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 .
Status of Claims
The present application is being examined under the claims filed on May 20, 2024.
Claims 1-18 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 29, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The following are in view of the drawings filed on February 8, 2024.
FIGS. 4a, 5a, 7, 8, 11, 13a, and 15 are objected to because they contain text that is not oriented in the same direction as the view [see 37 CFR 1.84(p)(1)].
FIGS. 3a and 3b are objected to because they contain text that is illegible [see 37 CFR 1.84(p)(1)].
FIGS. 3a, 3b, 6, 7, 8, 10, and 15 are objected to because they contain text placed upon hatched or shaded surfaces [see 37 CFR 1.84(p)(3)].
FIG. 15 is objected to because it contains text that crosses with lines [see 37 CFR 1.84(p)(3)].
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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Abstract
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc.
The abstract of the disclosure, filed February 8, 2024, is objected to because it repeats information given in the title and uses phrases which can be implied. “Devices, systems and methods to detect stress using kinematic data are disclosed herein.” A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Disclosure
The disclosure is objected to because various acronyms and/or initialisms are used throughout the disclosure, but are not spelled at upon their first use. For example, [0003] recites “IRB-approved study”, “FLS trainer”, and “a modified OSATS technique”. They should be spelled out at their first use for clarity of the record. Appropriate correction is required.
Claim Objections
Claims 2-18 are objected to because of the following informalities:
Claims 2-9 and 11-18 are missing a comma between the preamble and the body. For example, in claim 2, “[t]he method of 1 wherein the parameters comprise” should read, “[t]he method of claim 1, wherein the parameters comprise”.
Claims 3-9 and 12-18 are objected to for inheriting the deficiencies of their respective parent claim.
In claims 3-5 and 12-14, “the weights and biases” should read, “the weights and the biases”.
Claims 4-7 and 13-16 are objected to for inheriting the deficiencies of their respective parent claims.
In claim 5, “[t]he method of any one of claim 4” should read, “[t]he method of claim 4”.
Claims 6-7 are objected to for inheriting the deficiencies of claim 4.
In claims 7 and 16, “wherein steps (1)-(4) are repeated” should read, “wherein the steps (1)-(4) are repeated”.
In claim 10, “wherein the system if configured to:” should read, “wherein the system is configured to:”.
Claims 11-18 are objected to for inheriting the deficiencies of claim 10
Claim 14 recites dependency upon claim 3, “[t]he system of claim 3”. However, claim 3 does not recite a system. Instead, claim 3 recites a method, “[t]he method of claim 2”. Examiner is interpreting claim 14 as dependent upon claim 13 in view of the numerical ordering of the claims. Therefore, claim 14’s “[t]he system of claim 3” should read, “[t]he system of claim 13”.
Claims 15-16 are objected to for inheriting the deficiencies of claim 14.
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.
Claims 8-9 and 17-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 8 and 17:
Claim 8 recites “an input sequence of kinematic data”. Claim 1 recites “using kinematic data”. It is unclear if the kinematic data from claim 8 is referring to the kinematic data from claim 1 or to another separate kinematic data. Examiner is interpreting the kinematic data from claim 8 to be referring to the kinematic data from claim 1. Applicant is advised to amend “an input sequence of kinematic data” to “an input sequence of the kinematic data”.
Claim 17 corresponds to claim 8. Therefore, claim 17 in view of claim 10 is rejected for the same reason as stated above for claim 8.
Regarding Claims 9 and 18:
Claims 9 and 18 are rejected for inheriting the deficiencies of claim 8 and 17, respectively.
Claims 9 and 18 recite “the input sequence of kinematic data” and are rejected for the same reasons as discussed above with respect to claims 8 and 17. Applicant is advised to amend “the input sequence of kinematic data” to “the input sequence of the kinematic data”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory matter. The claims are directed to software per se and subsequently do not fall within at least one of the four categories of patent eligible subject matter.
