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 .
Claim Objections
Claims 1 and 13 are objected to because of the following informalities:
In claim 1; “from wireless signal transmitted and received” should be changed to “from a wireless signal transmitted and received” for grammar.
In claim 13; “from wireless signal transmitted and received” should be changed to “from a wireless signal transmitted and received” for grammar.
Appropriate correction is required.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
A collection unit in claim 13.
A preprocessing unit in claim 13
An estimation unit in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the published 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 limitation:
A collection unit is described as a computer processor, a recording medium, and a computer program, or may be implemented by a field programmable gate array in paragraph 27.
A preprocessing unit is described as a computer processor, a recording medium, and a computer program, or may be implemented by a field programmable gate array in paragraph 27.
An estimation unit is described as a computer processor, a recording medium, and a computer program, or may be implemented by a field programmable gate array in paragraph 27.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim sets forth that “VO2 of the user" is estimated by inputting user-specific characteristic data that is generated according to a change in the wireless signal into a machine learning model. This is understood to be a computer-implemented functional limitation which requires disclosure of the underlying algorithm(s) for obtaining the result to comply with the written description requirement. See MPEP § 2161.01(1). While the specification provides literal support for generating user-specific characteristic data and estimating VO2 of a user as in [0005-0014], [0043-0047], there is no description as to what specific wireless signal data is collected, other than that amplitude and standard deviation as a filter to select certain subcarriers, how that it is converted into user-specific data, and what the machine learning does to determine VO2. The specification essentially describes a black box where some variables are put in and a desired output is generated without a proper description of how the output is generated. For this reason, Applicant has failed to comply with the written description requirement for this computer-implemented function, and the claim is rejected under 112a.
Regarding claim 13, the claim sets forth that “VO2 of the user" is estimated by inputting user-specific characteristic data that is generated according to a change in the wireless signal into a machine learning model. This is understood to be a computer-implemented functional limitation which requires disclosure of the underlying algorithm(s) for obtaining the result to comply with the written description requirement. See MPEP § 2161.01(1). While the specification provides literal support for generating user-specific characteristic data and estimating VO2 of a user as in [0005-0014], [0043-0047], there is no description as to what specific wireless signal data is collected, other than that amplitude and standard deviation are used as a filter to select certain subcarriers, how that it is converted into user-specific data, and what the machine learning does to determine VO2. The specification essentially describes a black box where some variables are put in and a desired output is generated without a proper description of how the output is generated. For this reason, Applicant has failed to comply with the written description requirement for this computer-implemented function, and the claim is rejected under 112a.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 13, the claims recite “a transmitter and a receiver around a user”. The term “around” is a relative term which renders the claim indefinite. The term “around” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, it is unclear how near something must be to be “around” a user. Clarification is required. For examination purposes, a transmitter and receiver communicating information about a user will be interpreted as being “around a user”.
Regarding claims 1 and 13, the claims recite “a transmitter and a receiver around a user”. It is unclear if the term “around” is modifying both a transmitter and a receiver or just a receiver. Clarification is required.
Regarding claim 11, the claim recites the limitation “the channel state information is collected before the channel state information is collected”. This limitation appears to be contradictory. Clarification is required. For examination purposes, a reference disclosing collecting data to train the machine learning model will be interpreted as meeting the limitations of this claim.
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.
Claim 12 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of “a computer-readable storage medium” reads on signals per se. To overcome this rejection, the Examiner recommends amending claim 12 to recite a “non-transitory” medium.
Claims 1-13 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception of an abstract idea in the form of a mental process without significantly more.
Claim 1 is directed to method of estimating VO2, claim 12 is directed towards a computer-readable recording medium containing a program to perform the steps of claim 1, and claim 13 is directed to a device comprising a unit to execute processing steps that are substantially similar to the steps of claim 1.
