of gait patterns (Cho, Paragraph 0050, 0059).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hayashi to incorporate heatmap representations because heatmap representations facilitate classification of gait patterns from extracted gait features (Hayashi,” Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network”, Sensors 2021, 21, 3643, page 8, Section 4.3, the second paragraph, lines 14-17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Wu to incorporate technique which segments gate cycles into defined gait phases provides a fast and accurate framework for analyzing gait-cycle characteristics (Wu, Col2, lines 38-43).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cox because averaged stride profiles provide a suitable and sensitive gait signature (Cox, Col6, lines 19-30)..
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Ohki, Cho, Wu, Hayashi, Cox, and in further view of Rehman (“Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach”, Springer Nature, Scientific Reports, (2019) 9:17269)
Regarding claim 9, Chu in view of Ohki, Cho, Wu, Hayashi, and Cox teaches the gait analysis device of claim 6 (See rejection of claim 6 above),
Chu does not teach wherein the controller is configured to use at least one of the heatmap and the median graph as the gait data to: analyze a gait control state based on a distribution degree; or
Cox teaches wherein the controller is configured to use at least one of the heatmap and the median graph as the gait data to: analyze a gait control state based on a distribution degree (Col6, lines 17-23: “The Software calculates an average stride profile, based on analyzing numerous stride waveforms and after accounting for the slight differences in the stride to-stride durations in each leg. This averaging process is intended to “average out the effects of the treadmill itself, since the user contacts a different section of the treadmill belt with each step”, and Figs 8-10: illustrates average stride profiles for gait analysis).
Chu does not teach “analyze a predicted disease based on at least one among the shape of a curve, a distinction location for each gait posture phase, a slope between the distinction locations, and relative sizes of the distinction locations”.
Cox teaches “analyze a predicted disease based on at least one among the shape of a curve, a distinction location for each gait posture phase, a slope between the distinction locations, and relative sizes of the distinction locations.” (Col6, line 38-44: “a variety of parameters that are present in the stride profiles. These parameter measurements are shown in FIG. 10. Several of these parameters were judged to be very sensitive indicators of gait anomalies such as Stride profile, average stride profile, average cycle fraction difference, stride length unbalance, estimated weight unbalance, difference in max location, and difference in slope max location.”, Col6, line 45-46:” Average stride profiles are provided to illustrate the ability of ESA methods to characterize gait variations, and Figs 9-10: illustrate measurable stride profile parameters). However, Cox does not explicitly teach “analyze a predicted disease”.
Rehman teaches “analyze a predicted disease” (Abstract: “we identified a subset of gait characteristics for accurate early classification of PD.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Cox and further Rehman to incorporate Cox’s gait-profile analysis into Chu’s gait-analysis system and to apply Rehman’s disease-classification techniques based on gait characteristics because each reference addresses analysis of gait-related data obtained from repeated walking cycles and because Cox’s gait analysis provide stride-profile parameters, including profile shape, location-based parameters, and slope-related parameters, which are very sensitive indicators of gait anomalies and characterize gait variances (Cox, Col6, lines 23-26, 45-49) and Rehman’s system provides gait characteristics-based accurate early classification of Parkinson’s Disease (Rahman, “Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach” Scientific Reports, (2019) 9:17269, Abstract:” we identified a subset of gait characteristics for accurate early classification of PD”). Such modification could provide predictable gait data for identifying disease state associated with abnormal gait patterns.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Ohki, Wu, and in further view of “Hughes” (US 20220323766 A1)
Regarding claim 10, Chu in view of Ohki and Wu teaches the gait analysis device of claim 3 (See rejection of claim 3 above),
Chu does not teach wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.
Hughes teaches “wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.” (Paragraph, 0197: “The gait features such as Step Length, Stride length, Stance Phase, Swing phase, Single support, Total double support, Load response, Pre-swing, Step time, Gait cycle, Cadence, Speed[m/s] and average speed may be analyzed real time and be used toward predicting the programming setting and pain level of the patient”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hughes to incorporate Hughes’s gait-based pain prediction techniques into Chu’s gait-analysis system because both reference addresses gait data obtained from repeated walking cycles and because Hughes’s gait features may be analyzed in real time and used toward predicting a patient’s pain level and programming setting (Hughes, Paragraph, 0197). The gait characteristics could be used as inputs to Hughes’s pain-prediction technique, enabling estimation of pain or discomfort from gait-derived information. Such modification would have yielded predictable results.