Regarding Claim 10:
Step 1: The claim does not fall within at least one of the four categories of patent eligible subject matter. The claim recites:
A system for stress detection using kinematic data, wherein the system if configured to:
The claim is directed to a system, but does not recite any structure, and the following additional elements of an input, kinematic data, and a model are also all software without structure. Therefore, claim 10 is rejected under 35 U.S.C. as it is not drawn to eligible subject matter.
Regarding Claims 11-18:
Claims 11-18 are rejected to for inheriting the deficiencies of claim 10.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 are directed to a method [process].
Claims 10-18 are directed to a system [machine]. Claims 10-18 have been rejected as software per se. However, in view of compact prosecution, claims 10-18 are being interpreted as directed to one of the statutory categories.
Regarding Claim 1:
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining if the kinematic data from a user belong to a class of sub-movements associated with known signatures found to highly correlate with when the user is experiencing motor degradation due to high psychological stress versus a normal class of movements where the user is unaffected by stress
As drafted, under their broadest reasonable interpretation (BRI), in view of the specification, the above limitations cover concepts performed in the human mind (observation, evaluation, judgement, or opinion). Given a sufficiently small set of data, nothing in the claim prohibits this process from being performed mentally or with pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to integrate the judicial exception into a practical application.
inputting the kinematic data into a model
training the model by iteratively updating parameters of the model to minimize error between a prediction and a ground-truth label through backpropagation
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to amount to significantly more than the judicial exception.
inputting the kinematic data into a model
training the model by iteratively updating parameters of the model to minimize error between a prediction and a ground-truth label through backpropagation
Regarding Claim 2:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to integrate the judicial exception into a practical application.
wherein the parameters comprise weights and biases
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to amount to significantly more than the judicial exception.
wherein the parameters comprise weights and biases
Regarding Claim 3:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to integrate the judicial exception into a practical application.
wherein the weights and biases are in cells of a long-short- term-memory (LSTM) recurrent neural network
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to amount to significantly more than the judicial exception.
wherein the weights and biases are in cells of a long-short- term-memory (LSTM) recurrent neural network
Regarding Claim 4:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to integrate the judicial exception into a practical application.
wherein the weights and biases are in fully-connected layers
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to amount to significantly more than the judicial exception.
wherein the weights and biases are in fully-connected layers
Regarding Claim 5:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim. Additionally,
the following limitations are directed to the abstract idea of a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations) [see MPEP 2106.04(a)(2) I.C.].
wherein the backpropagation comprises:
(2) calculating the error between the prediction and the ground-truth label
(3) propagating the error backwards through the LSTM recurrent neural network and fully- connected layers
(4) updating the weights and biases of the model using optimization methods
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to integrate the judicial exception into a practical application.
(1) inputting the kinematic data to the model to make the prediction
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)], and therefore fails to amount to significantly more than the judicial exception.
(1) inputting the kinematic data to the model to make the prediction
Regarding Claim 6:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)].
further comprising repeating steps (1)-(4) multiple times
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional element is directed to performing repetitive calculations. The courts have recognized performing repetitive calculations as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.].
further comprising repeating steps (1)-(4) multiple times
Regarding Claim 7:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)].
wherein steps (1)-(4) are repeated until the error between the prediction and ground-truth label in minimized
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional element is directed to performing repetitive calculations. The courts have recognized performing repetitive calculations as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.].
wherein steps (1)-(4) are repeated until the error between the prediction and ground-truth label in minimized
Regarding Claim 8:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim. Additionally,
The following limitations are/remain directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein an importance is assigned to different time steps in an input sequence of kinematic data
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim. Additionally,
The following limitations are/remain directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
wherein an importance is assigned to different time steps in an input sequence of kinematic data
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claims 10-17:
Claims 10-17 correspond to claims 1-8. In particular, 10:1, 11:2, 12:3, 13:4, 14:5, 15:6, 16:7, 17:8.
