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 .
Response to Amendment
The Amendments to the Claims filed 06/17/2025 have been entered. Claims 1, 5-24, and 26-30 are pending in the application. Claims 2-4 and 25 have been canceled. Applicant’s amendment to the Claims have overcome some of the 35 U.S.C. 101 rejections and 35 U.S.C. 103 rejections previously set forth in the Non-final rejection dated 09/12/2025.
Claim Objections
As noted above the claim objections previously presented have been overcome by amendment to the claims.
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 22-24, 26-28 and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an Abstract idea without significantly more.
With respect to claim 22 the limitation(s):
receiving a data set of signal measurements;
extracting a data set of timing information from the data set of signal measurements; and
training a machine learning model to predict, based on the data set of signal measurements and the data set of timing information, locations of stationary reflection points in a spatial environment and locations of non-stationary reflection points in the spatial environment, wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals.
The limitation(s) highlighted in (bold) is/are directed to an abstract idea and would fall within the “Mental Processes” groupings of abstract ideas. The above portion(s) of the claim(s) constitute(s) an abstract idea because:
The limitation(s) regarding “extracting a data set of timing information from the data set of signal measurements”, as drafted, is an act of observation and evaluation that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than reciting “a processor-implemented method,” nothing in the claim language precludes the Step(s) from practically being performed in the mind. For example, but for the “a processor-implemented method” language, “extracting” in the context of this claim encompasses the user manually extracting timing information.
Further, the limitation regarding “training a machine learning model to predict, based on the data set of signal measurements and the data set of timing information, locations of stationary reflection points in a spatial environment and locations of non-stationary reflection points in the spatial environment, wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals”, as drafted, falls within the “Mathematical Concepts” groupings of abstract ideas. This interpretation is supported by the recitation of a mathematical operation acting on one or more variables to determine another. In particular, the recited “training” explicitly recites performing mathematical calculations using “a probabilistic convolutional neural network.” It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
Further, referring to the MPEP 2106.04, the claim limitations are analogous to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Further, if a claim limitation, under its broadest reasonable interpretation, recites mathematical relationships, mathematical formulas or equations, and mathematical calculations, then it fall within the “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the non- abstract additional elements of the claims do not impose meaningful limits on practicing the abstract idea(s) recited in the preceding claim(s). In particular, the claims recited the additional elements of:
The limitation(s) regarding “detect objects based on wireless communication data” does/do not integrate the abstract idea into a practical application, because it is recited at such a high-level of generality that it is viewed as generally linking the use of the judicial exception to wireless communication. Generally linking the use of the judicial exception to a particular technological environment or field of use, fails to integrate the abstract ideas into a practical application, because the claim does not specify what practical application the claim is directed to.
The limitation(s) regarding “receiving a data set of signal measurements” does/do not integrate the abstract idea into a practical application because the claim does not specify what practical application the claim is directed to. Rather the limitation is recited at such a high-level of generality that it amounts to no more than adding insignificant extra- solution activity to the judicial exception, i.e. data gathering. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are regarded as data gathering steps necessary or routine to implement the abstract idea.
The limitation(s) regarding “a processor-implemented method” does/do not integrate the abstract idea into a practical application because the claim limitation is a generic computer component performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component.
As such Examiner does NOT view that the claims:
-Improve the functioning of a computer, or to any other technology or technical field;
-Apply the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b);
-Effect a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); or
-Apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception – see MPEP 2106.05(e) and Vanda Memo.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements amount to no more than mere instructions to apply the exception using a generic computer component, or are well-understood, routine, and conventional (WURC) data gathering functions.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “wireless communication” is/are seen as generally linking the use of the judicial exception to a particular technological environment. Linking a judicial exception to a technological environment cannot provide an inventive concept. Similarly, with regards to the additional element(s) of “receiving a data set of signal measurements” is/are viewed as insignificant extra-solution activity, such as mere data gathering in a conventional way and, therefore, does not provide an inventive concept. Similarly, with regards to the additional element(s) of “processor implemented” is/are view as a generic computer component performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Examiner further notes that such additional elements are viewed to be well- understood, routine, and conventional (WURC) as evidenced by: Ma et al. (Ma, Yongsen, Gang Zhou, and Shuangquan Wang. "WiFi sensing with channel state information: A survey." ACM Computing Surveys (CSUR) 52.3 (2019): 1-36.); Al Rawi et al. (US 20240027602 A1); Jeong et al. (US 20200196091 A1); Kennedy et al. (US 3373427 A); Wu et al. (US 20200271747 A1); Li et al. (Li, Shengjie, et al. "Ar-alarm: An adaptive and robust intrusion detection system leveraging csi from commodity wi-fi." Enhanced Quality of Life and Smart Living: 15th International Conference, ICOST 2017, Paris, France, August 29-31, 2017, Proceedings 15. Springer International Publishing, 2017.); Li et al. (Li, Yan, et al. "High-dimensional probabilistic fingerprinting in wireless sensor networks based on a multivariate Gaussian mixture model." Sensors 18.8 (2018): 2602.); and Xi et al. (Xi, Wei, et al. "Electronic frog eye: Counting crowd using WiFi." IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, 2014.)