Claim 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Ohki, Cho, and in further view of Hughes.
Regarding claim 11, Chu in view of Ohki and Cho teaches the gait analysis device of claim 4 (See rejection of claim 4 above),
Chu does not teach wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.
Hughes teaches “wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.” (Paragraph, 0197: “The gait features such as Step Length, Stride length, Stance Phase, Swing phase, Single support, Total double support, Load response, Pre-swing, Step time, Gait cycle, Cadence, Speed[m/s] and average speed may be analyzed real time and be used toward predicting the programming setting and pain level of the patient”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hughes to incorporate Hughes’s gait-based pain prediction techniques into Chu’s gait-analysis system because both reference addresses gait data obtained from repeated walking cycles and because Hughes’s gait features may be analyzed in real time and used toward predicting a patient’s pain level and programming setting (Hughes, Paragraph, 0197). The gait characteristics could be used as inputs to Hughes’s pain-prediction technique, enabling estimation of pain or discomfort from gait-derived information. Such modification would have yielded predictable results.
Regarding claim 12, Chu in view of Ohki and Cho teaches the gait analysis device of claim 5 (See rejection of claim 5 above),
Chu does not teach wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.
Hughes teaches “wherein the controller is configured to calculate the degree of pain and discomfort related to the user's predicted disease by additionally reflecting the collected sensing data in the analysis in case that the predicted disease has been analyzed using the gait data.” (Paragraph, 0197: “The gait features such as Step Length, Stride length, Stance Phase, Swing phase, Single support, Total double support, Load response, Pre-swing, Step time, Gait cycle, Cadence, Speed[m/s] and average speed may be analyzed real time and be used toward predicting the programming setting and pain level of the patient”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Chu in view of Hughes to incorporate Hughes’s gait-based pain prediction techniques into Chu’s gait-analysis system because both reference addresses gait data obtained from repeated walking cycles and because Hughes’s gait features may be analyzed in real time and used toward predicting a patient’s pain level and programming setting (Hughes, Paragraph, 0197). The gait characteristics could be used as inputs to Hughes’s pain-prediction technique, enabling estimation of pain or discomfort from gait-derived information. Such modification would have yielded predictable results.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable Chu in view of Ohki and Wu, and in further view of Cho
Regarding claim 13, Chu in view of Ohki and Wu teaches the gait analysis device of claim 3 (See rejection of claim 3 above),
Chu does not teach wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill and control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data.
Cho teaches wherein the controller is configured to: provide gait-related content to a display device of a user walking on the treadmill (paragraph, 20: “The analysis device may further include a display of a curved surface disposed in front of the treadmill to collect the exercise desire of the target to be measured, and,” and “by analyzing the walking pattern of the target to be measured,”) and
Cho teaches control the providing of the gait-related content to the display device based on real-time gait patterns and gait pattern changes analyzed using the gait data. (Paragraph, 11: “a processor that determines a movement pattern of the target to be measured based on the instantaneous current value detected through the current sensor.” And Paragraph, 49: “The processor 170 May determine the motion pattern of the object to be measured based on the current value sensed through the current sensor 120.”).
It would have been obvious to one of ordinary skilled person in the art before the effective filing date of the claimed invention to modify Chu in view of Cho because both references analyze gait characteristics of a treadmill user based on motor-current signals and Cho provides an analysis of a user’s gait and health condition through a display device (Cho, Paragraphs, 42,43, and 78). It would have been obvious to one of ordinary skill in the art to incorporate Cho’s display into Chu’s gait-analysis system so that gait-related information derived from analyzed gait data could be communicated to the user through a display. Such a modification would have predictably enabled display of gait-related content based on analyzed gait patterns and gait-pattern changes.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORGAN S SHIM whose telephone number is (571)272-9032. The examiner can normally be reached Mon-Fri 7:30AM-4:30PM.
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/MORGAN SANGJO SHIM/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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