Claims 1 and 12-13 are considered to be directed towards an abstract idea because they recite a mental process of generating user-specific data based on a wireless signal using that user-specific data to estimate VO2 of a user. Specifically, the limitation of generating user-specific data and estimating VO2 of a user is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “mental processes” grouping of abstract ideas. Examples of this type of concept include diagnosing an abnormal condition by performing clinical tests and analyzing the results, In re Grams, 888 F.2d 835, 840, 12 USPQ2d 1824, 1828 (Fed. Cir. 1989); see CyberSource, 654 F.3d at 1372 n.2, 99 USPQ2d at 1695 n.2 (describing the abstract idea in Grams), and collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351, 119 USPQ2d 1739, 1739 (Fed. Cir. 2016). See MPEP § 2106.04(a)(2). III. A-B.
The judicial exceptions enumerated above (i.e., mathematical formula, mental process, and law of nature) are not integrated into a practical application. Specifically, the additional limitations of claims 12-13 directed towards processing circuitry or use thereof comprise no more than instructions to implement the judicial exceptions on a computer or merely use a computer as a tool to perform the judicial exceptions. The additional limitations of claims 1 and 12-13 directed towards a machine learning model comprise no more than instructions to implement the judicial exceptions on a computer or merely use a computer as a tool to perform the judicial exceptions. The additional limitations of claims 1 and 12-13 directed towards collecting wireless signal are merely an extra-solution activity of gathering data. Consequently, these additional elements do not integrate the judicial exceptions into a practical application because they do not impose any meaningful limits on practicing the judicial exceptions.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. For example, as explained above, the additional limitations of claims 12-13 directed towards processing circuitry or use thereof comprise no more than instructions to implement the judicial exceptions on a computer, or merely use a computer as a tool to perform the judicial exceptions. Mere instructions to apply an exception using generic computer components do not add significantly more to the judicial exceptions. The additional limitations of claims 1 and 12-13 directed towards a machine learning model comprise no more than instructions to implement the judicial exceptions on a computer or merely use a computer as a tool to perform the judicial exceptions. Mere instructions to apply an exception using generic computer components do not add significantly more to the judicial exceptions. The additional limitations of claims 1 and 12-13 directed towards collecting wireless signal are merely an extra-solution activity of gathering data. Mere instructions to apply an exception using generic computer components do not add significantly more to the judicial exceptions. Consequently, these additional elements do not add significantly more to the judicial exceptions.
Turning to the dependent claims:
Claims 2-4 and 11 recite additional elements directed towards further details of the abstract idea, specifying particular calculations or variables to be used to implement the abstract idea of generating user-specific data and VO2 data. These elements are not integrated into a practical application and are insufficient to amount to “significantly more” than the abstract idea because they too are directed towards the abstract idea.
Claims 5-10 recite additional elements directed towards further details of the extra-solution activity of data gathering, specifying particular details of the data collection process. These elements are not integrated into a practical application and are insufficient to amount to “significantly more” than the abstract idea because they amount to no more than mere extra-solution activity. Mere extra-solution activity does not add significantly more to the judicial exceptions.
The limitations of the dependent claims do not improve a computer or another technology or technical field. The limitations do not apply or use the judicial exceptions to affect a particular treatment or prophylaxis for a disease or medical condition. The limitations do not apply or use the judicial exceptions with, or by use of, a particular machine. The limitations do not affect a transformation or reduction of a particular article to a different state or thing. In addition, the claims do not include other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, when considered separately and in combination, the additional limitations of the dependent claims do not add significantly more (also known as an “inventive concept”) to the judicial exceptions.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-8, and 11-13 is rejected under 35 U.S.C. 103 as being unpatentable over Zebian (US20230255509) and Sheth (WO2022254462).