Step 2A, Prong 1: Claims 10-17 recite the same abstract ideas as in claims 1-8.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 10-17 at this step mirror that of claims 1-8.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 10-17 at this step mirror that of claims 1-8.
Regarding Claim 18:
Step 2A, Prong 1: This claim recites the same abstract ideas as in the parent claim. Additionally,
The following limitations are/remain directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion).
to assign the importance to different time steps in the input sequence of kinematic data based on the relevance of the importance to a final classification task
including also detecting signatures associated with stress onset that enhance performance rather than degrade it
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shin et al. (“Attention-based Stress Detection Exploiting Non-contact Monitoring of Movement Patterns with IR-UWB Radar”, published: April 22, 2021), hereinafter Shin.
Regarding Claim 1:
Shin discloses:
A method for stress detection using kinematic data, wherein the method comprises:
Shin, p. 637, col. 2, “we propose a non-contact stress detection technique using an IR-UWB radar by analyzing body movement patterns. We note that the movement patterns of a person are closely relevant with one’s mental stress state.”
Shin discloses analyzing body movement patterns [using kinematic data] to perform non-contact stress detection [stress detection].
inputting the kinematic data into a model
Shin, p. 638, col. 1, FIG. 1:
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480
639
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In FIG. 1, Shin depicts inputting UWB (ultra-wide band) motion features [inputting the kinematic data] into their attention-based stress detection model [into a model].
determining if the kinematic data from a user belong to a class of sub-movements associated with known signatures found to highly correlate with when the user is experiencing motor degradation due to high physiological stress versus a normal class of movements where the user is unaffected by stress
As cited above, FIG. 1 depicts the attention-based stress detection model using the UWB motion features of a person [determining if the kinematic data from a user belong to] to output a classification of a stressed state versus [a class of sub-movements associated with known signatures found to highly correlate with when the user is experiencing motor degradation due to high physiological stress] versus a non-stressed state [versus a normal class of movements where the user is unaffected by stress].
training the model by iteratively updating parameters of the model to minimize error between a prediction and a ground-truth label through backpropagation
Shin, p. 639, col. 1, “
W
1
∈
R
d
×
d
,
b
1
∈
R
1
×
d
,
W
2
∈
R
d
×
1
,
b
2
∈
R
1
×
d
are trainable parameters and the one-dimensional convolution and the bidirectional LSTM also have their parameters to learn.”
p. 640, 3.3 Ground Truth, “Subjective scores collected in the self report session are used to decide whether each stage for each participant should be labelled as stressed or non-stressed.”
On page 639, Shin discloses trainable parameters for their attention-based stress detection model [training the model by iteratively updating parameters of the model]. Their stress detection model includes a bidirectional LSTM [minimize error between a prediction and a ground-truth label through backpropagation]. Page 640 further specifies how they obtained their ground-truth label data.
Regarding Claim 2:
As discussed above, Shin teaches [the] method of claim 1, and further discloses:
wherein the parameters comprise weights and biases
Shin, p. 639, col. 1, “
W
1
∈
R
d
×
d
,
b
1
∈
R
1
×
d
,
W
2
∈
R
d
×
1
,
b
2
∈
R
1
×
d
are trainable parameters and the one-dimensional convolution and the bidirectional LSTM also have their parameters to learn.”
Shin discloses the trainable parameters includes weights and biases:
W
1
,
b
1
,
W
2
,
b
2
[the parameters comprise weights and biases].
Regarding Claim 3:
As discussed above, Shin teaches [the] method of claim 2, and further discloses:
wherein the weights and biases are in cells of a long-short-term-memory (LSTM) recurrent neural network
Shin, p. 639, col. 1, “
W
1
∈
R
d
×
d
,
b
1
∈
R
1
×
d
,
W
2
∈
R
d
×
1
,
b
2
∈
R
1
×
d
are trainable parameters and the one-dimensional convolution and the bidirectional LSTM also have their parameters to learn.”
