DETAILED ACTION
This action is pursuant to claims filed on 03/15/2023. Claims 1-18 are pending, with claims 8-18 withdrawn. A first action on the merits of claims 1-7 is as follows.
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 .
Election/Restrictions
Applicant's election with traverse of Group I in the reply filed on 04/20/2026 is acknowledged. The traversal is on the ground(s) that the Office has not demonstrated any indications of distinctness and that there is not a search burden. This is not found persuasive because as stated in the restriction requirement, the inventions are distinct because the method of Invention I could be conducted with a materially different apparatus or system from Inventions II and III, and states that Invention II claims many features that are not required in the method of Invention I. Additionally, Invention III does not contain many of the required features of the method of Invention I, showing that they cannot be used together. Similarly, Inventions II and III are distinct from each other as they contain many different types of sensors and configurations, therefore having materially different designs and are not used together. All of these factors are evidence of the distinctness between the three inventions, therefore fulfilling the indications of distinctness (A), (B), and (C) listed in MPEP 806.05(j) with detailed explanation, not simply a stated conclusion. Additionally, there is a search burden, as searching for the inventions together would require a different field of search for each invention, including searching different classes/subclasses and electronic resources, and employing different search queries.
The requirement is still deemed proper and is therefore made FINAL.
Claims 8-18 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Groups II and III, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 04/20/2026.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the haptic feedback system comprising a vest worn on the subject’s chest in claim 2 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Reference character “1080m” does not appear in the figures
Reference character “1612” does not appear in the figures
Reference character “1914” does not appear in the figures
Reference character “1616” does not appear in the figures
Reference character “1622” does not appear in the figures
Reference character “1624” does not appear in the figures
Reference character “1626” does not appear in the figures
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference character “Nz” in Figure 2B does not appear in the specification
Reference character “Fpz” in Figure 2B does not appear in the specification
Reference character “AFz” in Figure 2B does not appear in the specification
Reference character “Fz” in Figure 2B does not appear in the specification
Reference character “FCz” in Figure 2B does not appear in the specification
Reference character “Cz” in Figure 2B does not appear in the specification
Reference character “CPz” in Figure 2B does not appear in the specification
Reference character “Pz” in Figure 2B does not appear in the specification
Reference character “POz” in Figure 2B does not appear in the specification
Reference character “Oz” in Figure 2B does not appear in the specification
Reference character “Iz” in Figure 2B does not appear in the specification
Reference characters “Fp1” – “Fp2” in Figure 2B do not appear in the specification
Reference characters “AF1” – “AF8” in Figure 2B do not appear in the specification
Reference characters “F1” – “F10” in Figure 2B do not appear in the specification
Reference characters “FC1” – “FC6” in Figure 2B do not appear in the specification
Reference characters “FT7” – “FT10” in Figure 2B do not appear in the specification
Reference characters “C1” – “C6” in Figure 2B do not appear in the specification
Reference characters “T7” – “T10” in Figure 2B do not appear in the specification
Reference characters “CP1” – “CP6” in Figure 2B do not appear in the specification
Reference characters “TP7” – “TP10” in Figure 2B do not appear in the specification
Reference characters “P1” – “P10” in Figure 2B do not appear in the specification
Reference characters “PO1” – “PO10” in Figure 2B do not appear in the specification
Reference characters “O1” – “O2” in Figure 2B do not appear in the specification
Reference characters “I1” – “I2” in Figure 2B do not appear in the specification
Reference character “252” in Figure 2D does not appear in the specification
Reference character “400” in Figure 4A does not appear in the specification
Reference character “P1” in Figure 9C does not appear in the specification
Reference character “P2” in Figure 9C does not appear in the specification
Reference character “P3” in Figure 9C does not appear in the specification
Reference character “I” in Figure 11 does not appear in the specification
Reference character “λ” in Figure 11 does not appear in the specification
Reference character “
λ
6
” in Figure 11 does not appear in the specification
Reference character 1500” in Figure 15 does not appear in the specification
Reference character “
h
t
” in Figure 15 does not appear in the specification
Reference character “
x
t
” in Figure 15 does not appear in the specification
Reference character “
o
t
” in Figure 15 does not appear in the specification
Reference character “
C
t
” in Figure 15 does not appear in the specification
Reference character “
f
t
” in Figure 15 does not appear in the specification
Reference character “
r
t
” in Figure 15 does not appear in the specification
Reference character “
z
t
” in Figure 15 does not appear in the specification
Reference character “1806” in Figure 18B does not appear in the specification
Reference character “1808” in Figure 18C does not appear in the specification
Reference character “R” in Figure 2C, Figure 8B, and Figure 12B does not appear in the specification
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "236", “1008”, and "1080m" have both been used to designate “the optical interrogator”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
On page 31, line 14 reads “Fig. 10B illustrates … the optical interrogator 1080m”. It appears that this should read “the computer controller 1080”, as Figure 10B shows reference figure 1080, not 1080m, and reference character 1080 refers to the computer controller, not the optical interrogator. Additionally, reference character 1080m does not appear anywhere in the figures.