Regarding claim 1, Zebian discloses in Figure 1 a method of estimating VO2 (Zebian, Para 42; “By calculating oxygen consumption of the body of the user, the system 100 is capable of accurately calculate metabolic rate of the user.”) (Zebian, Para 1; “The present disclosure is directed to a system and method for more accurate calculation of metabolic rate of a user based on quantifying the boundary properties of the user's lung”), the method comprising:
transmit a wireless signal between a transmitter and a receiver around a user (Zebian, Para 49; “the plurality of sensors 102 may be integrated by the controller 104 inside a portable device, e.g., a smart watch. In this condition, the metabolic rate of the user is measured by the portable device and transmitted to the monitoring device 108 over the network 110. The monitoring device 108 may include a display to notify the user about the metabolic rate received from the portable device. In various embodiments, the metabolic rate may be displayed in energy burning unit such as calorie over time”) (Zebian, Para 19; “As the wearable sensors on the user collect and transmit data, either wirelessly or through a direct connection”);
generating user-specific characteristic data indicating characteristics of change in the wireless signal around the user according to a change in a body of the user from the collected channel state information (Zebian, Para 34; “The lung oxygen concentration sensor 204 may measure the breathing volume of the user by indication of the rib cage deformation of the user's body”) (Zebian, Para 40-41; “he blood oxygen saturation sensor 202 operates simultaneously with the heartrate sensor 206, such as being housed in a same wearable device, like a smart watch. In this condition, the controller 104 receives indicative data from the oxygen saturation sensor 202 and the heartrate sensor 206 corresponding to a real time condition of the user. The controller 104 is configured to calculate an oxygen concentration of blood for the user, based on the data received simultaneously from the oxygen saturation sensor 202 and the heartrate sensor 206. A pulse oximeter may operate as a combination of the oxygen saturation sensor 202 and the heartrate sensor 206”); and
estimating VO2 of the user based on an output of a machine learning model by inputting the user-specific characteristic data to a pre-learned machine learning model (Zebian, Para 44; “the different recorded data may be used to train a Machine Learning system, where the Machin Learning system may accurately estimate the metabolic rate of the user based on the measurements of the term “O2 blood” and the term “O2 lung” of the equation in different conditions. The Machin Learning system may generate a first indicative data of the metabolic rate based on the measured “O2 blood” and “O2 lung” when the user is doing an exercise such as biking, while generating a second indicative data of the metabolic rate based on the same measured “O2 blood” and “O2 lung” when the user is doing another exercise such as hiking”) (Zebian, Para 43; “After calibrating the system 100, the gas exchange analyzer 106 can be decoupled from the controller 104 and the term “J” is calculated based on the real time measurement of the term “O2 blood” and the term “O2 lung” of the equation with the controller”) (Zebian, Para 43; “After calibrating the system 100, the gas exchange analyzer 106 can be decoupled from the controller 104 and the term “J” is calculated based on the real time measurement of the term “O2 blood” and the term “O2 lung” of the equation with the controller”) (Zebian, Para 30; “A simple mathematical equation can give us the result of oxygen consumed by the body: O2 consumed=volume flowrate of the blood x hemoglobin concentration per blood volume×(SpO2−SvO2)”) (Zebian, Para 42; “For calculating oxygen consumption of the body, breathing volume of the user can be measured that indicates oxygen concentration in lung of the user. The lung oxygen concentration sensor 204 may operate simultaneously with the oxygen saturation sensor 202 and the heartrate sensor 206. In this condition, the lung oxygen concentration sensor 204 measures and transmits indicative data of the oxygen concentration in lung of the user to the controller 104. The controller 104 may calculate the oxygen consumption of the body based on the received data simultaneously from the lung oxygen concentration sensor 204, the oxygen saturation sensor 202, and the heartrate sensor 206.”).
Zebian does not clearly and explicitly disclose collecting channel state information indicating a wireless channel state.
In an analogous wireless monitoring device field of endeavor Sheth discloses collecting channel state information indicating a wireless channel state (Sheth, Para 18; “The processing unit is configured to perform and use at least one dielectric characterization by initiating at least one Wi-Fi station (STA) corresponding to the at least one Wi-Fi transmitter to generate a request in a loop to Wi-Fi Access Point (AP) corresponding to at least one Wi-Fi receiver, initiating the Access Point (AP) to print the Channel State Information data (CSI Data), reading and storing the CSI Data for a plurality of requests made by STA to AP over a serial port, computing the amplitude for each sub-carrier from the CSI Data, obtain the amplitude timeseries data for the plurality of instances for the sub-carrier, applying decision filters to remove the outliers in the Amplitude timeseries, tagging the obtained amplitude timeseries by a reference blood glucose value, capturing and storing a plurality of timeseries as training data, performing training of a real-time sample value by the machine learning module, predicting and displaying a blood glucose level on the output display.”) (Sheth, Para 37-38; discussing this further).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian to include collecting channel state information indicating a wireless channel state in order to increase efficiency by filtering out data and in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Regarding claim 2, Zebian as modified by Sheth above discloses all of the limitations of claim 1 as discussed above.
