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 .
Status of Claims
The present application is being examined under the claims filed 06/08/2023.
Claims 1-13 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/08/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 1-13 are patentable under 35 U.S.C. 103. Rejections remain under 35 U.S.C. 101 and 35 U.S.C. 103 which must be addressed prior to allowance.
The following is a statement of reasons for the indication of allowable subject matter:
Examiner’s search revealed the references below. When viewed as a whole, the totality of independent claims 1 and 13 are non-obvious over the prior art found such that a person having ordinary skill in the art would not be think to create the claimed invention. Particularly, combining the wi-fi action recognition technologies with other methods of action recognition as claimed would require extensive redesign and experimentation based on the prior art available before the effective filing date.
Zhang et al. “Room zonal location and activity intensity recognition model for residential occupant using passive-infrared sensors and machine learning”
Zhang teaches dividing an activity space into regions for activity recognition and collecting data within each of the regions:
(page 1135 column 2 paragraph 1) “The floor area of the test field was marked as Zone A for the clear area and Zone B for the sofa area. Zones A and B were sub-divided equidistantly into 3 zones respectively, yielding 6 zones in total, see Figure 2.”
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Zhang further teaches generally using activity data from the sections for machine learning
(page 1133 abstract) “A PIR sensor array with 15 nodes was employed to monitor indoor occupant’s and cat’s behavior in a case residential building for 71 days. The output signals of PIR sensors varied with different locations and activity intensities. By analyzing the PIR data feature, models were established using six machine learning algorithms and two sets of data.”
Liu et al. “Human Activity Sensing with Wireless Signals: A Survey”
Liu teaches methods to use Wi-Fi signals for human activity sensing, and specifically convolutional neural networks.
(page 28 paragraph 1) “Convolutional neural network (CNN) is a typical kind of DNN, whose neurons in the neighbor layers are connected through the convolution kernel as an intermediary. CNN has the characteristic of limiting the number of parameters and minimum local structures. Zhang et al. [108] apply CNN to differentiate sit-up, push-up, and walk-out easily. It uses 1980 activities in training and achieves an accuracy of 82%. Sign-Fi [21] adopts nine-layer CNN for 150 gestures of sign language classification. The average recognition accuracy of SignFi is 86.66% for 7,500 instances of 150 sign gestures performed by five volunteers. In order to achieve the environment-independent human motion recognition, EI [77] collects the activities set of 40 subject-room pairs (about 1,200 in total) to train a CNN classifier for six human daily activities sensing (wiping the whiteboard, walking, moving a suitcase, rotating the chair, sitting, standing up and sitting down). Its classification accuracy is between 61–75%.”
Tang et al. “Human Behavior Recognition Based on WiFi Channel State Information”
Tang teaches using channel state information and channel impulse response for activity recognition
(page 1158 column 1 last paragraph) “In the wireless channel, the channel impulse response (CIR) is usually used to describe and evaluate the wireless channel. It expresses as the following formula (1) [10].”
(page 1158 column 2 paragraph 1) “The CSI reflects the basic information at the physical channel level during the signal (single subcarrier) propagation. It reflects the performance of the channel. A set of CSI values can be got from each received data packet, and each set of CSI represents the amplitude and phase of an orthogonal frequency division multiplexed subcarrier [11]:”
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(page 1160 column 1 paragraph 1) “The dataset [6] contains seven actions of walking, standing up, lying down, falling, sitting down, running, and picking up. Each row of CSI data for each activity initially obtained is arranged in the order of timestamps.”
Further art considered:
Koile et al. “Activity Zones for Context-Aware Computing” teaches dividing an activity into regions for activity classification
He et al. “WiFi Vision: Sensing, Recognition, and Detection With Commodity MIMO-OFDM WiFi” teaches using wifi and CSI for action recognition indoors
Isikdogan et al. “SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems” teaches layerwise freezing of neural networks
Saeed et al. “Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition” teaches using radio signal with CSI in different regions to classify activities (published before US filing date, after PCT filing date).