Shin discloses weights and biases and a bidirectional LSTM [the weights and biases are in cells of a long-short-term-memory (LSTM) recurrent neural network].
Regarding Claim 4:
As discussed above, Shin teaches [the] method of claim 3, and further discloses:
wherein the weights are biases are in fully-connected layers
Shin, p. 639, col. 2, 2.3.3 Attention Layer, “Attention mechanism [8] can improve the performance by capturing important information from the latent hidden vector created by the bidirectional LSTM layer. As shown in Eq. (3), we multiply a weight matrix
W
1
and add a bias
b
1
to
h
f
⊕
h
b
. After that, we use a softmax function to produce the attention vector denoted a. This attention vector is element-wise multiplied with
h
f
⊕
h
b
again, and only its meaningful hidden elements will survive. Finally, we have one more fully-connected layer to produce a logit value, followed by a sigmoid function to produce the final prediction in [0,1].”
Shin discloses an attention mechanism from their reference [8] which is a fully-connected model, and Shin discloses using the weights and biases with their attention mechanism [the weights are biases are in fully-connected layers].
Regarding Claim 5:
As discussed above, Shin teaches [the] method of any one of claim 4, and further discloses:
wherein the backpropagation comprises:
(1) inputting the kinematic data to the model to make the prediction
(2) calculating the error between the prediction and the ground-truth label
(3) propagating the error backwards through the LSTM recurrent neural network and fully-connected layers
(4) updating the weights and biases of the model using optimization methods
Shin, p. 639, col. 1, FIG. 2:
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687
637
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p. 6.39, col. 2, “Bidirectional LSTMs [7] can exploit both the preceding and succeeding contexts and can encode the temporal dependency of the feature maps created by the one-dimensional convolutional layer.”
In FIG. 2, Shin depicts inputting the UWB motion features into their attention-based stress detection model which includes the bidirectional LSTM which includes backpropagation through time. Backpropagation through time for LSTMs involve at every step, generating a prediction [inputting the kinematic data to the model to make a prediction], and a corresponding ground truth is provided to calculate the error [calculating the error between the prediction and the ground truth-label]. As disclosed by the bidirectional LSTM, Shin discloses backpropagation through time, and backpropagation through time is the training algorithm used to update the LSTM [propagating the error backwards through the LSTM recurrent neural network and fully-connected layers…updating the weights and biases of the model using optimization methods].
Regarding Claim 6:
As discussed above, Shin teaches [the] method of claim 5, and further discloses:
further comprising repeating steps (1)-(4) multiple times
As cited above in claim 5, Shin depicts FIG. 2 including the bidirectional LSTM, which includes backpropagation through time. LSTMs process a sequence of steps, therefore, the bidirectional LSTM disclosed by Shin will repeat steps (1)-(4) multiple times for the sequence of data. As further seen in FIG. 2, the features involve data over time/sequence data [further comprising repeating steps (1)-(4) multiple times].
Regarding Claim 7:
As discussed above, Shin teaches [the] method of claim 6, and further discloses:
wherein steps (1)-(4) are repeated until the error between the prediction and the ground-truth label is minimized
As discussed above in claim 6, Shin discloses a bidirectional LSTM using backpropagation through time with input UWB motion feature data over time [steps (1)-(4) are repeated]. Also discussed above in claim 5, the backpropagation through time involves calculating the error between the prediction and the ground-truth label, the error or loss is then used by an optimization algorithm to adjust the weights and biases of the model by minimizing the error [repeated until the error between the prediction and the ground-truth label is minimized].