Zebian further discloses wherein in the generating of the user-specific characteristic data, the user-specific characteristic data indicating at least one of amplitude change characteristics and phase change characteristics of the wireless signal around the user according to a physical change of the user is generated from the collected channel state information (Zebian, Para 34; “For instance, the lung oxygen concentration sensor 204 may include […] a resistive or pressure sensor […]The pressure sensor may be a capacitive sensor that produce a voltage change based on the expansion of the chest during the breathing. Thus, indicative data can be a voltage variation to be transmitted to the controller 104 by the lung oxygen concentration sensor 204. The controller 104 may be configured to perform signal processing on the received data from the lung oxygen concentration sensor 204, to calculate the breathing volume of the user based on the voltage variation”) (Zebian, Para 19; “As the wearable sensors on the user collect and transmit data, either wirelessly or through a direct connection”).
Regarding claim 3, Zebian as modified by Sheth above discloses all of the limitations of claim 2 as discussed above.
Zebian further discloses wherein the user-specific characteristic data is generated based on multiple amplitude values of the wireless signal around the user (Zebian, Para 34; “For instance, the lung oxygen concentration sensor 204 may include […] a resistive or pressure sensor […]The pressure sensor may be a capacitive sensor that produce a voltage change based on the expansion of the chest during the breathing. Thus, indicative data can be a voltage variation to be transmitted to the controller 104 by the lung oxygen concentration sensor 204. The controller 104 may be configured to perform signal processing on the received data from the lung oxygen concentration sensor 204, to calculate the breathing volume of the user based on the voltage variation”) (Zebian, Para 19; “As the wearable sensors on the user collect and transmit data, either wirelessly or through a direct connection”).
Regarding claim 5, Zebian as modified by Sheth above discloses all of the limitations of claim 1 as discussed above.
Zebian does not clearly and explicitly disclose wherein in the collecting of the channel state information, the channel state information is collected by periodically receiving multiple Wi-Fi packets from the receiver, and each of the multiple Wi-Fi packets received from the receiver includes channel state information on the receiver.
Sheth further discloses wherein channel state information is collected by periodically receiving multiple Wi-Fi packets from a receiver, and each of the multiple Wi-Fi packets received from the receiver includes channel state information on the receiver (Sheth, Para 37-38; “According to one of the embodiment of the present invention the channel estimation signal response transmitted from Wi-Fi transmitter (10a) to Wi-Fi receiver (10b) while transferring a data packet are analyzed for the dielectric properties of the sample (hand) kept between Wi-Fi transmitter (10a) and Wi-Fi receiver (10b). […] The channel state information is captured and the amplitude values for respective sub- carriers is computed. To reduce the dimension of the dataset some key frequency data is taken. The Model is trained using machine learning module such as but not limited to ML.Net Fast Forest Regression. Using the reduced dataset, the Model is trained for different glucose readings during the day and 3 subjects. Using the conventional glucose meter to compare the readings, the accuracy obtained is around 90%. Further, real patient data is to be collected and the machine learning approach is used to train the system based on the data values collected / experimented. […] Generally, in practice signals propagating between source and destination are affected due to physical properties of wireless medium. Further, resource allocation at physical layer is done based on RSSI as well as CSI values obtained from Wi-Fi modules. RSSI provide information on wireless channel properties and packet delivery status.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein in the collecting of the channel state information, the channel state information is collected by periodically receiving multiple Wi-Fi packets from the receiver, and each of the multiple Wi-Fi packets received from the receiver includes channel state information on the receiver in order to increase efficiency by filtering out data and in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Regarding claim 6, Zebian as modified by Sheth above discloses all of the limitations of claim 5 as discussed above.
Zebian does not clearly and explicitly disclose extracting the multiple Wi-Fi packets including the channel state information indicating the wireless channel state between the transmitter and the receiver from the received multiple Wi-Fi packets with reference to MAC addresses of the multiple Wi-Fi packets received from the receiver.