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.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitations:
a collection module… in claim 13 (paragraph 37) “a collection module, configured to divide an indoor activity space into a plurality of regions, collecting a CIR data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix, where M denotes a region number, N denotes a human activity type, and Z denotes the CIR data packet;”
a preprocessing module… in claim 13 (paragraph 38) “a preprocessing module, configured to preprocess the H (M, N, Z) matrix to obtain a preprocessed H (M, N, Z) matrix;”
a training sample acquisition module… in claim 13 (paragraph 39) “a training sample acquisition module, configured to extract features of the preprocessed H (M, N, Z) matrix to obtain a training sample of a CNN model;”
a CNN training module… in claim 13 (paragraph 40) “a CNN training module, configured to perform transfer learning on the CNN model using the training sample, so as to obtain a trained CNN model;”
a behavior recognition module… in claim 13 (paragraph 41) “a behavior recognition module, configured to obtain an indoor CIR amplitude value, input the CIR amplitude value into the trained CNN model, and output a human behavior”
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 13 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 13
Claim limitation “a collection module…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s written description does not provide any disclosure of structure corresponding to the claimed module, but instead merely reiterates the claim language verbatim with no mention of, for example, a computer to perform the functions.
Claim limitation “a preprocessing module…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s written description does not provide any disclosure of structure corresponding to the claimed module, but instead merely reiterates the claim language verbatim with no mention of, for example, a computer to perform the functions.
Claim limitation “a training sample acquisition module…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s written description does not provide any disclosure of structure corresponding to the claimed module, but instead merely reiterates the claim language verbatim with no mention of, for example, a computer to perform the functions.
Claim limitation “a CNN training module…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s written description does not provide any disclosure of structure corresponding to the claimed module, but instead merely reiterates the claim language verbatim with no mention of, for example, a computer to perform the functions.
Claim limitation “a behavior recognition module…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s written description does not provide any disclosure of structure corresponding to the claimed module, but instead merely reiterates the claim language verbatim with no mention of, for example, a computer to perform the functions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 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.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
An indoor passive human behavior recognition method, comprising the following steps: Step 1: dividing an indoor activity space into a plurality of regions — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to procedurally evaluating a 2D plane by dividing it into a grid and assigning numbers, which could be done in the human mind. For example, one could imagine a room that they are familiar with and mentally divide up the space as in the picture below:
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Step 2: preprocessing the H (M, N, Z) matrix to obtain a preprocessed H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating points of data to prepare it for further evaluation, for example calculating normalizations.
Step 3: extracting features of the preprocessed H (M, N, Z) matrix to obtain a training sample of a CNN (convolutional neural network) model — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data to determine, for example, a classification result of an activity represented in a data sample.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
collecting a CIR (channel impulse response) data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix, wherein M denotes a region number, N denotes a human activity type, and Z denotes the CIR data packet; — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 4: performing transfer learning on the CNN model using the training sample, so as to obtain a trained CNN model — This limitation is directed to mere instructions to apply a judicial exception. Using ordinary machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
and Step 5: obtaining an indoor CIR amplitude value, inputting the CIR amplitude value into the trained CNN model, and outputting a human behavior — This limitation is directed to mere instructions to apply a judicial exception. Using machine learning inference to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning inference is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
collecting a CIR (channel impulse response) data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix, wherein M denotes a region number, N denotes a human activity type, and Z denotes the CIR data packet; — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Step 4: performing transfer learning on the CNN model using the training sample, so as to obtain a trained CNN model — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
and Step 5: obtaining an indoor CIR amplitude value, inputting the CIR amplitude value into the trained CNN model, and outputting a human behavior —Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Regarding Claim 2
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which included an abstract idea (see rejection for claim 2). The claim recites the additional limitations:
Step 2A Prong 1:
wherein a calculation formula for the CIR is as follows:
H
i
=
H
i
e
j
∠
H
i
wherein
H
i
denotes channel state information of an i-th sub-carrier,
H
i
denotes an amplitude of the i-th sub-carrier,
∠
H
i
denotes a phase of the i-th sub-carrier, and j is an imaginary part of a complex number — This limitation is directed to the abstract idea of a mathematical process, and mathematical formulas or equations in particular (MPEP 2106.04(a)(2) I. B.). The claim describes the mathematical channel impulse response formula.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 3
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 3). The claim recites the additional limitations:
Step 2A Prong 1:
wherein an acquisition method for the region number is as follows: dividing the activity space into M regions with the same area and in a n×n distribution, and starting from the top left corner, numbering the regions in each row from left to right in turn — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to procedurally evaluating a 2D plane by dividing it into a grid and assigning numbers, which could be done in the human mind. For example, one could imagine a room that they are familiar with and mentally divide up the space as in the picture below:
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2
3
4
5
6
7
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Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 4
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
The indoor passive human behavior recognition method according to claim 1, wherein Step 2 comprises the following steps: filtering the CIR data packet of the H (M, N, Z) matrix using hampel, so as to obtain a filtered H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating a hampel filter of a matrix to obtain a new matrix.