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claims fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitation regarding “wireless communication,” “receiving a data set of signal measurements,” and “processor-implemented” can be viewed as a field of use, necessary data gathering, and any device and do not impose a meaningful limitation describing what problem is being remedied or solved.
Independent claim 30 are also held to be patent ineligible under 35 U.S.C. 101 because the additionally recited limitations fail to establish that the claims are not directed to an Abstract idea. Claim 30 recites the additional elements of:
The limitation(s) regarding “a memory” and “one or more processors” does/do not integrate the abstract idea into a practical application because the claim does not specify what practical application the claim is directed to. Rather the limitation is recited at such a high-level of generality that it amounts to a generic computer component performing the generic computer function of receiving, storing, and comparing data such that it amounts to no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 23-24, and 26-28 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additionally recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below: there are no additional element(s) in the dependent claims that adds a meaningful limitation to the abstract idea to make the claims significantly more than the judicial exception (abstract idea).
Claims 5 27-28 recite limitations regarding data gathering steps and insignificant application necessary or routine to implement the abstract idea and thus are not significantly more than the abstract idea and viewed to be well known routine and conventional as evidenced by the prior art shown above.
Claims 23-24, and 26 further limit the abstract idea with an abstract idea, such as an “Mental Processes” and “Mathematical Concepts”, and thus the claims are still directed to an abstract idea without significantly more.
Claims 1 and 29 are seen as applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As such, claims 1, 5-21, and 29 are not rejected under 35 USC 101.
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.
Claim(s) 22-23, 27-28, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (Ma, Yongsen, Gang Zhou, and Shuangquan Wang. "WiFi sensing with channel state information: A survey." ACM Computing Surveys (CSUR) 52.3 (2019): 1-36.) in view of Ohara et al. (Ohara, Kazuya, Takuya Maekawa, and Yasuyuki Matsushita. "Detecting state changes of indoor everyday objects using Wi-Fi channel state information." Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 1.3 (2017): 1-28.).
Regarding Claims 22 and 30. Ma teaches:
A system, comprising:
memory having executable instructions stored thereon (See Page 46:1: Wireless devices.); and
one or more processors (See Page 46:1: Wireless devices.) configured to execute the executable instructions to cause the system to:
receive a data set of signal measurements (See Pages 46:2 – 46:3: A time series of CSI measurements captures how wireless signals travel through surrounding objects and humans in time, frequency, and spatial domains, so it can be used for different wireless sensing applications. The CSI amplitude |H| and phase ∠H are impacted by the displacements and movements of the transmitter, receiver, and surrounding objects and humans. In other words, CSI captures the wireless characteristics of the nearby environment.);
extract a data set of timing information from the data set of signal measurements (See Fig. 7, Page 46:6 and Page 46:12: Angle-of-Arrival (AoA) and Time-of-Flight (ToF). AoAs and ToFs are two popular models for CSI-based tracking and localization. AoAs and ToFs are estimated by the phase shift or time delay from CSI measurements of an antenna array.); and
train a machine learning model to predict, based on the data set of signal measurements and the data set of timing information, locations of stationary reflection points in a spatial environment and locations of non- stationary reflection points in the spatial environment (See Fig. 8, Table 5, Table 6, Pages 46:12 – 46:14: If CSI similarity is less than 0.9, then the WiFi device is moving; if it is no less than 0.9 but less than 0.99, then it is environmental mobility; otherwise, it is static. These algorithms try to learn the mapping function using training samples of CSI measurements and the corresponding ground-truth labels. A Convolutional Neural Network (CNN) is a DNN with at least one of its layers involving convolution operations.).
Ma is silent as to the language of:
wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals.