Sheth further discloses extracting multiple Wi-Fi packets including channel state information indicating the wireless channel state between a transmitter and a receiver from received multiple Wi-Fi packets with reference to MAC addresses of the multiple Wi-Fi packets received from the receiver (Sheth, Para 44; “In an implementation according to one of the embodiments the device (100) and method thereof of the present invention sends additional header information per CSI frame such as MAC address, RSSI, and other metadata along with the channel state information for all 64 subcarriers resulting in each frame of size s = lkB = 8kbit”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian to include extracting the multiple Wi-Fi packets including the channel state information indicating the wireless channel state between the transmitter and the receiver from the received multiple Wi-Fi packets with reference to MAC addresses of the multiple Wi-Fi packets received from the receiver in order to increase throughput while retaining consistency as taught by Sheth (Sheth, Para 44).
Regarding claim 7, Zebian as modified by Sheth above discloses all of the limitations of claim 6 as discussed above.
Zebian does not clearly and explicitly disclose wherein the generating of the user-specific characteristic data includes reading multiple channel state information (CSI) values for each subcarrier from the extracted multiple Wi-Fi packets, and calculating multiple amplitude values for each subcarrier from the read multiple CSI values for each subcarrier, and the user-specific characteristic data is generated based on the multiple amplitude values calculated for each subcarrier.
Sheth further discloses reading multiple channel state information (CSI) values for each subcarrier from extracted multiple Wi-Fi packets, and calculating multiple amplitude values for each subcarrier from the read multiple CSI values for each subcarrier, and generated data based on the multiple amplitude values calculated for each subcarrier (Sheth, Para 37-38; “According to one of the embodiment of the present invention the channel estimation signal response transmitted from Wi-Fi transmitter (10a) to Wi-Fi receiver (10b) while transferring a data packet are analyzed for the dielectric properties of the sample (hand) kept between Wi-Fi transmitter (10a) and Wi-Fi receiver (10b). […] The channel state information is captured and the amplitude values for respective sub- carriers is computed. To reduce the dimension of the dataset some key frequency data is taken. The Model is trained using machine learning module such as but not limited to ML.Net Fast Forest Regression. Using the reduced dataset, the Model is trained for different glucose readings during the day and 3 subjects. Using the conventional glucose meter to compare the readings, the accuracy obtained is around 90%. Further, real patient data is to be collected and the machine learning approach is used to train the system based on the data values collected / experimented. […] Generally, in practice signals propagating between source and destination are affected due to physical properties of wireless medium. Further, resource allocation at physical layer is done based on RSSI as well as CSI values obtained from Wi-Fi modules. RSSI provide information on wireless channel properties and packet delivery status.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein the generating of the user-specific characteristic data includes reading multiple channel state information (CSI) values for each subcarrier from the extracted multiple Wi-Fi packets, and calculating multiple amplitude values for each subcarrier from the read multiple CSI values for each subcarrier, and the user-specific characteristic data is generated based on the multiple amplitude values calculated for each subcarrier in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Regarding claim 8, Zebian as modified by Sheth above discloses all of the limitations of claim 7 as discussed above.
Zebian does not clearly and explicitly disclose wherein the generating of the user-specific characteristic data further includes: determining packet frequencies of the extracted multiple Wi-Fi packets with reference to a timestamp value recorded in the extracted multiple Wi-Fi packets; generating an amplitude change pattern for each subcarrier by listing the multiple amplitude values calculated for each subcarrier; and interpolating the generated amplitude change pattern for each subcarrier based on the determined packet frequencies.