Appropriate correction is required.
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 6 is 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.
Regarding claim 6, the claim recites the limitation “the plurality of sensors” in line 3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what the sensors are, what data they collect, and how they are included in the method. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention. For purposes of examination, it is being interpreted as any plurality of sensors that can be connected to the foot of the subject.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US 20210401324) in view of Wang (US 20050232532), Liu (US 20230075309), and Sarker (“Machine Learning: Algorithms, Real-World Applications and Research Directions”).
Regarding independent claim 1, Liao teaches a method of generating a touch signal corresponding to a state of a subject (Abstract: “The present application relates to a method for recognizing motion pattern of human lower limb and prostheses, orthoses, or exoskeletons thereof”) comprising:
receiving, with processing circuitry of a computer controller ([0091]: “the computing system 600 includes one or more central processing units (CPU) 601 that provides computing resources and controls the computer. CPU 601 may be implemented with a microprocessor or the like, and may also include one or more graphics processing units (GPU) 602 and/or a floating-point coprocessor for mathematical computations”), a plurality of signals corresponding to an applied plantar pressure from a foot of the subject, the applied plantar pressure from the foot of the subject corresponding with the state of the subject ([0026]: “the method may further comprise recognizing the different motion patterns by combining one or more of … the absolute acceleration to ground with a foot pressure distribution of the subject”. The different motion patterns are the states of the subject; Fig. 5 shows the different states of the subject (i.e. the standing stage and the swing stage).).
However, Liao does not teach the plurality of signals being wavelength signals.
Wang discloses a pressure sensor. Specifically, Wang teaches receiving a plurality of wavelength signals corresponding to an applied plantar pressure from a foot of a subject ([0045]: “the wavelength shift detector 308 may monitor the wavelength shifts and a map of pressure may be constructed based the wavelength shifts and the deformation of each pressure point is determined by monitoring the shift in Bragg wavelengths”). Liao and Wang are analogous art as they are in the same field of endeavor and are both related to devices that measure the pressure of a user’s foot.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the pressure signals being wavelength signals as Liao is silent on the type of signals received from the pressure sensors of the foot, and Wang discloses a specific type of pressure sensor receiving a specific type of signal in an analogous device.
The Liao/Wang combination teaches receiving, with the processing circuitry of the computer controller, a plurality of electroencephalography (EEG) signals corresponding to brain signals of the subject (Liao, [0026]: “the method may further comprise recognizing the different motion patterns by combining one or more of … an electroencephalographic signal (EEG) of the subject.”. EEG signals are measures of brain signals, therefore the EEG signals correspond to the brain signals of the subject.),
wherein each signal of the plurality of EEG signals corresponds with one signal of the plurality of wavelength signals (Liao, [0008]: “recognizing the motion pattern of the limb by inputting the motion data of the limb, which is obtained in real time by the sensor”. The motion data is input in real time, therefore the EEG signals and the foot pressure signals correspond with one another.).
However, the Liao/Wang combination does not teach wherein each signal of the plurality of EEG signals is registered by one channel of a plurality of channels on a brain control interface (BCI) mounted on the subject’s head.