Nevertheless Ohara teaches:
wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals (See Fig. 1, Fig. 3, Abstract, and Page 88:10 - Page 88:12: The decomposed data are then fed into our event classifier based on convolutional and recurrent neural networks to automatically extract features from CSI data, as it is difficult to intuitively design features to be extracted from the CSI data. We correct the neural network estimates by incorporating knowledge about the state transitions of an object using hidden Markov models. The convolutional layers are designed to extract meaningful features from the multichannel CSI data based on the findings of existing studies on CSI. The output layer using softmax function is used to output class probabilities (scores, to be exact) for the window at time t. As mentioned in Section 3.1, a vector consisting of class probabilities is generated by the DNN for each time window. We then employ HMMs to recognize events/states related to the object of interest using the probability vector sequence. We extract segments related to events of the object and use only the extracted segments to compute W tailored to the object.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals such as that of Ohara. Ohara teaches, “In addition, because we deal with time-series data, incorporating an HMM, which is a generative model that can be used to model events/states with temporal patterns can improve the performance and smoothness of the event/state recognition” (See Page 88:7 – Page 88:8). One of ordinary skill would have been motivated to modify Ma, because using a probabilistic convolutional neural network would have helped to improve the performance and smoothness of the event/state recognition, as recognized by Ohara.
Regarding Claims 23. Ma teaches:
the method of claim 22,
wherein the machine learning model comprises a Gaussian mixture model (See Page 46:13: Gaussian mean clustering is used to identify AoAs and ToFs from the same path but different packets.).
Regarding Claim 27. Ma teaches:
The method of claim 22,
wherein the locations of non-stationary reflection points in the spatial environment comprises locations of humans in motion in the spatial environment (See Table 1, Table 6, Page 46:1, and Page 46:5: A time series of CSI measurements captures how wireless signals travel through surrounding objects and humans in time, frequency, and spatial domains, so it can be used for different wireless sensing applications. The application scope of this survey includes but is not limited to human detection, motion detection, activity recognition, gesture recognition, human tracking, respiration estimation, human counting, and sleeping monitoring.).
Regarding Claim 28. Ma teaches:
The method of claim 22,
wherein the data set of signal measurements comprises a data set of channel state information (CSI) measurements from an environment different from a spatial environment in which the machine learning model is deployed (See Table 7 and Pages 46:25 – 46:26: Accuracy: 93%/80% (same/different testing environments). New environment.).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Ohara as applied to claim 23 above, and further in view of Li et al. (Li, Yan, et al. "High-dimensional probabilistic fingerprinting in wireless sensor networks based on a multivariate Gaussian mixture model." Sensors 18.8 (2018): 2602.), herein Yan Li.
Regarding Claim 24. Ma is silent as to the language of:
The method of claim 23,
wherein the Gaussian mixture model further comprises one of a Bayesian model trained based on received signal energy maximization or a posterior multivariate Gaussian mixture model trained based on received signal energy maximization.
Nevertheless Yan Li teaches:
wherein the Gaussian mixture model (See Abstract: a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell.) further comprises one of a Bayesian model trained based on received signal energy maximization or a posterior multivariate Gaussian mixture model trained based on received signal energy maximization (See Pages 8-9: In this paper, we implement the expectation maximisation (EM) algorithm for incomplete data parameter estimation, assuming the missing data mechanism under the missing at random (MAR) assumption. Calculate the responsibilities using the current parameters, which can be viewed as the posterior probability that the mth measurement Sm is from the kth component.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma wherein the Gaussian mixture model further comprises one of a Bayesian model trained based on received signal energy maximization or a posterior multivariate Gaussian mixture model trained based on received signal energy maximization such as that of Yan Li. Yan Li teaches, “While the incompleteness in the sensing data can lead to bias in the estimation of parameters, we have tried a number of approaches to overcome this problem. The most successful, reported here, invokes the expectation-maximisation (EM) imputation strategy. This method, widely used in statistics, provides a method to impute the missing data and simultaneously learn the parameters from the incomplete data” (See Page 2). One of ordinary skill would have been motivated to modify Ma, because training a Gaussian mixture model using maximization would have helped to estimate parameters using incomplete data, as recognized by Yan Li.
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Ohara as applied to claim 25 above, and further in view of Xi et al. (Xi, Wei, et al. "Electronic frog eye: Counting crowd using WiFi." IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, 2014.).
Regarding Claim 26. Ma is silent as to the language of:
The method of claim 22,
wherein the probabilistic convolutional neural network comprises one of:
one or more convolutional kernels with activation parameters associated with detection of a human entering an area, or
a probabilistic model configured to recognize humans entering an area and maintain a counter tracking a number of humans entering the area over time.