interpolating the CIR data packet of the filtered H (M, N, Z) matrix to obtain an interpolated H (M, N, Z) matrix —This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of interpolating data (for example via linear interpolation).
preforming Kalman smoothing filtering on the CIR data packet of the interpolated H (M, N, Z) matrix to obtain a smoothed H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating a kalman smoothing filter of a matrix to obtain a new matrix.
performing wavelet transform on the CIR data packet of the smoothed H (M, N, Z) matrix to obtain a denoised H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating a wavelet transform on entries of a matrix.
and performing data dimension reduction processing on the CIR data packet of the denoised H (M, N, Z) matrix using PCA (Principal Component Analysis), so as to obtain a dimension-reduced H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating principal component analysis on a matrix.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 5
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein Step 3 comprises the following steps: clustering CIR amplitude values of various regions for various activities to obtain n major types — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to an opinion of a group from a list of groups that a numerical value belongs to.
dividing the M regions for various activities into n major types — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to an opinion of a group from a list of groups that a region of a room belongs to.
calculating MKMMD (Multiple Kernel Maximum Mean Discrepancy) values of the CIR amplitude values of various regions for each type of activity, and obtaining a number of a region corresponding to the minimum MKMMD value — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim explicitly describes the mathematical calculation of multiple kernel maximum mean discrepancy in words.
acquiring a number of a region corresponding to a human reflection path from the regions corresponding to the minimum MKMMD values in various types of activity according to the wireless sensing principle of a Fresnel zone — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to an observation of a reflection path related to regions and the Fresnel zone based on numerical values.
Step 2A Prong 2:
using a CIR amplitude value of the region corresponding to the human reflection path of each type of activity as a first training sample; and using CIR amplitude values corresponding to the remaining numbered regions for various activities as a second training sample — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the judicial exception to using amplitude values in a training (computer) environment.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
using a CIR amplitude value of the region corresponding to the human reflection path of each type of activity as a first training sample; and using CIR amplitude values corresponding to the remaining numbered regions for various activities as a second training sample — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 6
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
The indoor passive human behavior recognition method according to claim 1, wherein Step 4 comprises the following steps: training the CNN model using the first training sample, so as to obtain initial parameters of the CNN model — This limitation is directed to mere instructions to apply a judicial exception. Using generic machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
substituting the initial parameters of the CNN model into the CNN model, freezing parameters of a convolution layer and a pooling layer before a fully connected layer of the CNN model — This limitation is directed to insignificant application of data, which has been recognized by the courts (as per Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and then selecting a certain number of second training samples to form secondary training data to train the fully connected layer of the CNN model, thus obtaining a trained CNN model — This limitation is directed to mere instructions to apply a judicial exception. Using generic machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
The indoor passive human behavior recognition method according to claim 1, wherein Step 4 comprises the following steps: training the CNN model using the first training sample, so as to obtain initial parameters of the CNN model — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
substituting the initial parameters of the CNN model into the CNN model, freezing parameters of a convolution layer and a pooling layer before a fully connected layer of the CNN model — This limitation is recited in a merely generic manner and amounts to freezing layers of a neural network which is well-understood, routine, and conventional activity. A factual determination that this element is well-understood, routine, and conventional activity (see MPEP 2106.05(d) I.) is supported by Isikdogan et al. “SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems”, which recites that (figure 1 caption) “A high-level illustration of how the vertical freezing scheme in SemifreddoNets (right) differs from traditional layer-level parameter freezing approaches (left)”. Therefore, the additional element cannot amount to significantly more than the judicial exception under step 2B.
