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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 12, the claim recites “An apparatus for cross-modal association between wearable and structural vibration signal segments for indoor occupant sensing, comprising:
(a) a multimodal signal alignment module configured for receiving sensor inputs of different modalities as a combination of a structural vibration sensor associated with a physical structure, and a wearable sensor having wearable sensor inputs associated with a user;
(b) wherein a sampling rate and timestamps of the sensor inputs are aligned in said multimodal signal alignment module;
(c) wherein infrastructure events are detected and segmented as segment-level associated cross modalities between the vibration sensor inputs and the wearable sensor inputs within said multimodal signal alignment module;
(d) an association discovery temporal convolutional network (AD-TCN) module configured for determining an extent of shared context between signal segments from different modalities, comprising: an association score layer coupled to a plurality of temporal convolution network (TCN) blocks, with each of the plurality of TCN blocks coupled to a pointwise convolution layer which performs infrastructure signal prediction over a period of time and outputs wearable and vibration segment values;
(e) wherein said wearable and vibration segment values are utilized to predict the current time step value of the vibration segment, and train the convolution network model to determine association probability between signal segments from these two modalities based on the weights of the trained AD-TCN, wherein association probability reflect contributions of one signal segment for predicting the other signal segment; and
(f) a pairwise association determination module receives output from the AD-TCN and estimates association probabilities in response to determining association distance as a measurement of the association relationship which is then converted to a common measurement between multi-modal sensing, to which association thresholding is performed to generate a pairwise association output indicating whether there is sufficient cross-modal association between the structural vibration sensor associated with a physical structure, and the wearable sensor associated with a given user to consider both sensor inputs to be indicative of the same event.“
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (apparatus for claim 12).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations).
In claim 1, the steps identified in bold type are mathematical concepts, therefore, they are considered to be abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
a multimodal signal alignment module configured for receiving sensor inputs of different modalities as a combination of a structural vibration sensor associated with a physical structure, and a wearable sensor having wearable sensor inputs associated with a user; wherein a sampling rate and timestamps of the sensor inputs are aligned in said multimodal signal alignment module; wherein infrastructure events are detected and segmented as segment-level associated cross modalities between the vibration sensor inputs and the wearable sensor inputs within said multimodal signal alignment module; an association discovery temporal convolutional network (AD-TCN) module; an association score layer coupled to a plurality of temporal convolution network (TCN) blocks, with each of the plurality of TCN blocks coupled to a pointwise convolution layer which performs infrastructure signal prediction over a period of time and outputs wearable and vibration segment values; wherein said wearable and vibration segment values are utilized; and train the convolution network model; wherein association probability reflect contributions of one signal segment for predicting the other signal segment; a pairwise association determination module receives output from the AD-TCN; association distance is converted to a common measurement between multi-modal sensing, to which association thresholding is performed; and the wearable sensor associated with a given user to consider both sensor inputs to be indicative of the same event.
The additional elements “a multimodal signal alignment module configured for receiving sensor inputs of different modalities as a combination of a structural vibration sensor associated with a physical structure, and a wearable sensor having wearable sensor inputs associated with a user”; “wherein a sampling rate and timestamps of the sensor inputs are aligned in said multimodal signal alignment module”; “wherein infrastructure events are detected and segmented as segment-level associated cross modalities between the vibration sensor inputs and the wearable sensor inputs within said multimodal signal alignment module”; “an association discovery temporal convolutional network (AD-TCN) module”; “an association score layer coupled to a plurality of temporal convolution network (TCN) blocks, with each of the plurality of TCN blocks coupled to a pointwise convolution layer which performs infrastructure signal prediction over a period of time and outputs wearable and vibration segment values”; “wherein said wearable and vibration segment values are utilized”; “train the convolution network model”; “wherein association probability reflect contributions of one signal segment for predicting the other signal segment”; “a pairwise association determination module receives output from the AD-TCN”; “association distance is converted to a common measurement between multi-modal sensing, to which association thresholding is performed”; and “the wearable sensor associated with a given user to consider both sensor inputs to be indicative of the same event” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception.
The additional element “a pairwise association determination module” is not sufficient to integrate the abstract idea into a practical application because it is considered a generic computer element. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional.
The claim, therefore, is not patent eligible.
Independent claims 1 and 20 recite subject matter that are similar or analogous to that of claim 12, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 2-11 and 13-19 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner' s Note
Regarding Claims 1-20, the most pertinent prior arts are “Zhang US 10614299B2”; “Zhang et al. (A Signal Quality Assessment Metrics for Vibration-based Human Sensing Data Acquisition), ACM, 2019”; “Chen et al. (A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine), IEEE, Vol. 6, No. 2, April 2019”; “Lin et al. (Temporal Convolutional Attention Neural Networks for Time Series Forecasting), IEEE, 2021”; “Chung et al. (Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning), MDPI, 2019”.
As to claims 1, 12, and 20, the prior arts of record, alone or in combination, do not fairly teach or suggest “a multimodal signal alignment module configured for receiving sensor inputs of different modalities as a combination of a structural vibration sensor associated with a physical structure, and a wearable sensor having wearable sensor inputs associated with a user”;
“wherein a sampling rate and timestamps of the sensor inputs are aligned in said multimodal signal alignment module”;
“wherein infrastructure events are detected and segmented as segment-level associated cross modalities between the vibration sensor inputs and the wearable sensor inputs within said multimodal signal alignment module”;
“an association discovery temporal convolutional network (AD-TCN) module configured for determining an extent of shared context between signal segments from different modalities, comprising: an association score layer coupled to a plurality of temporal convolution network (TCN) blocks, with each of the plurality of TCN blocks coupled to a pointwise convolution layer which performs infrastructure signal prediction over a period of time and outputs wearable and vibration segment values”;
“wherein said wearable and vibration segment values are utilized to predict the current time step value of the vibration segment, and train the convolution network model to determine association probability between signal segments from these two modalities based on the weights of the trained AD-TCN, wherein association probability reflect contributions of one signal segment for predicting the other signal segment”; and
“a pairwise association determination module receives output from the AD-TCN and estimates association probabilities in response to determining association distance as a measurement of the association relationship which is then converted to a common measurement between multi-modal sensing, to which association thresholding is performed to generate a pairwise association output indicating whether there is sufficient cross-modal association between the structural vibration sensor associated with a physical structure, and the wearable sensor associated with a given user to consider both sensor inputs to be indicative of the same event” including all limitations as claimed.
Dependent claims 2-11 and 13-19 are also distinguished over the prior art for at least the same reason as claims 1 and 12.
Examiner notes, however, that claims 1-20 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Zou US20220124154” teaches “A system can include multiple WiFi-enabled commercial off the shelf (COTS) Internet of Things (IoT) devices disposed within an environment and configured to be a transmitter (TX) or a received (RX) to send or receive data over a WiFi radio frequency communication link. A server can be configured to receive and parse the CSI data transmitted from the RX, store the CSI data with a corresponding human identity label collected for training, train a human identification classifier using a Convex Clustered Concurrent Shapelet Learning (C3SL) method, and estimate an identification of a user based on the CSI data and the C3SL method. The server can be configured to receive and parse the CSI data transmitted from the RX, transfer the CSI data into real-time CSI frames, store the real-time CSI frames in a database, store the real-time CSI frames with a corresponding gesture label collected in an original environment, and estimate and identify the gesture performed by user using a trained target encoder and source classifier. label collected in an original environment, and estimate and identify the gesture performed by user using a trained target encoder and source classifier.”.
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/LAL CE MANG/Primary Examiner, Art Unit 2857
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