Nevertheless Xi teaches:
one or more convolutional kernels with activation parameters associated with detection of a human entering an area, or
a probabilistic model configured to recognize humans entering an area and maintain a counter tracking a number of humans entering the area over time (See Fig. 7, Abstract, and Page 6: A major challenge in our design of FCC is to find a stable monotonic function to characterize the relationship between the crowd number and various features of CSI. The monotonic relationship can be explicitly formulated by the Grey Verhulst Model, which is used for crowd counting without a labor-intensive site survey.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma with a probabilistic model configured to recognize humans entering an area and maintain a counter tracking a number of humans entering the area over time such as that of Xi. Xi teaches “Robust crowd counting is an important yet challenging task. It is of great interest in a number of potential applications, such as guided tour, crowd control, marketing research and analysis, etc.” (See Page 1). One of ordinary skill would have been motivated to modify Ma, because counting the number of humans entering an area would have helped to track a crowd for crowd control, as recognized by Xi.
Allowable Subject Matter
Claims 1, 5-21, and 29 are allowed.
The following is an examiner’s statement of reasons for allowance:
Claims 1 and 29 are allowed for disclosing:
taking one or more actions at the device based on determining the locations of stationary reflection points and non-stationary reflection points in the spatial environment, wherein taking the one or more actions comprises:
detecting, based on the determined locations of the non-stationary reflection points in the spatial environment, entry of an object into an area defined by a radius from the device;
based on detecting entry of the object into the area, generating an alert at the device indicating that the object entered the area; and
cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment.
The prior art Ma et al. (Ma, Yongsen, Gang Zhou, and Shuangquan Wang. "WiFi sensing with channel state information: A survey." ACM Computing Surveys (CSUR) 52.3 (2019): 1-36.) teaches using wireless channel state information to sense the location of static and moving objects (See Abstract). However, Ma either singularly or in combination, fails to anticipate or render obvious “cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment” in combination with all other limitations in the claim as claimed and defined by applicant.
The prior art Parker et al. (US 20230209377 A1) teaches detecting changes in an environment using wireless sensing (See para[0019] and para[0027]). However, Parker either singularly or in combination, fails to anticipate or render obvious “cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment” in combination with all other limitations in the claim as claimed and defined by applicant.
The prior art Kennedy et al. (US 3373427 A) teaches eliminating reflections from stationary objects using differences in a signals phase (See Col. 1, lines 10-50). However, Kennedy either singularly or in combination, fails to anticipate or render obvious “cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment” in combination with all other limitations in the claim as claimed and defined by applicant.
The prior art Cordie et al. (US 20230026131 A1) teaches removing a component from the sample data that corresponds to a reflection from a static object by subtracting a constant background (See para[0013]). However, Cordie either singularly or in combination, fails to anticipate or render obvious “cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment” in combination with all other limitations in the claim as claimed and defined by applicant.
Thus, these limitations, in combination with the other elements of the claims, are neither anticipated by nor obvious in view of the prior art of record and to one of ordinary skill in the art.
Claims 5-21 are allowed for depending from claim 1.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant's arguments filed 12/10/2025 have been fully considered but they are not persuasive.
Applicant argues that: In particular, Applicant submits that this practical application reflects an improvement to other technology or a technical field (namely, the field of object detection using machine learning). Specifically, any alleged abstract idea has been integrated into the practical application of determining and/or training a machine learning model to determine locations of stationary and non-stationary reflection points.
Applicant’s arguments with respect to the rejection of claims 22 and 30 under 35 USC 101 have been fully considered but are not persuasive. Referring to the MPEP 2106.04(a)(2), Step 2A: whether a claim is directed to a judicial exception, “Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception.” As described in further detail under the 35 USC 101 rejection above, the non-abstract additional elements of the independent claims are seen as either generally linking the use of the judicial exception to a particular technological environment or field of use; adding insignificant extra-solution activity to the judicial exception, i.e. necessary data gathering; or mere instructions to implement an abstract idea on a computer. Further, the non-abstract additional elements are not seen as integrating the claim as a whole into a practical application because the non- abstract additional elements are well understood, routine, and conventional activity that as shown by the recited references is widely prevalent or in common use in the relevant industry. As claims 22 and 30 recite a judicial exception and are not integrated into a practical application the 35 USC 101 rejection is maintained.
Applicant argues that: The USPTO explained that while Claim 3 of Example 48 recites abstract ideas, the claim is directed to an improvement to existing speech-to-text technology. Specifically, the USPTO noted how the claim recited that the deep neural network, which was trained on speech source separation, could be used to make "individual transcription of each separated speech signal possible." Id., p. 28 (citing M.P.E.P. §2106.05(a)). Accordingly, the USPTO said that this claim was patent eligible under Step 2A, Prong 2.