Sheth further discloses determining packet frequencies of extracted multiple Wi-Fi packets with reference to a timestamp value recorded in the extracted multiple Wi-Fi packets; generating an amplitude change pattern for each subcarrier by listing multiple amplitude values calculated for each subcarrier; and interpolating the generated amplitude change pattern for each subcarrier based on the determined packet frequencies (Sheth, Para 37-38; “According to one of the embodiment of the present invention the channel estimation signal response transmitted from Wi-Fi transmitter (10a) to Wi-Fi receiver (10b) while transferring a data packet are analyzed for the dielectric properties of the sample (hand) kept between Wi-Fi transmitter (10a) and Wi-Fi receiver (10b). […] The channel state information is captured and the amplitude values for respective sub- carriers is computed. To reduce the dimension of the dataset some key frequency data is taken. The Model is trained using machine learning module such as but not limited to ML.Net Fast Forest Regression. Using the reduced dataset, the Model is trained for different glucose readings during the day and 3 subjects. Using the conventional glucose meter to compare the readings, the accuracy obtained is around 90%. Further, real patient data is to be collected and the machine learning approach is used to train the system based on the data values collected / experimented. […] Generally, in practice signals propagating between source and destination are affected due to physical properties of wireless medium. Further, resource allocation at physical layer is done based on RSSI as well as CSI values obtained from Wi-Fi modules. RSSI provide information on wireless channel properties and packet delivery status.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein the generating of the user-specific characteristic data further includes: determining packet frequencies of the extracted multiple Wi-Fi packets with reference to a timestamp value recorded in the extracted multiple Wi-Fi packets; generating an amplitude change pattern for each subcarrier by listing the multiple amplitude values calculated for each subcarrier; and interpolating the generated amplitude change pattern for each subcarrier based on the determined packet frequencies in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Regarding claim 11, Zebian as modified by Sheth above discloses all of the limitations of claim 1 as discussed above.
Zebian further discloses generating participant-specific characteristic data indicating characteristics of change in wireless signal around a participant according to a physical change of a participant participating in learning the machine learning model from channel state information collected before the channel state information is collected; collecting a VO2 measurement value of the participant at a timepoint when the channel state information is collected before the channel state information is collected; and training the machine learning model by using the generated participant-specific characteristic data as input data of the machine learning model and using the collected VO2 measurement value of the participant as a label of the input data (Zebian, Para 24-26; “This system 100 is configured to gather real time data about the user in a first training or calibration phase and a second exercise or use phase. The first training phase includes the gas exchange analyzer 106 to set a baseline measurement of the user's amount of change in CO2 between exhaled and inhaled air, which can be calculated to provide an amount calorie expenditure […] J is the amount of oxygen consumption of the body measured by the gas exchange analyzer 106 during the period of time of the calibrating the system 100, i.e. in the first training phase.”) (Zebian, Para 45; “The monitoring data from the gas exchange analyzer 106 can be used for training a Machine Learning system or performing a curve fitting to retrieve boundary properties of lung 200.”) (Zebian, Para 69; “After the training process is done, the user can wear the sensors without the need for the gas exchange analyzer and be free to perform any activity while accurately calculating his energy expenditure based on the scientific model just created and the functions just calibrated”).
Regarding claim 12, Zebian as modified by Sheth above discloses all of the limitations of claim 1 as discussed above.
Zebian further discloses a computer-readable recording medium in which a program for causing a computer to perform the method (Zebian, Para 16; “To do so, this system and method are configured to implement a model with variables being body signatures that can be obtained from non-obstructive sensors, such as various wearable sensors”) (Zebian, Para 47; “The controller 104 may include a non-transitory readable memory”).
Regarding claim 13, Zebian discloses in Figure 1 a VO2 estimation device (Zebian, Para 42; “By calculating oxygen consumption of the body of the user, the system 100 is capable of accurately calculate metabolic rate of the user.”) (Zebian, Para 1; “The present disclosure is directed to a system and method for more accurate calculation of metabolic rate of a user based on quantifying the boundary properties of the user's lung”) comprising:
a transmitter and a receiver around a user configured to transmit a wireless signal between the transmitter and the receiver (Zebian, Para 49; “the plurality of sensors 102 may be integrated by the controller 104 inside a portable device, e.g., a smart watch. In this condition, the metabolic rate of the user is measured by the portable device and transmitted to the monitoring device 108 over the network 110. The monitoring device 108 may include a display to notify the user about the metabolic rate received from the portable device. In various embodiments, the metabolic rate may be displayed in energy burning unit such as calorie over time”) (Zebian, Para 19; “As the wearable sensors on the user collect and transmit data, either wirelessly or through a direct connection”);