and then selecting a certain number of second training samples to form secondary training data to train the fully connected layer of the CNN model, thus obtaining a trained CNN model — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 7
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the CNN model comprises three convolution layers, a pooling layer is connected after each convolution layer, output ends of all pooling layers are connected with two fully connected layers after fusion calculation; a Dropout layer is connected after the last fully connected layer, and a softmax layer is connected after the Dropout layer — This limitation is directed to mere instructions to apply a judicial exception. Using a generic neural network archictecture to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the architecture is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the CNN model comprises three convolution layers, a pooling layer is connected after each convolution layer, output ends of all pooling layers are connected with two fully connected layers after fusion calculation; a Dropout layer is connected after the last fully connected layer, and a softmax layer is connected after the Dropout layer — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 8
Claim 8 is identical in scope to claim 7, except that claim 8 is dependent upon claim 2 instead of claim 1. Therefore, the same rejection and rationale applies to claim 8.
Regarding Claim 9
Claim 9 is identical in scope to claim 7, except that claim 9 is dependent upon claim 3 instead of claim 1. Therefore, the same rejection and rationale applies to claim 9.
Regarding Claim 10
Claim 10 is identical in scope to claim 7, except that claim 10 is dependent upon claim 4 instead of claim 1. Therefore, the same rejection and rationale applies to claim 10.
Regarding Claim 11
Claim 11 is identical in scope to claim 7, except that claim 11 is dependent upon claim 5 instead of claim 1. Therefore, the same rejection and rationale applies to claim 11.
Regarding Claim 12
Claim 12 is identical in scope to claim 7, except that claim 8 is dependent upon claim 6 instead of claim 1. Therefore, the same rejection and rationale applies to claim 12.
Regarding Claim 13
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
An indoor passive human behavior recognition device, comprising the following modules: a collection module, configured to divide an indoor activity space into a plurality of regions — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to procedurally evaluating a 2D plane by dividing it into a grid and assigning numbers, which could be done in the human mind. For example, one could imagine a room that they are familiar with and mentally divide up the space as in the picture below:
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a preprocessing module, configured to preprocess the H (M, N, Z) matrix to obtain a preprocessed H (M, N, Z) matrix — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating points of
a training sample acquisition module, configured to extract features of the preprocessed H (M, N, Z) matrix to obtain a training sample of a CNN model — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data to determine, for example, a classification result of an activity represented in a data sample.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
collecting a CIR data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix, wherein M denotes a region number, N denotes a human activity type, and Z denotes the CIR data packet — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
a CNN training module, configured to perform transfer learning on the CNN model using the training sample, so as to obtain a trained CNN model — This limitation is directed to mere instructions to apply a judicial exception. Using ordinary machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
and a behavior recognition module, configured to obtain an indoor CIR amplitude value, input the CIR amplitude value into the trained CNN model, and output a human behavior — This limitation is directed to mere instructions to apply a judicial exception. Using machine learning inference to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the machine learning inference is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
collecting a CIR data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix, wherein M denotes a region number, N denotes a human activity type, and Z denotes the CIR data packet — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
a CNN training module, configured to perform transfer learning on the CNN model using the training sample, so as to obtain a trained CNN model — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
and a behavior recognition module, configured to obtain an indoor CIR amplitude value, input the CIR amplitude value into the trained CNN model, and output a human behavior — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Conclusion
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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