Regarding Claim 8:
As discussed above, Shin teaches [the] method of claim 2, and further discloses:
wherein an importance is assigned to different time steps in an input sequence of kinematic data
Shin, p. 638, 2.2.3 Spatiotemporal features, “Our proposed spatiotemporal features are extracted from the target motion matrix. Firstly, we consider spatial information for analyzing the behavioral patterns. Specifically, we focus on a location where a distinctive movement is observed during a certain period of time. For instance, we use the distance of the location from the radar as additional features, where we observe the maximum and the minimum signals. The distance where movements are most frequently observed is also utilized as our spatiotemporal feature. In addition, we categorize observed movements into 4 levels (no, weak, medium, strong) and count the number of movements for each level in the target motion matrix. As a result, a set of 7 spatiotemporal features are created for our non-contact stress detection.”
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Shin discloses spatiotemporal features [an input sequence of kinematic data]. In particular, as shown in Table l (on page 639), Shin discloses 7 spatiotemporal features to be used for analyzing behavioral patterns [an importance is assigned to different time steps].
Regarding Claim 9:
As discussed above, Shin teaches [the] method of claim 8, and further discloses:
wherein the importance is assigned to different time steps in the input sequence of kinematic data based on the relevance of the importance to a final classification task
Shin, p. 638, col. 2, “We use three types of features depicted in Table 1: Raw features (Fraw), Statistical features (Fstatistical), and Spatiotemporal features (Fspatiotemporal) for effectively capturing one’s stress-related movement patterns”
As cited and discussed above in claim 8, Shin discloses using the spatiotemporal features [wherein the importance is assigned to different time steps in the input sequence of kinematic data] to help analyze behavioral patterns for a person’s body movement [based on the relevance of the importance to a final classification task]. This is further backed on page 638 which discloses using the spatiotemporal features to capture stress-related movement patterns.
Regarding Claims 10-17:
Claims 10-17 correspond to claims 1-8 and are rejected for at least the same reasons as given in the rejections of claims 1-8. In particular, 10:1, 11:2, 12:3, 13:4, 14:5, 15:6, 16:7, 17:8.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Shin in view of Li et al. (“Eustress of Distress: An Empirical Study of Perceived Stress in Everyday College Life”), hereinafter Li.
Regarding Claim 18:
Claim 18 corresponds to claim 9 and is rejected for at least the same reasons as given in the rejection of claim 9, with the exception of the following limitations.
Regarding Claim :
As discussed above, Shin teaches [the] system of claim 17, but does not explicitly disclose:
including also detecting signatures associated with stress onset that enhance performance rather than degrade it
However, in the same field, analogous art Li teaches:
including also detecting signatures associated with stress onset that enhance performance rather than degrade it
Li, p. 1211, “we designed the experiment to investigate the possibility of using physiological and behavioral signal together to build an accurate classifier of eustress recognition.”
p. 1216, “Eustress Recognition In this study, we have several assumptions: 1) eustress is the ‘right’ amount of stress that improves performance [3]; 2) eustress associated with positive feeling. Therefore, we define eustress in twofold: Eustress is the combination of moderate stress with high performance, and eustress is the combination of moderate stress with high mood.”
Li teaches building a classifier for recognizing eustress [detecting signatures associated with stress onset that enhance performance rather than degrade it]. Eustress is disclosed by Li to be the amount of stress that improves performance.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Shin with Li to measure and categorize distinct positive stress from negative stress in order to ensure a more robust and enhanced performance. “Eustress is literally the ‘good stress’ that associated with positive feelings and health benefits. Previous studies focused on general stress, where the concept of eustress has been overlooked. This paper presents a novel approach towards stress recognition using data collected from wearable sensors, smartphones, and computers. The main goal is to determine if behavioral factors can help differentiate eustress from another kind of stress”, and “[a]nother dominating approach for understanding eustress was developed on the Yerkes-Dodson Law [3]. It suggests that stress is beneficial to performance until some optimal level is reached, after which performance will decline, which follow the inverted U shape diagram” (Li, p. 1209; 1210).
Conclusion
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/STEVEN PHUNG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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