a preprocessing unit configured to generate user-specific characteristic data indicating characteristics of change in the wireless signal around the user according to a change in a body of the user from the collected channel state information (Zebian, Para 34; “The lung oxygen concentration sensor 204 may measure the breathing volume of the user by indication of the rib cage deformation of the user's body”) (Zebian, Para 40-41; “he blood oxygen saturation sensor 202 operates simultaneously with the heartrate sensor 206, such as being housed in a same wearable device, like a smart watch. In this condition, the controller 104 receives indicative data from the oxygen saturation sensor 202 and the heartrate sensor 206 corresponding to a real time condition of the user. The controller 104 is configured to calculate an oxygen concentration of blood for the user, based on the data received simultaneously from the oxygen saturation sensor 202 and the heartrate sensor 206. A pulse oximeter may operate as a combination of the oxygen saturation sensor 202 and the heartrate sensor 206”); and
an estimation unit configured to estimate VO2 of the user from an output of a machine learning model by inputting the user-specific characteristic data to a pre-learned machine learning model (Zebian, Para 44; “the different recorded data may be used to train a Machine Learning system, where the Machin Learning system may accurately estimate the metabolic rate of the user based on the measurements of the term “O2 blood” and the term “O2 lung” of the equation in different conditions. The Machin Learning system may generate a first indicative data of the metabolic rate based on the measured “O2 blood” and “O2 lung” when the user is doing an exercise such as biking, while generating a second indicative data of the metabolic rate based on the same measured “O2 blood” and “O2 lung” when the user is doing another exercise such as hiking”) (Zebian, Para 43; “After calibrating the system 100, the gas exchange analyzer 106 can be decoupled from the controller 104 and the term “J” is calculated based on the real time measurement of the term “O2 blood” and the term “O2 lung” of the equation with the controller”) (Zebian, Para 30; “A simple mathematical equation can give us the result of oxygen consumed by the body: O2 consumed=volume flowrate of the blood x hemoglobin concentration per blood volume×(SpO2−SvO2)”) (Zebian, Para 42; “For calculating oxygen consumption of the body, breathing volume of the user can be measured that indicates oxygen concentration in lung of the user. The lung oxygen concentration sensor 204 may operate simultaneously with the oxygen saturation sensor 202 and the heartrate sensor 206. In this condition, the lung oxygen concentration sensor 204 measures and transmits indicative data of the oxygen concentration in lung of the user to the controller 104. The controller 104 may calculate the oxygen consumption of the body based on the received data simultaneously from the lung oxygen concentration sensor 204, the oxygen saturation sensor 202, and the heartrate sensor 206.”).
Zebian does not clearly and explicitly disclose a collection unit configured to collect channel state information indicating a wireless channel state between the transmitter and the receiver.
In an analogous wireless monitoring device field of endeavor Sheth discloses a collection unit configured to collect channel state information indicating a wireless channel state between a transmitter and a receiver (Sheth, Para 18; “The processing unit is configured to perform and use at least one dielectric characterization by initiating at least one Wi-Fi station (STA) corresponding to the at least one Wi-Fi transmitter to generate a request in a loop to Wi-Fi Access Point (AP) corresponding to at least one Wi-Fi receiver, initiating the Access Point (AP) to print the Channel State Information data (CSI Data), reading and storing the CSI Data for a plurality of requests made by STA to AP over a serial port, computing the amplitude for each sub-carrier from the CSI Data, obtain the amplitude timeseries data for the plurality of instances for the sub-carrier, applying decision filters to remove the outliers in the Amplitude timeseries, tagging the obtained amplitude timeseries by a reference blood glucose value, capturing and storing a plurality of timeseries as training data, performing training of a real-time sample value by the machine learning module, predicting and displaying a blood glucose level on the output display.”) (Sheth, Para 37-38; discussing this further).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian to include a collection unit configured to collect channel state information indicating a wireless channel state between the transmitter and the receiver in order to increase efficiency by filtering out data and in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Claims 4 and 9-10 rejected under 35 U.S.C. 103 as being unpatentable over Zebian and Sheth as applied to claims 3 and 8 above, and in further view of Harra et al. (US20100072386, hereafter Harra).
Regarding claim 4, Zebian as modified by Sheth above discloses all of the limitations of claim 3 as discussed above.
Zebian does not clearly and explicitly disclose wherein the user-specific characteristic data is an average amplitude value and a standard deviation of the multiple amplitude values of the wireless signal around the user.