Liu discloses an electroencephalogram device. Specifically, Liu teaches wherein each signal of the plurality of EEG signals is registered by one channel of a plurality of channels on a brain control interface (BCI) mounted on the subject’s head ([0160]: “the BCI obtains EEG signals generated in different regions of the head of the target object through electrodes connected to the target object, and the electrodes connected to the target object transmit EEG signals corresponding to each electrode to a terminal device corresponding to the BCI through different EEG channels.”). Liao and Liu are analogous art as they are in the same field of endeavor and are both related to devices that measure EEG signals from a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the brain control interface device from Liu into the Liao/Wang combination as the combination is silent on the device used to measure EEG signals, and Liu discloses a suitable device to measure EEG signals in an analogous device.
The Liao/Wang/Liu combination teaches transmitting, via the plurality of channels, the plurality of EEG signals to a classifier (Liao, Abstract: “The method may comprise collecting motion data; inputting the collected motion data and corresponding limb motion patterns into a classifier or pattern recognizer to train the classifier or the pattern recognizer”. The collected motion data includes the EEG signals),
training, with the processing circuity of the computer controller, the classifier using the plurality of EEG signals, the classifier identifying a correlation between the plurality of EEG signals and the plurality of wavelength signals (Liao, [0063]: “after the motion data of the extremity end of the subject in the swing stage of different motion patterns are collected, the obtained training data may be input to the pattern recognizer or the classifier to repeatedly train the pattern recognizer or the classifier with a plurality of times in step S104 to meet the required accuracy requirements”. The motion data includes the EEG data and the foot pressure signals, therefore the classifier determines a correlation between them.).
Liao discloses training the classifier to meet the required accuracy requirements, however, Liao does not disclose the specific methods used to train the model to the accuracy requirements.
Sarker discloses machine learning algorithms and training methods. Specifically, Sarker teaches selecting, via the classifier, a subsection of channels from the plurality of channels with a high correlation to the plurality of wavelength signals, combining, with the processing circuitry of the computer controller, the subsection of channels with the plurality of wavelength signals to form a secondary dataset, the secondary dataset being passed to a machine learning model, training, with the processing circuitry of the computer controller, the machine learning model using the secondary dataset (Page 10: “In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction … The selection of features, also known as the selection of variables or attributes in the data, is the process of choosing a subset of unique features (variables, predictors) to use in building machine learning and data science model. It decreases a model’s complexity by eliminating the irrelevant or less important features and allows for faster training of machine learning algorithms. A right and optimal subset of the selected features in a problem domain is capable to minimize the overfitting problem through simplifying and generalizing the model as well as increases the model’s accuracy [97]. Thus, “feature selection” [66, 99] is considered as one of the primary concepts in machine learning that greatly affects the effectiveness and efficiency of the target machine learning model. Chi-squared test, Analysis of variance (ANOVA) test, Pearson’s correlation coefficient, recursive feature elimination, are some popular techniques that can be used for feature selection.”; Page 11: “Pearson’s correlation is another method to understand a feature’s relation to the response variable and can be used for feature selection [99]. This method is also used for finding the association between the features in a dataset. The resulting value is [−1, 1] , where −1 means perfect negative correlation, +1 means perfect positive correlation, and 0 means that the two variables do not have a linear correlation. If two random variables represent X and Y, then the correlation coefficient between X and Y is defined”. This limitation describes common machine learning techniques where the model selects features based on their correlation value to use for training the machine learning model, therefore this method can be used in the machine learning model from Liao.). Liao and Sarker are analogous art as they are directed towards solving a similar problem and are both related to training machine learning models.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the training methods from Sarker into the Liao/Wang/Liu combination as the combination is silent on the methods used to train the model to the accuracy requirements, and Sarker discloses a suitable method for training the model in an analogous art.