Applicant’s arguments with respect to the rejection of claims 22 and 30 under 35 USC 101 have been fully considered but are not persuasive. Referring to the MPEP 2106.04(II), for step 2A like the other steps in the eligibility analysis, evaluation of this step should be made after determining what the inventor has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation. Further referring to the July 2024 Subject Matter Eligibility Examples, These examples should be interpreted based on the fact patterns set forth below, as other fact patterns may have different eligibility outcomes.
As shown in further detail in the 35 U.S.C. 101 rejection above, the limitations regarding “train a machine learning model to predict” are seen as using mathematical calculations and are directed to “Mathematical Concepts”. Further, the non-abstract additional elements of the independent claims are seen as either generally linking the use of the judicial exception to a particular technological environment or field of use; adding insignificant extra-solution activity to the judicial exception, i.e. necessary data gathering; or mere instructions to implement an abstract idea on a computer. As claim interpretation is based on a case-by-case basis and claims 22 and 30 recite a judicial exception and are not integrated into a practical application the 35 USC 101 rejection is maintained.
Applicant argues that: Second, and similarly, the additional elements are not insignificant extra-solution activity because they are important to achieving the intended improvements. That is, without the additional elements the claimed methods are non-functional. The claimed methods will not achieve the intended result without the additional elements. Thus, the additional elements are not "insignificant."
Applicant’s arguments with respect to the rejection of claims 22 and 30 under 35 USC 101 have been fully considered but are not persuasive. Referring to the MPEP 2106.05(g), When determining whether an additional element is insignificant extra-solution activity, examiners may consider the following: Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Claims 22 and 30 recite the non-abstract additional element “receive a data set of signal measurements”. The recited additional element is seen as is insignificant extra-solution activity, because the additional element is amounts to necessary data gathering. The test for whether an additional element is directed insignificant extra-solution activity is not based on whether the additional element is unimportant to the claim.
As claims 22 and 30 recite a judicial exception and are not integrated into a practical application the 35 USC 101 rejection is maintained.
Applicant argues that: However, Ohara describes a convolutional neural network configured to generate "[output] class probabilities for 'open,''close,''opened,' and 'closed."' Ohara at Section 3.31. That is, the convolutional neural network of Ohara generates probabilities not "locations of stationary reflection points and non-stationary reflection points." Thus, because output class probabilities are not equivalent to "locations of [] reflection points,".
Applicant’s arguments with respect to the rejection of claims 22 and 30 under 35 USC 103 have been fully considered but are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, during patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005).
As shown in further detail in the 35 U.S.C. 103 rejection above, Ohara teaches, “Note that we attempt to estimate an event/state of an indoor object at each time slice. For example, a door can have “open” and “close” events and “opened” and ”closed” states. Therefore, we estimate which state or event the door is in at each time slice” (See Page 88:7).
Claims 22 and 30 recite “wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals.” In view of the state of the art, the examiner understands a broadest reasonable interpretation of “locations of the stationary reflection points and non-stationary reflection points” to include the state of an object. Applicant’s specification does not rebut the presumption that the term “locations of the stationary reflection points and non-stationary reflection points” is to be given its broadest reasonable interpretation by clearly setting forth a different definition of the term. Ohara discloses a broadest reasonable interpretation of the recited limitation, because Ohara discloses determining a state of an object using a probabilistic convolutional neural network.
Claims 22 and 30 recite “wherein the machine learning model comprises a probabilistic convolutional neural network configured to predict locations of the stationary reflection points and non-stationary reflection points based on temporal and spatial segmentation of measured signals.” In view of the state of the art, the examiner understands a broadest reasonable interpretation of “configured to” to include capable of.
Ohara teaches, “The authors employ CSI magnitudes to form location clusters based on the k-means algorithm using training data collected varying the position of a person in the room, and compare test CSI data with the constructed cluster to locate a person. E-eyes [46] achieves device-free location-oriented activity recognition using Wi-Fi CSI. The system predicts a use’s activities such as cooking and eating based on the estimated indoor locations of the user. To construct activity/location fingerprints, the system employs a clustering algorithm to identify similar instances of activities” (See Page 88:6).
Ohara discloses a broadest reasonable interpretation of the recited limitation, because Ohara discloses a probabilistic convolutional neural network that is capable of being used to determine the location of a person in a room. Accordingly, applicant’s arguments regarding the recited limitation are not persuasive and the rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARTER W FERRELL whose telephone number is (571)272-0551. The examiner can normally be reached Monday - Friday 10 am - 8 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T. Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARTER W FERRELL/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857