In an analogous diagnostic signal analysis field of endeavor Harra discloses generating an average amplitude value (Harra, Para 147; “by accumulating and averaging the successive excellent scans over time, and comparing each new spectral data set to the accumulated average”) and a standard deviation of multiple amplitude values of a signal of a user (Harra, Para 167; “After enough scans have been collected, the data is inspected for gross errors, step 1610, by calculating the average, standard deviation, and sum of standard deviations of the spectral data scans. This procedure allows for the identification of erroneous or questionable spectra taken at the wrong moment, such as when the item was moving instead of being at rest. If the data is erroneous or questionable, step 1612, then the enrollment process is restarted, step 1614”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein the user-specific characteristic data is an average amplitude value and a standard deviation of the multiple amplitude values of the wireless signal around the user in order to improve accuracy as taught by Harra (Harra, Para 147) and in order to identify erroneous or questionable data as taught by Harra (Harra, Para 167).
Regarding claim 9, Zebian as modified by Sheth above discloses all of the limitations of claim 8 as discussed above.
Zebian does not clearly and explicitly disclose wherein the generating of the user-specific characteristic data further includes calculating an average amplitude value and a standard deviation of the amplitude change pattern for each subcarrier corresponding to the interpolated result, and the user-specific characteristic data is generated based on the average amplitude value and the standard deviation of the amplitude change pattern for each subcarrier.
In an analogous diagnostic signal analysis field of endeavor Harra discloses generating an average amplitude value (Harra, Para 147; “by accumulating and averaging the successive excellent scans over time, and comparing each new spectral data set to the accumulated average”) and a standard deviation of multiple amplitude values of a signal of a user (Harra, Para 167; “After enough scans have been collected, the data is inspected for gross errors, step 1610, by calculating the average, standard deviation, and sum of standard deviations of the spectral data scans. This procedure allows for the identification of erroneous or questionable spectra taken at the wrong moment, such as when the item was moving instead of being at rest. If the data is erroneous or questionable, step 1612, then the enrollment process is restarted, step 1614”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein the generating of the user-specific characteristic data further includes calculating an average amplitude value and a standard deviation of the amplitude change pattern for each subcarrier corresponding to the interpolated result, and the user-specific characteristic data is generated based on the average amplitude value and the standard deviation of the amplitude change pattern for each subcarrier in order to improve accuracy as taught by Harra (Harra, Para 147) and in order to identify erroneous or questionable data as taught by Harra (Harra, Para 167).
Regarding claim 10, Zebian as modified by Sheth and Harra above discloses all of the limitations of claim 9 as discussed above.
Zebian does not clearly and explicitly disclose wherein the generating of the user-specific characteristic data further includes selecting one subcarrier among multiple subcarriers of each of the extracted multiple WiFi packets based on the standard deviation of the amplitude change pattern for each subcarrier, and the user-specific characteristic data is an average amplitude value and a standard deviation of an amplitude change pattern of the selected one subcarrier.
Sheth further discloses selecting on one subcarrier among multiple subcarriers of each of the extracted multiple WiFi packets based on the standard deviation of the amplitude change pattern for each subcarrier (Sheth, Para 33; “The subcarriers 8,16,24,32,40,48,56 out of 64 at the storage medium communicatively coupled to the processor, embedding decision filters such as but not limited to Hample filter and removing the outliers in the Amplitude timeseries by using/executing the same”) (Sheth, Para 52; “The Hampel filter is applies to extract the time series data to identify outliers and replace them with more representative values. The filter is basically a configurable-width sliding window that is in this implementation is slided across the time series. For each window, the filter calculates the median and estimates the window’s standard deviation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zebian wherein the generating of the user-specific characteristic data further includes selecting one subcarrier among multiple subcarriers of each of the extracted multiple WiFi packets based on the standard deviation of the amplitude change pattern for each subcarrier, and the user-specific characteristic data is an average amplitude value and a standard deviation of an amplitude change pattern of the selected one subcarrier in order to increase efficiency by filtering out data and in order to increase accuracy as taught by Sheth (Sheth, Para 37).
Conclusion
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/JOHN D LI/Primary Examiner, Art Unit 3798
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