The Liao/Wang/Liu/Sarker combination teaches using the machine learning model to generate the touch signal corresponding to the subject’s state, wherein the touch signal is relayed to a lower limb prosthesis, the touch signal eliciting a subject movement response, wherein the subject movement response comprises a movement of a foot of the lower limb prosthesis, the movement of the foot of the lower limb prosthesis corresponding to the subject’s state (Liao, [0064]: “the motion data may be classified or recognized by a linear discriminant analyzer, a secondary discriminant analyzer, a support vector machine, or a neural network”; [0004]: “It has been noted that in different motion patterns, such as upslope, downslope, upstairs or downstairs, the function performed by each joint of the lower limb of human body and the corresponding biomechanical characteristics vary considerably. Therefore, in order to achieve the desired function more accurately, the lower limb auxiliary device firstly should be able to accurately recognize the motion pattern of the user (wearer), and then control a driver to generate a preset auxiliary torque according to the corresponding motion pattern, thereby assisting the wearer to perform the desired action more easily”; [0009]: “the limb may for example comprise a … lower limb prosthesis”; [0071]: “In order to be able to recognize the motion pattern of the lower limb, prosthesis, orthosis or exoskeleton before the next foot contacting the ground, thereby enabling the lower limb, prosthesis, orthosis or exoskeleton to complete the required preparation during the swing stage, a predetermined triggering condition may be used to trigger the pattern recognition decision of the classifier or the pattern recognizer”. The motion pattern is the touch signal used to control the prosthesis, and the required preparation is the subject movement response. The required preparation is conducted during the swing stage, which is the subject state.).
Regarding claim 5, the Liao/Wang/Liu/Sarker combination teaches the method of claim 1.
However, the Liao/Wang/Liu/Sarker combination does not teach wherein each of the plurality of wavelength signals comprises a unique wavelength signal.
Wang teaches wherein each of the plurality of wavelength signals comprises a unique wavelength signal ([0014]: “a pressure sensor includes a flexible substrate. A waveguide may be disposed in or on the flexible substrate. The waveguide may include an input to receive an optical signal and an output to detect a reflected or transmitted optical signal. The pressure sensor also may include a Bragg grating array. The Bragg grating array may include Bragg gratings disposed in series along the length of the waveguide. Each Bragg grating may include a different characteristic grating spacing and thus reflect a different Bragg wavelength”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include each wavelength signal being a unique signal from Wang into the Liao/Wang/Liu/Sarker combination as the combination is silent on the distinction between the wavelength signals, and Wang discloses more information about the signals in an analogous device.
Regarding claim 7, the Liao/Wang/Liu/Sarker combination teaches the method of claim 1, wherein the subject’s state is at least one of a sitting position, a standing position, and a walking movement (Liao, [0010]: “the motion data may comprise one or more of an absolute motion trajectory to ground, an absolute velocity to ground, and an absolute acceleration to ground of the limb extremity end during the swing stage in the different motion modes.”. The swing stage is the subject state, which is a walking movement.).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over the Liao/Wang/Liu/Sarker combination as applied to claim 1 above, and further in view of Cruz-Hernandez (US 20170199569).
Regarding claim 2, the Liao/Wang/Liu/Sarker combination teaches the method of claim 1.
However, the Liao/Wang/Liu/Sarker combination does not teach wherein the touch signal is transmitted from the lower limb prosthesis to a haptic feedback system, the haptic feedback system comprising a vest worn on the subject’s chest, wherein the touch signal elicits a haptic response corresponding to the subject’s state.
Cruz-Hernandez discloses systems for haptically-enabled neural devices. Specifically, Cruz-Hernandez teaches wherein the touch signal is transmitted from the lower limb prosthesis to a haptic feedback system, the haptic feedback system comprising a vest worn on the subject’s chest, wherein the touch signal elicits a haptic response corresponding to the subject’s state ([0058]: “the computing device 302 may output a haptic effect associated with a manipulation (e.g., movement) and/or an attempted manipulation of a prosthetic limb 316. For instance, the user may attempt to manipulate the prosthetic limb 316 (e.g., to walk along a path). But the user may be unable to see or otherwise detect whether or not the prosthetic limb 316 actually moved (e.g., if the user is blind). The computing device 302 may output a first haptic effect (e.g., a vibration via the haptic output device 310) configured to confirm that the prosthetic limb 316 moved and/or a second haptic effect configured to notify the user that the prosthetic limb 316 did not move.”; [0033]: “the haptic output device 220 may be coupled to a wearable device comprising, for example, … a vest”). Liao and Cruz-Hernandez are analogous art as they are in the same field of endeavor and are both related to devices that can monitor a prosthetic limb.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the haptic feedback to a vest of a user from Cruz-Hernandez into the Liao/Wang/Liu/Sarker combination as it allows the user to receive feedback from the prosthetic limb, which can let them know about the movement of the prosthetic the state that it is in, making the user more informed of their own prosthetic.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over the Liao/Wang/Liu/Sarker combination as applied to claim 1 above, and further in view of Hendler (US 20210259615).
Regarding claim 3, the Liao/Wang/Liu/Sarker combination does not teach wherein the plurality of channels comprises 16 channels.
Hendler discloses an EEG measuring device. Specifically, Hendler teaches wherein the plurality of channels comprises 16 channels ([0417]: “EEG data acquisition and online processing: EEG data were acquired using the V-Amp™ EEG amplifier (Brain Products™, Munich Germany) and the BrainCap™ electrode cap with sintered Ag/AgCI ring electrodes providing 16 EEG channels”). Liao and Hendler are analogous art as they are in the same field of endeavor and they are both related to devices used to measure EEG signals of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the 16 channels from Hendler into the Liao/Wang/Liu/Sarker combination as the combination is silent on the number of channels, and Hendler discloses a suitable number of channels in an analogous device.
The Liao/Wang/Liu/Sarker/Hendler combination teaches or suggests the method of claim 1, wherein the subsection of channels selected by the classifier comprises 6 channels (Sarker, Page 10: “In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction … The selection of features, also known as the selection of variables or attributes in the data, is the process of choosing a subset of unique features (variables, predictors) to use in building machine learning and data science model. It decreases a model’s complexity by eliminating the irrelevant or less important features and allows for faster training of machine learning algorithms. A right and optimal subset of the selected features in a problem domain is capable to minimize the overfitting problem through simplifying and generalizing the model as well as increases the model’s accuracy [97]. Thus, “feature selection” [66, 99] is considered as one of the primary concepts in machine learning that greatly affects the effectiveness and efficiency of the target machine learning model. Chi-squared test, Analysis of variance (ANOVA) test, Pearson’s correlation coefficient, recursive feature elimination, are some popular techniques that can be used for feature selection.”; Page 11: “Pearson’s correlation is another method to understand a feature’s relation to the response variable and can be used for feature selection [99]. This method is also used for finding the association between the features in a dataset. The resulting value is [−1, 1] , where −1 means perfect negative correlation, +1 means perfect positive correlation, and 0 means that the two variables do not have a linear correlation. If two random variables represent X and Y, then the correlation coefficient between X and Y is defined”. The limitation includes using a subset of however many channels are most correlated, which suggests choosing any suitable number of channels under 16 channels as a subsection that are considered most correlated, which includes 6 channels).
Regarding claim 4, the Liao/Wang/Liu/Sarker/Hendler combination teaches the method of claim 3.
However, the Liao/Wang/Liu/Sarker/Hendler combination does not teach wherein each of the 16 channels comprises an electrode affixed to a crown of the subject’s head.
Hendler teaches wherein each of the 16 channels comprises an electrode affixed to a crown of the subject’s head (Fig. 1D shows the electrodes (166 and 164) connected to the crown of the subject’s head).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the location of the electrodes from Hendler into the Liao/Wang/Liu/Sarker/Hendler combination as the combination is silent on the location of the electrodes, and Hendler discloses a suitable location of the electrodes in an analogous device.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over the Liao/Wang/Liu/Sarker combination as applied to claim 1 above, and further in view of Razak (“Foot Plantar Pressure Measurement System: A Review”).
Regarding claim 6, the Liao/Walker/Liu/Sarker combination teaches the method of claim 1.
However, the Liao/Walker/Liu/Sarker combination does not teach wherein the foot of the subject is segmented into eight distinct regions, wherein each of the plurality of sensors on the foot of the subject is fixed to at least one of the eight distinct regions, wherein each of the plurality of sensors on the foot of the subject cannot be fixed to the same distinct region.
Razak discloses sensors to measure foot plantar pressure. Specifically, Razak teaches wherein the foot of the subject is segmented into eight distinct regions, wherein each of the plurality of sensors on the foot of the subject is fixed to at least one of the eight distinct regions, wherein each of the plurality of sensors on the foot of the subject cannot be fixed to the same distinct region (Fig. 19 shows eight sensors each in a distinct location). Liao and Razak are analogous art as they are in the same field of endeavor and are both related to devices used to measure pressure from a user’s foot.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the location of the sensors from Razak into the Liao/Walker/Liu/Sarker combination as the combination is silent on the locations of the sensors, and Razak discloses suitable locations in an analogous device.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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